From 4896964ae882011ada74638d72d36129a8b173ab Mon Sep 17 00:00:00 2001 From: Robitzsch Date: Fri, 14 Feb 2020 17:30:39 +0100 Subject: [PATCH] 3.4-15 --- DESCRIPTION | 7 +- NAMESPACE | 1 + R/RcppExports.R | 80 +- R/plot.tam.R | 27 +- R/plot_tam_grouped_wle.R | 8 +- R/tam.mml.R | 2 +- R/tam_downcode.R | 21 + R/tam_mml_ic.R | 6 +- R/tam_mml_mstep_regression.R | 18 +- R/tamaan.R | 38 +- R/tamaanify.R | 33 +- R/tamaanify.create.A.R | 8 +- ...aanify.create.Q.R => tamaanify_create_Q.R} | 20 +- ...anmodel.R => tamaanify_proc_lavaanmodel.R} | 13 +- ...nce.fixed.R => tamaanify_variance_fixed.R} | 11 +- README.md | 6 +- docs/404.html | 135 ++ docs/authors.html | 38 +- docs/index.html | 31 +- docs/pkgdown.css | 46 +- docs/pkgdown.js | 14 +- docs/pkgdown.yml | 4 +- docs/reference/DescribeBy.html | 40 +- docs/reference/IRT.WrightMap.html | 177 +-- docs/reference/IRT.cv.html | 38 +- docs/reference/IRT.data.tam.html | 61 +- docs/reference/IRT.drawPV.html | 51 +- docs/reference/IRT.expectedCounts.tam.html | 69 +- docs/reference/IRT.factor.scores.tam.html | 67 +- docs/reference/IRT.frequencies.tam.html | 67 +- docs/reference/IRT.informationCurves.html | 131 +- docs/reference/IRT.irfprob.tam.html | 71 +- docs/reference/IRT.itemfit.html | 99 +- docs/reference/IRT.likelihood.tam.html | 111 +- docs/reference/IRT.linearCFA.html | 109 +- docs/reference/IRT.residuals.html | 65 +- docs/reference/IRT.simulate.html | 156 +-- docs/reference/IRT.threshold.html | 107 +- docs/reference/IRT.truescore.html | 86 +- docs/reference/IRTLikelihood.cfa.html | 73 +- docs/reference/IRTLikelihood.ctt.html | 74 +- docs/reference/Scale.html | 40 +- docs/reference/TAM-defunct.html | 38 +- docs/reference/TAM-package.html | 53 +- docs/reference/TAM-utilities.html | 46 +- docs/reference/WLErel.html | 55 +- docs/reference/anova.tam.html | 124 +- docs/reference/cfa.extract.itempars.html | 88 +- docs/reference/data.cqc.html | 197 +-- docs/reference/data.ctest.html | 43 +- docs/reference/data.examples.html | 57 +- docs/reference/data.fims.Aus.Jpn.html | 64 +- docs/reference/data.geiser.html | 104 +- docs/reference/data.gpcm.html | 48 +- docs/reference/data.janssen.html | 70 +- docs/reference/data.mc.html | 37 +- docs/reference/data.numeracy.html | 95 +- docs/reference/data.sim.mfr.html | 86 +- docs/reference/data.sim.rasch.html | 75 +- docs/reference/data.timssAusTwn.html | 107 +- docs/reference/designMatrices.html | 65 +- docs/reference/doparse.html | 51 +- docs/reference/index.html | 51 +- docs/reference/lavaanify.IRT.html | 103 +- docs/reference/msq.itemfit.html | 185 +-- docs/reference/plot.tam.html | 98 +- docs/reference/plotDevianceTAM.html | 43 +- docs/reference/predict.html | 59 +- docs/reference/tam.ctt.html | 75 +- docs/reference/tam.fa.html | 93 +- docs/reference/tam.fit.html | 160 +-- docs/reference/tam.jml.html | 158 ++- docs/reference/tam.latreg.html | 186 +-- docs/reference/tam.linking.html | 229 ++-- docs/reference/tam.mml.3pl.html | 830 +----------- docs/reference/tam.mml.html | 1168 ++++++++--------- docs/reference/tam.modelfit.html | 158 +-- docs/reference/tam.np.html | 147 ++- docs/reference/tam.personfit.html | 51 +- docs/reference/tam.pv.html | 344 ++--- docs/reference/tam.se.html | 72 +- docs/reference/tam.threshold.html | 94 +- docs/reference/tam.wle.html | 142 +- docs/reference/tam_NA_pattern.html | 51 +- docs/reference/tam_downcode.html | 189 +++ docs/reference/tam_irf_3pl.html | 51 +- docs/reference/tamaan.html | 166 +-- docs/reference/tamaanify.html | 80 +- docs/reference/tampv2datalist.html | 40 +- docs/reference/weighted_Stats.html | 157 +-- inst/NEWS | 13 +- man/TAM-package.Rd | 12 +- man/plot.tam.Rd | 23 +- man/tam.mml.3pl.Rd | 4 +- man/tam.mml.Rd | 5 +- man/tam_downcode.Rd | 78 ++ src/RcppExports.cpp | 6 +- src/init.c | 2 +- 98 files changed, 4373 insertions(+), 4712 deletions(-) create mode 100644 R/tam_downcode.R rename R/{tamaanify.create.Q.R => tamaanify_create_Q.R} (60%) rename R/{tamaanify.proc.lavaanmodel.R => tamaanify_proc_lavaanmodel.R} (58%) rename R/{tamaanify.variance.fixed.R => tamaanify_variance_fixed.R} (74%) create mode 100644 docs/404.html create mode 100644 docs/reference/tam_downcode.html create mode 100644 man/tam_downcode.Rd diff --git a/DESCRIPTION b/DESCRIPTION index cc605db..75e096e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,11 +1,11 @@ Package: TAM Type: Package Title: Test Analysis Modules -Version: 3.4-5 -Date: 2019-10-24 15:51:29 +Version: 3.4-15 +Date: 2020-02-14 17:10:51 Author: Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Margaret Wu [aut] -Maintainer: Alexander Robitzsch +Maintainer: Alexander Robitzsch Description: Includes marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional @@ -36,4 +36,3 @@ URL: http://www.edmeasurementsurveys.com/TAM/Tutorials/, https://github.com/alexanderrobitzsch/TAM, https://sites.google.com/site/alexanderrobitzsch2/software -RoxygenNote: 6.1.1 diff --git a/NAMESPACE b/NAMESPACE index 255f477..4ed45c5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -176,6 +176,7 @@ export(tam_cov_wt) export(tam_cor_wt) export(tam_csink) export(tam_difference_quotient) +export(tam_downcode) export(tam_dmvnorm) export(tam_ginv) export(tam_ginv_scaled) diff --git a/R/RcppExports.R b/R/RcppExports.R index b63b81e..53b40ae 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -1,159 +1,161 @@ +## File Name: RcppExports.R +## File Version: 3.004015 # Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 tam_rcpp_calc_exp <- function(NP, rprobs, A, INDEXIPNO, INDEXIPLIST2, ESTXSIINDEX, C, ITEMWT, NR, TP) { - .Call('_TAM_tam_rcpp_calc_exp', PACKAGE = 'TAM', NP, rprobs, A, INDEXIPNO, INDEXIPLIST2, ESTXSIINDEX, C, ITEMWT, NR, TP) + .Call('_TAM_tam_rcpp_calc_exp', PACKAGE='TAM', NP, rprobs, A, INDEXIPNO, INDEXIPLIST2, ESTXSIINDEX, C, ITEMWT, NR, TP) } tam_rcpp_calc_exp_redefine_vector_na <- function(A, val) { - .Call('_TAM_tam_rcpp_calc_exp_redefine_vector_na', PACKAGE = 'TAM', A, val) + .Call('_TAM_tam_rcpp_calc_exp_redefine_vector_na', PACKAGE='TAM', A, val) } tam_rcpp_calc_prob_subtract_max <- function(rr0M, NI, NK, TP) { - .Call('_TAM_tam_rcpp_calc_prob_subtract_max', PACKAGE = 'TAM', rr0M, NI, NK, TP) + .Call('_TAM_tam_rcpp_calc_prob_subtract_max', PACKAGE='TAM', rr0M, NI, NK, TP) } tam_rcpp_calc_prob_subtract_max_exp <- function(rr0, dim_rr) { - .Call('_TAM_tam_rcpp_calc_prob_subtract_max_exp', PACKAGE = 'TAM', rr0, dim_rr) + .Call('_TAM_tam_rcpp_calc_prob_subtract_max_exp', PACKAGE='TAM', rr0, dim_rr) } tam_rcpp_calc_prob <- function(A, dimA, xsi, maxcat, AXsi0, iIndex, theta, B) { - .Call('_TAM_tam_rcpp_calc_prob', PACKAGE = 'TAM', A, dimA, xsi, maxcat, AXsi0, iIndex, theta, B) + .Call('_TAM_tam_rcpp_calc_prob', PACKAGE='TAM', A, dimA, xsi, maxcat, AXsi0, iIndex, theta, B) } tam_rcpp_tam_mml_calc_prob_R_outer_Btheta <- function(Btheta, B_dd, theta_dd, dim_Btheta) { - .Call('_TAM_tam_rcpp_tam_mml_calc_prob_R_outer_Btheta', PACKAGE = 'TAM', Btheta, B_dd, theta_dd, dim_Btheta) + .Call('_TAM_tam_rcpp_tam_mml_calc_prob_R_outer_Btheta', PACKAGE='TAM', Btheta, B_dd, theta_dd, dim_Btheta) } tam_rcpp_tam_mml_calc_prob_R_normalize_rprobs <- function(rr, dim_rr) { - .Call('_TAM_tam_rcpp_tam_mml_calc_prob_R_normalize_rprobs', PACKAGE = 'TAM', rr, dim_rr) + .Call('_TAM_tam_rcpp_tam_mml_calc_prob_R_normalize_rprobs', PACKAGE='TAM', rr, dim_rr) } tam_rcpp_calc_suff_stat <- function(resp, resp_ind, maxK, nitems, pweights, cA) { - .Call('_TAM_tam_rcpp_calc_suff_stat', PACKAGE = 'TAM', resp, resp_ind, maxK, nitems, pweights, cA) + .Call('_TAM_tam_rcpp_calc_suff_stat', PACKAGE='TAM', resp, resp_ind, maxK, nitems, pweights, cA) } tam_rcpp_ctt2 <- function(TDAT, WLE, MAXK, EST_WLE, prg) { - .Call('_TAM_tam_rcpp_ctt2', PACKAGE = 'TAM', TDAT, WLE, MAXK, EST_WLE, prg) + .Call('_TAM_tam_rcpp_ctt2', PACKAGE='TAM', TDAT, WLE, MAXK, EST_WLE, prg) } tam_rcpp_ctt3 <- function(TDAT, WLE, MAXK, EST_WLE, prg) { - .Call('_TAM_tam_rcpp_ctt3', PACKAGE = 'TAM', TDAT, WLE, MAXK, EST_WLE, prg) + .Call('_TAM_tam_rcpp_ctt3', PACKAGE='TAM', TDAT, WLE, MAXK, EST_WLE, prg) } tam_rcpp_fit_simul <- function(rn1M, c_hwt, Ax, xbar, var1, Uz2, Vz2, nstud_ip, pweights) { - .Call('_TAM_tam_rcpp_fit_simul', PACKAGE = 'TAM', rn1M, c_hwt, Ax, xbar, var1, Uz2, Vz2, nstud_ip, pweights) + .Call('_TAM_tam_rcpp_fit_simul', PACKAGE='TAM', rn1M, c_hwt, Ax, xbar, var1, Uz2, Vz2, nstud_ip, pweights) } tam_rcpp_theta_sq <- function(theta) { - .Call('_TAM_tam_rcpp_theta_sq', PACKAGE = 'TAM', theta) + .Call('_TAM_tam_rcpp_theta_sq', PACKAGE='TAM', theta) } tam_rcpp_interval_index <- function(MATR, RN) { - .Call('_TAM_tam_rcpp_interval_index', PACKAGE = 'TAM', MATR, RN) + .Call('_TAM_tam_rcpp_interval_index', PACKAGE='TAM', MATR, RN) } tam_rcpp_irt_likelihood_cfa <- function(data, nu, psi, L, theta) { - .Call('_TAM_tam_rcpp_irt_likelihood_cfa', PACKAGE = 'TAM', data, nu, psi, L, theta) + .Call('_TAM_tam_rcpp_irt_likelihood_cfa', PACKAGE='TAM', data, nu, psi, L, theta) } tam_rcpp_mml_2pl_mstep_item_slopes_suffstat <- function(rprobs, items_temp, theta, dd, LIT, TP, nitems, maxcat, maxK, itemwt, xxf_, xbar_, xbar2_, irtmodel, xtemp_, items_conv) { - .Call('_TAM_tam_rcpp_mml_2pl_mstep_item_slopes_suffstat', PACKAGE = 'TAM', rprobs, items_temp, theta, dd, LIT, TP, nitems, maxcat, maxK, itemwt, xxf_, xbar_, xbar2_, irtmodel, xtemp_, items_conv) + .Call('_TAM_tam_rcpp_mml_2pl_mstep_item_slopes_suffstat', PACKAGE='TAM', rprobs, items_temp, theta, dd, LIT, TP, nitems, maxcat, maxK, itemwt, xxf_, xbar_, xbar2_, irtmodel, xtemp_, items_conv) } tam_rcpp_mml_3pl_calc_Fdes <- function(XDES, dimXdes) { - .Call('_TAM_tam_rcpp_mml_3pl_calc_Fdes', PACKAGE = 'TAM', XDES, dimXdes) + .Call('_TAM_tam_rcpp_mml_3pl_calc_Fdes', PACKAGE='TAM', XDES, dimXdes) } tam_rcpp_mml_3pl_slca_deriv <- function(XdesM, dimXdes, Xlambda, probs, nik, Nik, guess, probs0) { - .Call('_TAM_tam_rcpp_mml_3pl_slca_deriv', PACKAGE = 'TAM', XdesM, dimXdes, Xlambda, probs, nik, Nik, guess, probs0) + .Call('_TAM_tam_rcpp_mml_3pl_slca_deriv', PACKAGE='TAM', XdesM, dimXdes, Xlambda, probs, nik, Nik, guess, probs0) } tam_rcpp_mml_3pl_calcexp <- function(NP, rprobs, A, INDEXIPNO, INDEXIPLIST2, ESTXSIINDEX, C, ITEMWT, rprobs0, GUESS, nik, ni) { - .Call('_TAM_tam_rcpp_mml_3pl_calcexp', PACKAGE = 'TAM', NP, rprobs, A, INDEXIPNO, INDEXIPLIST2, ESTXSIINDEX, C, ITEMWT, rprobs0, GUESS, nik, ni) + .Call('_TAM_tam_rcpp_mml_3pl_calcexp', PACKAGE='TAM', NP, rprobs, A, INDEXIPNO, INDEXIPLIST2, ESTXSIINDEX, C, ITEMWT, rprobs0, GUESS, nik, ni) } tam_rcpp_mml_3pl_compute_B <- function(Edes, gammaslope, dimE) { - .Call('_TAM_tam_rcpp_mml_3pl_compute_B', PACKAGE = 'TAM', Edes, gammaslope, dimE) + .Call('_TAM_tam_rcpp_mml_3pl_compute_B', PACKAGE='TAM', Edes, gammaslope, dimE) } tam_rcpp_mml_3pl_nonzero_entries <- function(E, dimE) { - .Call('_TAM_tam_rcpp_mml_3pl_nonzero_entries', PACKAGE = 'TAM', E, dimE) + .Call('_TAM_tam_rcpp_mml_3pl_nonzero_entries', PACKAGE='TAM', E, dimE) } tam_rcpp_mml_mfr_gresp_extend <- function(gresp, xstep) { - .Call('_TAM_tam_rcpp_mml_mfr_gresp_extend', PACKAGE = 'TAM', gresp, xstep) + .Call('_TAM_tam_rcpp_mml_mfr_gresp_extend', PACKAGE='TAM', gresp, xstep) } tam_rcpp_mml_mfr_gresp_na_facets <- function(gresp, rnfacets, rnx) { - .Call('_TAM_tam_rcpp_mml_mfr_gresp_na_facets', PACKAGE = 'TAM', gresp, rnfacets, rnx) + .Call('_TAM_tam_rcpp_mml_mfr_gresp_na_facets', PACKAGE='TAM', gresp, rnfacets, rnx) } tam_rcpp_mml_mfr_a_matrix_cumsum <- function(index_matr, mm, SG) { - .Call('_TAM_tam_rcpp_mml_mfr_a_matrix_cumsum', PACKAGE = 'TAM', index_matr, mm, SG) + .Call('_TAM_tam_rcpp_mml_mfr_a_matrix_cumsum', PACKAGE='TAM', index_matr, mm, SG) } tam_rcpp_mml_mfr_colsums_gresp <- function(gresp) { - .Call('_TAM_tam_rcpp_mml_mfr_colsums_gresp', PACKAGE = 'TAM', gresp) + .Call('_TAM_tam_rcpp_mml_mfr_colsums_gresp', PACKAGE='TAM', gresp) } tam_rcpp_mml_maxcat <- function(A, dimA) { - .Call('_TAM_tam_rcpp_mml_maxcat', PACKAGE = 'TAM', A, dimA) + .Call('_TAM_tam_rcpp_mml_maxcat', PACKAGE='TAM', A, dimA) } tam_rcpp_modelfit_q3 <- function(residM, resp_bool) { - .Call('_TAM_tam_rcpp_modelfit_q3', PACKAGE = 'TAM', residM, resp_bool) + .Call('_TAM_tam_rcpp_modelfit_q3', PACKAGE='TAM', residM, resp_bool) } tam_rcpp_modelfit_counts <- function(resp0, resp_bool, rprobs, hwt, maxKi, maxK) { - .Call('_TAM_tam_rcpp_modelfit_counts', PACKAGE = 'TAM', resp0, resp_bool, rprobs, hwt, maxKi, maxK) + .Call('_TAM_tam_rcpp_modelfit_counts', PACKAGE='TAM', resp0, resp_bool, rprobs, hwt, maxKi, maxK) } tam_rcpp_modelfit_ccov <- function(counts, scorematrix, adjust) { - .Call('_TAM_tam_rcpp_modelfit_ccov', PACKAGE = 'TAM', counts, scorematrix, adjust) + .Call('_TAM_tam_rcpp_modelfit_ccov', PACKAGE='TAM', counts, scorematrix, adjust) } tam_rcpp_modelfit_residuals <- function(rprobs, resp, I, TP, maxK, maxKi, hwt, resp_bool) { - .Call('_TAM_tam_rcpp_modelfit_residuals', PACKAGE = 'TAM', rprobs, resp, I, TP, maxK, maxKi, hwt, resp_bool) + .Call('_TAM_tam_rcpp_modelfit_residuals', PACKAGE='TAM', rprobs, resp, I, TP, maxK, maxKi, hwt, resp_bool) } tam_rcpp_msq_itemfit <- function(resp, irf1, K, TP, post1, FIT, fitIndexM, resp_bool) { - .Call('_TAM_tam_rcpp_msq_itemfit', PACKAGE = 'TAM', resp, irf1, K, TP, post1, FIT, fitIndexM, resp_bool) + .Call('_TAM_tam_rcpp_msq_itemfit', PACKAGE='TAM', resp, irf1, K, TP, post1, FIT, fitIndexM, resp_bool) } tam_rcpp_prior_normal_density_unequal_means <- function(theta, mu, varInverse, COEFF) { - .Call('_TAM_tam_rcpp_prior_normal_density_unequal_means', PACKAGE = 'TAM', theta, mu, varInverse, COEFF) + .Call('_TAM_tam_rcpp_prior_normal_density_unequal_means', PACKAGE='TAM', theta, mu, varInverse, COEFF) } tam_rcpp_prior_normal_density_equal_means <- function(theta, mu, varInverse, COEFF) { - .Call('_TAM_tam_rcpp_prior_normal_density_equal_means', PACKAGE = 'TAM', theta, mu, varInverse, COEFF) + .Call('_TAM_tam_rcpp_prior_normal_density_equal_means', PACKAGE='TAM', theta, mu, varInverse, COEFF) } tam_rcpp_pv_mcmc_likelihood <- function(probs, resp, resp_ind_bool, maxK, nstud, nitems) { - .Call('_TAM_tam_rcpp_pv_mcmc_likelihood', PACKAGE = 'TAM', probs, resp, resp_ind_bool, maxK, nstud, nitems) + .Call('_TAM_tam_rcpp_pv_mcmc_likelihood', PACKAGE='TAM', probs, resp, resp_ind_bool, maxK, nstud, nitems) } tam_rcpp_pv_mcmc_calc_probs_irf_3pl <- function(theta, B, I, maxK, resp_ind, AXsi) { - .Call('_TAM_tam_rcpp_pv_mcmc_calc_probs_irf_3pl', PACKAGE = 'TAM', theta, B, I, maxK, resp_ind, AXsi) + .Call('_TAM_tam_rcpp_pv_mcmc_calc_probs_irf_3pl', PACKAGE='TAM', theta, B, I, maxK, resp_ind, AXsi) } tam_rcpp_pv_sample_theta_multidim <- function(post, theta) { - .Call('_TAM_tam_rcpp_pv_sample_theta_multidim', PACKAGE = 'TAM', post, theta) + .Call('_TAM_tam_rcpp_pv_sample_theta_multidim', PACKAGE='TAM', post, theta) } tam_rcpp_rowCumsums <- function(input) { - .Call('_TAM_tam_rcpp_rowCumsums', PACKAGE = 'TAM', input) + .Call('_TAM_tam_rcpp_rowCumsums', PACKAGE='TAM', input) } tam_rcpp_tam_np_posterior <- function(dat2, dat_resp, probs0, pi_k, pweights, K1) { - .Call('_TAM_tam_rcpp_tam_np_posterior', PACKAGE = 'TAM', dat2, dat_resp, probs0, pi_k, pweights, K1) + .Call('_TAM_tam_rcpp_tam_np_posterior', PACKAGE='TAM', dat2, dat_resp, probs0, pi_k, pweights, K1) } tam_rcpp_wle_suffstat <- function(RPROBS, CBL, CBB, CBBB, cndim, cnitems, cmaxK, cnstud, resp_ind) { - .Call('_TAM_tam_rcpp_wle_suffstat', PACKAGE = 'TAM', RPROBS, CBL, CBB, CBBB, cndim, cnitems, cmaxK, cnstud, resp_ind) + .Call('_TAM_tam_rcpp_wle_suffstat', PACKAGE='TAM', RPROBS, CBL, CBB, CBBB, cndim, cnitems, cmaxK, cnstud, resp_ind) } tam_rcpp_wle_errinv <- function(myERR, cndim, cnstud) { - .Call('_TAM_tam_rcpp_wle_errinv', PACKAGE = 'TAM', myERR, cndim, cnstud) + .Call('_TAM_tam_rcpp_wle_errinv', PACKAGE='TAM', myERR, cndim, cnstud) } diff --git a/R/plot.tam.R b/R/plot.tam.R index 7fc9da8..6875fed 100644 --- a/R/plot.tam.R +++ b/R/plot.tam.R @@ -1,5 +1,5 @@ ## File Name: plot.tam.R -## File Version: 9.283 +## File Version: 9.2873 #--- plotting tam expected scores curves plot.tam <- function(x, items=1:x$nitems, type="expected", @@ -7,7 +7,7 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", wle=NULL, export=TRUE, export.type="png", export.args=list(), observed=TRUE, overlay=FALSE, ask=FALSE, package="lattice", - fix.devices=TRUE, ...) + fix.devices=TRUE, nnodes=100, ...) { require_namespace_msg("grDevices") if ( package=="lattice"){ @@ -46,13 +46,10 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", } nitems <- tamobj$nitems - nnodes <- 100 if (ndim==1 ){ theta <- matrix(seq(low, high, length=nnodes), nrow=nnodes, ncol=ndim) } else { - # theta <- tamobj$theta - nnodes <- 40 nodes <- seq(low, high, length=nnodes) theta <- as.matrix( expand.grid( as.data.frame( matrix( rep(nodes, ndim), ncol=ndim ) ) ) ) nnodes <- nrow(theta) @@ -78,6 +75,10 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", AXsi <- res[["AXsi"]] cat <- 1:maxK - 1 + #@@@ define initial empty objects + expScore <- obScore <- wle_intervals <- NULL + theta2 <- NULL + #**** type='expected' if ( type=="expected" ){ expScore <- sapply(1:nitems, function(i) colSums(cat*rprobs[i,,], na.rm=TRUE)) @@ -91,6 +92,7 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", d2 <- res$d2 groupnumber <- res$groupnumber ngroups <- res$ngroups + wle_intervals <- res$wle_intervals #-- compute observed scores obScore <- apply(d2,2, function(x){ stats::aggregate(x, list(groupnumber), mean, na.rm=TRUE) @@ -130,6 +132,8 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", #************************************************* # begin plot function + probs_plot <- as.list(1:nitems) + names(probs_plot) <- items for (i in (1:nitems)[items]) { #*********************************************************** @@ -161,8 +165,9 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", } } #*********************************************************** - if ( type=="items"){ + + if ( type=="items"){ rprobs.ii <- rprobs[i,,] rprobs.ii <- rprobs.ii[ rowMeans( is.na(rprobs.ii) ) < 1, ] K <- nrow(rprobs.ii) @@ -177,6 +182,7 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", theta0 <- rprobs0.ii[,1,drop=FALSE] rprobs.ii <- t( rprobs0.ii[,-1] ) } + probs_plot[[i]] <- rprobs.ii #************** for (kk in 1:K){ dat2a <- data.frame( "Theta"=theta0[,1], "cat"=kk, "P"=rprobs.ii[kk,] ) @@ -410,7 +416,14 @@ plot.tam <- function(x, items=1:x$nitems, type="expected", file.path(getwd(), "Plots")) ; utils::flush.console() } } - + # option output + res_exp <- list(obScore=obScore, expScore=expScore, ngroups=ngroups, + wle_intervals=wle_intervals, theta=theta, + wle_obs_plotted=theta2) + res_items <- list(items=items, nitems=nitems, obScore=obScore, theta=theta0, + probs_plot=probs_plot) + res <- list(type_expected=res_exp, res_items=res_items) + invisible(res) } plot.tam.mml <- plot.tam diff --git a/R/plot_tam_grouped_wle.R b/R/plot_tam_grouped_wle.R index e1a248f..df8eaf7 100644 --- a/R/plot_tam_grouped_wle.R +++ b/R/plot_tam_grouped_wle.R @@ -1,5 +1,5 @@ ## File Name: plot_tam_grouped_wle.R -## File Version: 0.11 +## File Version: 0.123 plot_tam_grouped_wle <- function( tamobj, tammodel, wle, ngroups, resp) @@ -14,7 +14,8 @@ plot_tam_grouped_wle <- function( tamobj, tammodel, wle, ngroups, resp) } q1 <- 1 / ngroups quant <- unique( stats::quantile(wle, probs=seq(q1, 1 - q1, by=q1), na.rm=TRUE ) ) - groupnumber <- as.numeric( cut( wle, breaks=c( -Inf, quant, Inf) ) ) + wle_intervals <- c( -Inf, quant, Inf) + groupnumber <- as.numeric( cut( wle, breaks=wle_intervals ) ) aggr <- stats::aggregate(wle, list(groupnumber), mean, na.rm=TRUE) theta2 <- aggr$x ngroups <- length(theta2) @@ -23,6 +24,7 @@ plot_tam_grouped_wle <- function( tamobj, tammodel, wle, ngroups, resp) d2 <- d1[-1] #--- output res <- list(wle=wle, theta2=theta2, d=d, d1=d1, d2=d2, - groupnumber=groupnumber, ngroups=ngroups) + groupnumber=groupnumber, ngroups=ngroups, + wle_intervals=wle_intervals) return(res) } diff --git a/R/tam.mml.R b/R/tam.mml.R index 9269fbf..3d388e9 100644 --- a/R/tam.mml.R +++ b/R/tam.mml.R @@ -1,5 +1,5 @@ ## File Name: tam.mml.R -## File Version: 9.795 +## File Version: 9.797 tam.mml <- function( resp, Y=NULL, group=NULL, irtmodel="1PL", formulaY=NULL, dataY=NULL, diff --git a/R/tam_downcode.R b/R/tam_downcode.R new file mode 100644 index 0000000..c5171ef --- /dev/null +++ b/R/tam_downcode.R @@ -0,0 +1,21 @@ +## File Name: tam_downcode.R +## File Version: 0.03 + +tam_downcode <- function(dat) +{ + res <- NULL + dat1 <- NA*dat + I <- ncol(dat) + + for (ii in 1L:I){ + v1 <- dat[,ii] + vals_ii <- sort(unique(v1)) + n_ii <- length(vals_ii) + ind_ii <- match(v1, vals_ii) + dat1[,ii] <- ind_ii-1 + rec_ii <- data.frame(item=colnames(dat)[ii], orig=vals_ii, rec=0:(n_ii-1) ) + rec <- rbind(rec, rec_ii) + } + res <- list(dat=dat1, rec=rec) + return(res) +} diff --git a/R/tam_mml_ic.R b/R/tam_mml_ic.R index 5d10e30..2e4ed6f 100644 --- a/R/tam_mml_ic.R +++ b/R/tam_mml_ic.R @@ -1,5 +1,5 @@ ## File Name: tam_mml_ic.R -## File Version: 9.194 +## File Version: 9.196 #--- information criteria @@ -57,7 +57,7 @@ tam_mml_ic <- function( nstud, deviance, xsi, xsi.fixed, if ( irtmodel=="GPCM" ){ nB <- length(B.fixed[ B.fixed[,2]==2,1]) } - ic$NparsB <- ic$NparsB - nB + ic$NparsB <- max(ic$NparsB - nB, 0) } # beta regression parameters @@ -70,7 +70,7 @@ tam_mml_ic <- function( nstud, deviance, xsi, xsi.fixed, if ( ! est.variance ){ ic$Nparscov <- ic$Nparscov - ndim } - if ( ! is.null( variance.fixed) ){ + if ( ! is.null(variance.fixed) ){ ic$Nparscov <- max(0, ic$Nparscov - nrow(variance.fixed ) ) } diff --git a/R/tam_mml_mstep_regression.R b/R/tam_mml_mstep_regression.R index 37e1b3b..a50c3c2 100644 --- a/R/tam_mml_mstep_regression.R +++ b/R/tam_mml_mstep_regression.R @@ -1,5 +1,5 @@ ## File Name: tam_mml_mstep_regression.R -## File Version: 9.48 +## File Version: 9.497 # mstep.regression @@ -50,28 +50,26 @@ tam_mml_mstep_regression <- function( resp, hwt, resp.ind, sumbeta <- crossprod( Y, thetabar*pweights ) sumsig2 <- as.vector( crossprod( colSums( pweights * hwt ), theta2 ) ) } - #---------------------------------------- - # calculation of variance and regression coefficients + + #--- calculation of variance and regression coefficients beta <- YYinv %*% sumbeta # updated beta sumsig2 <- matrix(sumsig2, nrow=ndim, ncol=ndim) if (G==1){ variance <- (sumsig2- crossprod( sumbeta, beta ) )/nstud #new variance } - #---------------------------------------- - # fixed beta coefficients + + #--- fixed beta coefficients if ( ! is.null( beta.fixed )){ beta[ beta.fixed[,1:2,drop=FALSE] ] <- beta.fixed[,3] beta <- as.matrix( beta, ncol=ndim ) } - #---------------------------------------- - # fixed covariance matrix entries + + #--- fixed covariance matrix entries if ( ! is.null(variance.fixed) ){ variance[ variance.fixed[,1:2,drop=FALSE] ] <- variance.fixed[,3] variance[ variance.fixed[,c(2,1),drop=FALSE] ] <- variance.fixed[,3] } - #---------------------------------------- - #**************************************** ###### Multiple Groups ############## if ( G > 1){ # begin multiple groups if ( snodes > 0 ){ @@ -143,5 +141,3 @@ tam_mml_mstep_regression <- function( resp, hwt, resp.ind, mstep.regression <- tam_mml_mstep_regression - - diff --git a/R/tamaan.R b/R/tamaan.R index fc64626..64663aa 100644 --- a/R/tamaan.R +++ b/R/tamaan.R @@ -1,8 +1,8 @@ ## File Name: tamaan.R -## File Version: 9.19 +## File Version: 9.214 -########################################################## -# tamaan function + +#--- tamaan function tamaan <- function( tammodel, resp, tam.method=NULL, control=list(), doparse=TRUE, ... ) { @@ -46,14 +46,13 @@ tamaan <- function( tammodel, resp, tam.method=NULL, con[ names(control) ] <- control Lcon <- length(con) - con1a <- con1 <- con ; + con1a <- con1 <- con names(con1) <- NULL for (cc in 1:Lcon ){ assign( names(con)[cc], con1[[cc]], envir=e1 ) } - #****************************** - # tam.mml + #--- tam.mml if ( res0$method=="tam.mml" ){ res <- tam.mml( resp=res0$resp, A=res0$A, xsi.fixed=res0$xsi.fixed, Q=res0$Q, variance.fixed=res0$variance.fixed, @@ -61,8 +60,7 @@ tamaan <- function( tammodel, resp, tam.method=NULL, res$tamaan.method <- "tam.mml" } - #****************************** - # tam.mml.2pl + #--- tam.mml.2pl if ( res0$method=="tam.mml.2pl" ){ res <- tam.mml.2pl( resp=res0$resp, A=res0$A, xsi.fixed=res0$xsi.fixed, Q=res0$Q, variance.fixed=res0$variance.fixed, @@ -70,40 +68,31 @@ tamaan <- function( tammodel, resp, tam.method=NULL, control=con, ... ) res$tamaan.method <- "tam.mml.2pl" } - #****************************** - # 3PL: latent class analysis + + #--- 3PL: latent class analysis if ( ( res0$method=="tam.mml.3pl" ) & ( anal.list$type=="LCA" ) ){ res <- tamaan.3pl.lca( res0=res0, anal.list=anal.list, con=con, ... ) } - #*********************************** - #****************************** - # 3PL: ordered latent class analysis + #--- 3PL: ordered latent class analysis if ( ( res0$method=="tam.mml.3pl" ) & ( anal.list$type=="OLCA" ) ){ res <- tamaan.3pl.olca( res0=res0, anal.list=anal.list, con=con, ... ) } - #*********************************** - #****************************** - # 3PL: trait model + #--- 3PL: trait model if ( ( res0$method=="tam.mml.3pl" ) & ( anal.list$type=="TRAIT" ) ){ res <- tamaan.3pl.trait( res0=res0, anal.list=anal.list, con=con, ... ) } - #*********************************** - #****************************** - # 3PL: located latent class analysis + #--- 3PL: located latent class analysis if ( ( res0$method=="tam.mml.3pl" ) & ( anal.list$type=="LOCLCA" ) ){ res <- tamaan.3pl.loclca( res0=res0, anal.list=anal.list, con=con, ... ) } - #*********************************** - #****************************** - # 3PL: mixture distribution models + #--- 3PL: mixture distribution models if ( ( res0$method=="tam.mml.3pl" ) & ( anal.list$type=="MIXTURE" ) ){ res <- tamaan.3pl.mixture( res0=res0, anal.list=anal.list, con=con, ... ) } - #*********************************** s1 <- Sys.time() time1 <- c( s0, s1, s1-s0) @@ -116,8 +105,5 @@ tamaan <- function( tammodel, resp, tam.method=NULL, res$anal.list <- anal.list class(res) <- "tamaan" res$CALL <- cl - return(res) } -########################################################### - diff --git a/R/tamaanify.R b/R/tamaanify.R index 18e8266..8507ccf 100644 --- a/R/tamaanify.R +++ b/R/tamaanify.R @@ -1,8 +1,8 @@ ## File Name: tamaanify.R -## File Version: 9.19 +## File Version: 9.216 -###################################################### -# tamaanify + +#--- tamaanify function tamaanify <- function( tammodel, resp, tam.method=NULL, doparse=TRUE ) { dat <- resp @@ -43,7 +43,7 @@ tamaanify <- function( tammodel, resp, tam.method=NULL, doparse=TRUE ) res <- tamaanify.proc.analysis( res ) #*** extract lavaan model - res <- tamaanify.proc.lavaanmodel(res, resp ) + res <- tamaanify_proc_lavaanmodel(res=res, resp=resp) #*** item characteristics res <- tamaanify.proc.items( res, resp) @@ -57,7 +57,7 @@ tamaanify <- function( tammodel, resp, tam.method=NULL, doparse=TRUE ) #****** # add response dataset cols <- paste(res$items$item) - resp <- resp[, cols ] + resp <- resp[, cols] res$resp <- resp #********** @@ -68,34 +68,33 @@ tamaanify <- function( tammodel, resp, tam.method=NULL, doparse=TRUE ) } #*** A matrix - res <- tamaanify.create.A( res ) + res <- tamaanify.create.A(res=res) #*** Q matrix - res <- tamaanify.create.Q( res ) + res <- tamaanify_create_Q(res=res) #*** fixed loadings in tam.mml.2pl (B.fixed) - res <- tamaanify.proc.loadings.B.fixed(res) + res <- tamaanify.proc.loadings.B.fixed(res=res) #*** model constraints loadings - res <- tamaanify.modelconstraints.loadings(res) + res <- tamaanify.modelconstraints.loadings(res=res) #*** variance fixings - res <- tamaanify.variance.fixed( res) + res <- tamaanify_variance_fixed(res=res) #*** define design matrices for tam.mml.3pl method - res <- tamaanify.tam.mml.3pl.designMatrices(res) + res <- tamaanify.tam.mml.3pl.designMatrices(res=res) #*** delta design matrix - res <- tamaanify.tam.mml.3pl.deltadesign(res) + res <- tamaanify.tam.mml.3pl.deltadesign(res=res) #**** model prior - res <- tamaanify.modelprior( res ) + res <- tamaanify.modelprior(res=res) #*** define method - res <- tamaanify.define.method(res, tam.method ) + res <- tamaanify.define.method(res=res, tam.method=tam.method) - #************************************************+ - # OUTPUT + #--- OUTPUT return(res) } -############################################################### + diff --git a/R/tamaanify.create.A.R b/R/tamaanify.create.A.R index 88a8282..a207ec0 100644 --- a/R/tamaanify.create.A.R +++ b/R/tamaanify.create.A.R @@ -1,11 +1,9 @@ ## File Name: tamaanify.create.A.R -## File Version: 9.09 +## File Version: 9.11 - -######################################################### -# create A matrix -tamaanify.create.A <- function( res ) +#-- create A matrix +tamaanify.create.A <- function(res) { resp <- res$resp lavpartable <- res$lavpartable diff --git a/R/tamaanify.create.Q.R b/R/tamaanify_create_Q.R similarity index 60% rename from R/tamaanify.create.Q.R rename to R/tamaanify_create_Q.R index 3342014..b6ee65a 100644 --- a/R/tamaanify.create.Q.R +++ b/R/tamaanify_create_Q.R @@ -1,11 +1,11 @@ -## File Name: tamaanify.create.Q.R -## File Version: 9.06 +## File Name: tamaanify_create_Q.R +## File Version: 9.12 -############################################################# -# create Q matrix -tamaanify.create.Q <- function( res ){ +#--- create Q matrix +tamaanify_create_Q <- function( res ) +{ lavpartable <- res$lavpartable resp <- res$resp ind <- which( lavpartable$op=="=~" ) @@ -22,14 +22,12 @@ tamaanify.create.Q <- function( res ){ rownames(Q) <- items colnames(Q) <- facs for (ff in 1:NF){ - # ff <- 1 - ind.ff <- intersect( which( paste(lavpartable$lhs)==facs[ff] ), ind ) + ind.ff <- intersect( which( paste(lavpartable$lhs)==facs[ff] ), ind ) lff <- lavpartable[ ind.ff, ] Q[ rownames(Q) %in% paste(lff$rhs), ff ] <- lff$ustart - } + } res$Q <- Q res$lavpartable <- lavpartable - # res <- list( "Q"=Q, "lavpartable"=lavpartable ) return(res) - } -############################################################# +} + diff --git a/R/tamaanify.proc.lavaanmodel.R b/R/tamaanify_proc_lavaanmodel.R similarity index 58% rename from R/tamaanify.proc.lavaanmodel.R rename to R/tamaanify_proc_lavaanmodel.R index 2596e8f..e609c33 100644 --- a/R/tamaanify.proc.lavaanmodel.R +++ b/R/tamaanify_proc_lavaanmodel.R @@ -1,9 +1,9 @@ -## File Name: tamaanify.proc.lavaanmodel.R -## File Version: 9.08 +## File Name: tamaanify_proc_lavaanmodel.R +## File Version: 9.14 -############################################################ -# process lavaan model -tamaanify.proc.lavaanmodel <- function(res, resp ) + +#--- process lavaan model +tamaanify_proc_lavaanmodel <- function(res, resp ) { tam1 <- res$tammodel.dfr ind1 <- which( paste(tam1$syn)=="LAVAANMODEL:" ) @@ -11,9 +11,8 @@ tamaanify.proc.lavaanmodel <- function(res, resp ) lavmodel <- paste( tam1[ which( tam1$part_begin==index1 )[-1], "syn" ] ) lavmodel <- paste0( lavmodel, collapse="\n") res$LAVAANMODEL <- lavmodel - # process lavaan model + #** process lavaan model lavres <- lavaanify.IRT( lavmodel=res$LAVAANMODEL, data=resp ) res$lavpartable <- lavres$lavpartable return(res) } -########################################################## diff --git a/R/tamaanify.variance.fixed.R b/R/tamaanify_variance_fixed.R similarity index 74% rename from R/tamaanify.variance.fixed.R rename to R/tamaanify_variance_fixed.R index 35a04af..76550b2 100644 --- a/R/tamaanify.variance.fixed.R +++ b/R/tamaanify_variance_fixed.R @@ -1,12 +1,10 @@ -## File Name: tamaanify.variance.fixed.R -## File Version: 9.07 +## File Name: tamaanify_variance_fixed.R +## File Version: 9.202 - -########################################################## -# variance constraints -tamaanify.variance.fixed <- function( res ) +#--- variance constraints +tamaanify_variance_fixed <- function( res ) { lavpartable <- res$lavpartable Q <- res$Q @@ -29,4 +27,3 @@ tamaanify.variance.fixed <- function( res ) res$variance.fixed <- variance.fixed return(res) } -########################################################## diff --git a/README.md b/README.md index 2b91982..12011de 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ #### Test Analysis Modules -If you use `TAM` and have suggestions for improvement or have found bugs, please email me at robitzsch@leibniz-ipn.de. +If you use `TAM` and have suggestions for improvement or have found bugs, please email me at robitzsch@ipn.uni-kiel.de. #### Manual @@ -22,9 +22,9 @@ The CRAN version can be installed from within R using: utils::install.packages("TAM") ``` -#### GitHub version `TAM` 3.4-5 (2019-10-24) +#### GitHub version `TAM` 3.4-15 (2020-02-14) -[![](https://img.shields.io/badge/github%20version-3.4--5-orange.svg)](https://github.com/alexanderrobitzsch/TAM)   +[![](https://img.shields.io/badge/github%20version-3.4--15-orange.svg)](https://github.com/alexanderrobitzsch/TAM)   The version hosted [here](https://github.com/alexanderrobitzsch/TAM) is the development version of `TAM`. The GitHub version can be installed using `devtools` as: diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..fd313f8 --- /dev/null +++ b/docs/404.html @@ -0,0 +1,135 @@ + + + + + + + + +Page not found (404) • TAM + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + +
+ +
+
+
+

Page not found (404)

+
+ +Content not found. Please use links in the navbar. + +
+ +
+ + + +
+
+

Developed by Alexander Robitzsch.

+
+ +
+

Site built with pkgdown 1.4.1.

+
+ +
+
+ + + + + + + + diff --git a/docs/authors.html b/docs/authors.html index 37f028e..cdf4325 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -8,21 +8,25 @@ Citation and Authors • TAM + + - + + - - + + + @@ -30,10 +34,12 @@ + + @@ -44,6 +50,7 @@ + @@ -60,7 +67,7 @@ TAM - 3.4.5 + 3.4.15 @@ -68,7 +75,7 @@ -
  • - +
    • @@ -91,6 +97,7 @@ +
    @@ -100,17 +107,18 @@

    Citation

    Source: inst/CITATION
    -

    Robitzsch A, Kiefer T, Wu M (2019). +

    Robitzsch A, Kiefer T, Wu M (2020). TAM: Test Analysis Modules. -R package version 3.4-5, https://CRAN.R-project.org/package=TAM. +R package version 3.4-15, https://CRAN.R-project.org/package=TAM.

    - 
    @Manual{TAM_3.4-5,
+    
    @Manual{TAM_3.4-15,
   title = {TAM: Test Analysis Modules},
   author = {Alexander Robitzsch and Thomas Kiefer and Margaret Wu},
-  year = {2019},
-  note = {R package version 3.4-5},
+  year = {2020},
+  note = {R package version 3.4-15},
   url = {https://CRAN.R-project.org/package=TAM},
 }
    
+
     
    
       
    Authors
    
     
    
@@ -127,19 +135,23 @@ 
    Authors
    
 
 
 
+
       
    
       
    
   
    Developed by Alexander Robitzsch.
    
 
    
 
 
    
-  
    Site built with pkgdown 1.3.0.
    
+  
    Site built with pkgdown 1.4.1.
    
 
    
+
       
    
    
 
   
 
+
   
 
 
+
diff --git a/docs/index.html b/docs/index.html
index 12b86f9..b80eee7 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -7,8 +7,9 @@
 
 Test Analysis Modules • TAM
 
-
-
+
+
+
 
 
         TAM
-        3.4.5
+        3.4.15
       
     
 
@@ -50,7 +51,7 @@
       
      
 
    • 
   
-    
+    
      
   
 
      • 
@@ -61,7 +62,7 @@
 
        
 
      • 
   
-    
+    
      
   
 
        • 
@@ -74,6 +75,7 @@
 
 
       
+
       
        
   
        
 
        
@@ -82,12 +84,18 @@
 
        
 
        
 Test Analysis Modules
        
-
        If you use TAM and have suggestions for improvement or have found bugs, please email me at .
        
+
        If you use TAM and have suggestions for improvement or have found bugs, please email me at 
        
+.
 
        
 
        
 
        
 Manual
        
-
        The manual may be found here https://alexanderrobitzsch.github.io/TAM/
        
+
        The manual may be found here https://alexanderrobitzsch.github.io/TAM/
        
 
        
 
        
 
        
@@ -97,13 +105,13 @@ 
        
   
 -->
 
        The official version of TAM is hosted on CRAN and may be found here. The CRAN version can be installed from within R using:
        
-
+
        utils::install.packages("TAM")
        
 
        
 
        
 
        
 GitHub version
        
 
        The version hosted here is the development version of TAM. The GitHub version can be installed using devtools as:
        
-
        devtools::install_github("alexanderrobitzsch/TAM")
        
+
        devtools::install_github("alexanderrobitzsch/TAM")
        
 
        
 
        
   
        
@@ -140,17 +148,20 @@ 
        Developers
        
   
        
 
 
+
       
        
   
        Developed by Alexander Robitzsch.
        
 
        
 
 
        
-  
        Site built with pkgdown 1.3.0.
        
+  
        Site built with pkgdown 1.4.1.
        
 
        
+
       
        
 
 
   
 
+
   
 
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
index c03fb08..9145958 100644
--- a/docs/pkgdown.css
+++ b/docs/pkgdown.css
@@ -21,8 +21,6 @@ body > .container {
   display: flex;
   height: 100%;
   flex-direction: column;
-
-  padding-top: 60px;
 }
 
 body > .container .row {
@@ -102,21 +100,13 @@ a.anchor {
   margin-top: -40px;
 }
 
-/* Static header placement on mobile devices */
-@media (max-width: 767px) {
-  .navbar-fixed-top {
-    position: absolute;
-  }
-  .navbar {
-    padding: 0;
-  }
-}
-
-
 /* Sidebar --------------------------*/
 
 #sidebar {
   margin-top: 30px;
+  position: -webkit-sticky;
+  position: sticky;
+  top: 70px;
 }
 #sidebar h2 {
   font-size: 1.5em;
@@ -133,6 +123,9 @@ a.anchor {
 
 .orcid {
   height: 16px;
+  /* margins are required by official ORCID trademark and display guidelines */
+  margin-left:4px;
+  margin-right:4px;
   vertical-align: middle;
 }
 
@@ -222,6 +215,19 @@ a.sourceLine:hover {
   visibility: visible;
 }
 
+/* headroom.js ------------------------ */
+
+.headroom {
+  will-change: transform;
+  transition: transform 200ms linear;
+}
+.headroom--pinned {
+  transform: translateY(0%);
+}
+.headroom--unpinned {
+  transform: translateY(-100%);
+}
+
 /* mark.js ----------------------------*/
 
 mark {
@@ -234,3 +240,17 @@ mark {
 .html-widget {
   margin-bottom: 10px;
 }
+
+/* fontawesome ------------------------ */
+
+.fab {
+    font-family: "Font Awesome 5 Brands" !important;
+}
+
+/* don't display links in code chunks when printing */
+/* source: https://stackoverflow.com/a/10781533 */
+@media print {
+  code a:link:after, code a:visited:after {
+    content: "";
+  }
+}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index eb7e83d..087a762 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -2,14 +2,12 @@
 (function($) {
   $(function() {
 
-    $("#sidebar")
-      .stick_in_parent({offset_top: 40})
-      .on('sticky_kit:bottom', function(e) {
-        $(this).parent().css('position', 'static');
-      })
-      .on('sticky_kit:unbottom', function(e) {
-        $(this).parent().css('position', 'relative');
-      });
+    $('.navbar-fixed-top').headroom();
+
+    $('body').css('padding-top', $('.navbar').height() + 10);
+    $(window).resize(function(){
+      $('body').css('padding-top', $('.navbar').height() + 10);
+    });
 
     $('body').scrollspy({
       target: '#sidebar',
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 06304c0..27c8cbb 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,5 +1,5 @@
-pandoc: '2.6'
-pkgdown: 1.3.0
+pandoc: 1.13.1
+pkgdown: 1.4.1
 pkgdown_sha: ~
 articles: []
 
diff --git a/docs/reference/DescribeBy.html b/docs/reference/DescribeBy.html
index 4195808..3a09b83 100644
--- a/docs/reference/DescribeBy.html
+++ b/docs/reference/DescribeBy.html
@@ -8,21 +8,25 @@
 
 S3 Method for Descriptive Statistics of Objects — DescribeBy • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
          
         
        • 
   
-    
+    
      
   
 
          • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
          
@@ -105,13 +111,11 @@ 
          S3 Method for Descriptive Statistics of Objects
          
     
          
 
     
          
-    
     
          S3 method for descriptive statistics of objects
          
-    
     
          
 
     
          DescribeBy(object, ...)
          
-    
+
     
          Arguments
          
     
@@ -120,41 +124,43 @@ 
          Arg
       
          
-      
+      
          
     
          An object
          		
     
          
     
          …...
       
          Further arguments to be passed
          		
     
          
     
          
-    
+
     
          See also
          
 
-    
          psych::describe
          
-    
+    
          psych::describe
          
 
   
   
        
 
   
 
 
+
       
        
       
        
   
        Developed by Alexander Robitzsch.
        
 
        
 
 
        
-  
        Site built with pkgdown 1.3.0.
        
+  
        Site built with pkgdown 1.4.1.
        
 
        
+
       
        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.WrightMap.html b/docs/reference/IRT.WrightMap.html
index 36f2e34..a0bfa6c 100644
--- a/docs/reference/IRT.WrightMap.html
+++ b/docs/reference/IRT.WrightMap.html
@@ -8,21 +8,25 @@
 
 Wright Map for Item Response Models by Using the <span class="pkg">WrightMap</span> Package — IRT.WrightMap • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-WrightMap Package — IRT.WrightMap" />
 
+WrightMap Package — IRT.WrightMap" />
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
          
         
        • 
   
-    
+    
      
   
 
          • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
          
@@ -109,21 +115,19 @@ 
          Wright Map for Item Response Models by Using the WrightMap
     
          
 
     
          
-    
     
          This function creates a Wright map and works as a wrapper to the
-wrightMap function in
+wrightMap function in
 the WrightMap package. The arguments thetas and
 thresholds are automatically generated from fitted
 objects in TAM.
          
-    
     
          
 
-    
          # S3 method for tam.mml
-IRT.WrightMap(object, prob.lvl=.5, type="PV", …)
+    
          # S3 method for tam.mml
+IRT.WrightMap(object, prob.lvl=.5, type="PV", ...)
+
+# S3 method for tamaan
+IRT.WrightMap(object, prob.lvl=.5, type="PV", ...)
          
 
-# S3 method for tamaan
-IRT.WrightMap(object, prob.lvl=.5, type="PV", …)
          
-    
     
          Arguments
          
     
@@ -142,70 +146,67 @@ 
          Arg
 "Pop" (population trait distribution) can be specified.
          
     
     
          
-      
+      
          
     
          …...
       
          Further arguments to be passed in the
-wrightMap (WrightMap) function.
+wrightMap (WrightMap) function.
 See Examples.
          		
     
          
     
          
-    
+
     
          Details
          
 
     
          A Wright map is only created for models with an assumed normal distribution.
 Hence, not for all models of the tamaan functions
 Wright maps are created.
          
-    
     
          Value
          
 
     
          A Wright map generated by the WrightMap package.
          
-    
     
          See also
          
 
-    
          See the WrightMap::wrightMap function in
+    
          See the WrightMap::wrightMap function in
 the WrightMap package.
          
-    
 
     
          Examples
          
-    
          # NOT RUN {
-library(WrightMap)
+    
          if (FALSE) {
+library(WrightMap)
 
 #############################################################################
 # EXAMPLE 1: Unidimensional models dichotomous data
 #############################################################################
 
-data(data.sim.rasch)
-str(data.sim.rasch)
+data(data.sim.rasch)
+str(data.sim.rasch)
 dat <- data.sim.rasch
 
 # fit Rasch model
-mod1 <- TAM::tam.mml(resp=dat)
+mod1 <- TAM::tam.mml(resp=dat)
 # Wright map
 IRT.WrightMap( mod1 )
 # some customized plots
-IRT.WrightMap( mod1, show.thr.lab=FALSE, label.items=c(1:40), label.items.rows=3)
+IRT.WrightMap( mod1, show.thr.lab=FALSE, label.items=c(1:40), label.items.rows=3)
 
-IRT.WrightMap( mod1,  show.thr.sym=FALSE, thr.lab.text=paste0("I",1:ncol(dat)),
+IRT.WrightMap( mod1,  show.thr.sym=FALSE, thr.lab.text=paste0("I",1:ncol(dat)),
      label.items="", label.items.ticks=FALSE)
 
 #--- direct specification with wrightMap function
-theta <- TAM::tam.wle(mod1)$theta
-thr <- TAM::tam.threshold(mod1)
+theta <- TAM::tam.wle(mod1)$theta
+thr <- TAM::tam.threshold(mod1)
 
 # default wrightMap plots
-WrightMap::wrightMap( theta, thr, label.items.srt=90)
-WrightMap::wrightMap( theta, t(thr), label.items=c("items") )
+WrightMap::wrightMap( theta, thr, label.items.srt=90)
+WrightMap::wrightMap( theta, t(thr), label.items=c("items") )
 
 # stack all items below each other
-thr.lab.text <- matrix( "", 1, ncol(dat) )
-thr.lab.text[1,] <- colnames(dat)
-WrightMap::wrightMap( theta, t(thr), label.items=c("items"),
+thr.lab.text <- matrix( "", 1, ncol(dat) )
+thr.lab.text[1,] <- colnames(dat)
+WrightMap::wrightMap( theta, t(thr), label.items=c("items"),
        thr.lab.text=thr.lab.text, show.thr.sym=FALSE )
 
 #############################################################################
 # EXAMPLE 2: Unidimensional model polytomous data
 #############################################################################
 
-data( data.Students, package="CDM")
+data( data.Students, package="CDM")
 dat <- data.Students
 
 # fit generalized partial credit model using the tamaan function
@@ -214,34 +215,34 @@ 
          Examp
   SC=~ sc1__sc4
   SC ~~ 1*SC
     "
-mod1 <- TAM::tamaan( tammodel, dat )
+mod1 <- TAM::tamaan( tammodel, dat )
 # create item level colors
-library(RColorBrewer)
+library(RColorBrewer)
 ncat <- 3               # number of category parameters
-I <- ncol(mod1$resp)    # number of items
-itemlevelcolors <- matrix(rep( RColorBrewer::brewer.pal(ncat, "Set1"), I),
+I <- ncol(mod1$resp)    # number of items
+itemlevelcolors <- matrix(rep( RColorBrewer::brewer.pal(ncat, "Set1"), I),
         byrow=TRUE, ncol=ncat)
 # Wright map
 IRT.WrightMap(mod1, prob.lvl=.625, thr.sym.col.fg=itemlevelcolors,
-     thr.sym.col.bg=itemlevelcolors, label.items=colnames( mod1$resp) )
+     thr.sym.col.bg=itemlevelcolors, label.items=colnames( mod1$resp) )
 
 #############################################################################
 # EXAMPLE 3: Multidimensional item response model
 #############################################################################
 
-data( data.read, package="sirt")
+data( data.read, package="sirt")
 dat <- data.read
 
 # fit three-dimensional Rasch model
-Q <- matrix( 0, nrow=12, ncol=3 )
+Q <- matrix( 0, nrow=12, ncol=3 )
 Q[1:4,1] <- Q[5:8,2] <- Q[9:12,3] <- 1
-mod1 <- TAM::tam.mml( dat, Q=Q, control=list(maxiter=20, snodes=1000)  )
-summary(mod1)
+mod1 <- TAM::tam.mml( dat, Q=Q, control=list(maxiter=20, snodes=1000)  )
+summary(mod1)
 # define matrix with colors for thresholds
-c1 <- matrix( c( rep(1,4), rep(2,4), rep(4,4)), ncol=1 )
+c1 <- matrix( c( rep(1,4), rep(2,4), rep(4,4)), ncol=1 )
 # create Wright map using WLE
 IRT.WrightMap( mod1, prob.lvl=.65, type="WLE", thr.lab.col=c1, thr.sym.col.fg=c1,
-        thr.sym.col.bg=c1, label.items=colnames(dat) )
+        thr.sym.col.bg=c1, label.items=colnames(dat) )
 # Wright map using PV (the default)
 IRT.WrightMap( mod1, prob.lvl=.65, type="PV" )
 # Wright map using population distribution
@@ -254,34 +255,34 @@ 
          Examp
 # This example is copied from
 # http://wrightmap.org/post/107431190622/wrightmap-multifaceted-models
 
-library(WrightMap)
-data(data.ex10)
+library(WrightMap)
+data(data.ex10)
 dat <- data.ex10
 
 #--- fit multi-faceted Rasch model
 facets <- dat[, "rater", drop=FALSE]  # define facet (rater)
 pid <- dat$pid  # define person identifier (a person occurs multiple times)
-resp <- dat[, -c(1:2)]  # item response data
+resp <- dat[, -c(1:2)]  # item response data
 formulaA <- ~item * rater  # formula
-mod <- TAM::tam.mml.mfr(resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid)
+mod <- TAM::tam.mml.mfr(resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid)
 
 # person parameters
-persons.mod <- TAM::tam.wle(mod)
+persons.mod <- TAM::tam.wle(mod)
 theta <- persons.mod$theta
 # thresholds
-thr <- TAM::tam.threshold(mod)
-item.labs <- c("I0001", "I0002", "I0003", "I0004", "I0005")
-rater.labs <- c("rater1", "rater2", "rater3")
+thr <- TAM::tam.threshold(mod)
+item.labs <- c("I0001", "I0002", "I0003", "I0004", "I0005")
+rater.labs <- c("rater1", "rater2", "rater3")
 
 #--- Plot 1: Item specific
-thr1 <- matrix(thr, nrow=5, byrow=TRUE)
-WrightMap::wrightMap(theta, thr1, label.items=item.labs,
-   thr.lab.text=rep(rater.labs, each=5))
+thr1 <- matrix(thr, nrow=5, byrow=TRUE)
+WrightMap::wrightMap(theta, thr1, label.items=item.labs,
+   thr.lab.text=rep(rater.labs, each=5))
 
 #--- Plot 2: Rater specific
-thr2 <- matrix(thr, nrow=3)
-WrightMap::wrightMap(theta, thr2, label.items=rater.labs,
-   thr.lab.text=rep(item.labs,  each=3), axis.items="Raters")
+thr2 <- matrix(thr, nrow=3)
+WrightMap::wrightMap(theta, thr2, label.items=rater.labs,
+   thr.lab.text=rep(item.labs,  each=3), axis.items="Raters")
 
 #--- Plot 3a: item, rater and item*rater parameters
 pars <- mod$xsi.facets$xsi
@@ -291,79 +292,79 @@ 
          Examp
 rater.par <- pars[facet=="rater"]
 item_rat <- pars[facet=="item:rater"]
 
-len <- length(item_rat)
-item.long <- c(item.par, rep(NA, len - length(item.par)))
-rater.long <- c(rater.par, rep(NA, len - length(rater.par)))
+len <- length(item_rat)
+item.long <- c(item.par, rep(NA, len - length(item.par)))
+rater.long <- c(rater.par, rep(NA, len - length(rater.par)))
 ir.labs <- mod$xsi.facets$parameter[facet=="item:rater"]
 
-WrightMap::wrightMap(theta, rbind(item.long, rater.long, item_rat),
-    label.items=c("Items",  "Raters", "Item*Raters"),
-    thr.lab.text=rbind(item.labs, rater.labs, ir.labs), axis.items="")
+WrightMap::wrightMap(theta, rbind(item.long, rater.long, item_rat),
+    label.items=c("Items",  "Raters", "Item*Raters"),
+    thr.lab.text=rbind(item.labs, rater.labs, ir.labs), axis.items="")
 
 #--- Plot 3b: item, rater and item*rater (separated by raters) parameters
 
 # parameters item*rater
-ir_rater <- matrix(item_rat, nrow=3, byrow=TRUE)
+ir_rater <- matrix(item_rat, nrow=3, byrow=TRUE)
 # define matrix of thresholds
-thr <- rbind(item.par, c(rater.par, NA, NA), ir_rater)
+thr <- rbind(item.par, c(rater.par, NA, NA), ir_rater)
 # matrix with threshold labels
-thr.lab.text <- rbind(item.labs, rater.labs,
-           matrix(item.labs, nrow=3, ncol=5, byrow=TRUE))
+thr.lab.text <- rbind(item.labs, rater.labs,
+           matrix(item.labs, nrow=3, ncol=5, byrow=TRUE))
 
-WrightMap::wrightMap(theta, thresholds=thr,
-      label.items=c("Items", "Raters", "Item*Raters (R1)",
+WrightMap::wrightMap(theta, thresholds=thr,
+      label.items=c("Items", "Raters", "Item*Raters (R1)",
                            "Item*Raters (R2)", "Item*Raters (R3)"),
       axis.items="", thr.lab.text=thr.lab.text )
 
 #--- Plot 3c: item, rater and item*rater (separated by items) parameters
 
 # thresholds
-ir_item <- matrix(item_rat, nrow=5)
-thr <- rbind(item.par, c(rater.par, NA, NA), cbind(ir_item, NA, NA))
+ir_item <- matrix(item_rat, nrow=5)
+thr <- rbind(item.par, c(rater.par, NA, NA), cbind(ir_item, NA, NA))
 # labels
-label.items <- c("Items", "Raters", "Item*Raters\n (I1)", "Item*Raters \n(I2)",
+label.items <- c("Items", "Raters", "Item*Raters\n (I1)", "Item*Raters \n(I2)",
      "Item*Raters \n(I3)", "Item*Raters \n (I4)", "Item*Raters \n(I5)")
-thr.lab.text <- rbind(item.labs,
-          matrix(c(rater.labs, NA, NA), nrow=6, ncol=5, byrow=TRUE))
+thr.lab.text <- rbind(item.labs,
+          matrix(c(rater.labs, NA, NA), nrow=6, ncol=5, byrow=TRUE))
 
-WrightMap::wrightMap(theta, thr,  label.items=label.items,
+WrightMap::wrightMap(theta, thr,  label.items=label.items,
       axis.items="", thr.lab.text=thr.lab.text  )
-# }
          
+}
          
   
          
   
 
 
+
       
          
       
          
   
          Developed by Alexander Robitzsch.
          
 
          
 
 
          
-  
          Site built with pkgdown 1.3.0.
          
+  
          Site built with pkgdown 1.4.1.
          
 
          
+
       
          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.cv.html b/docs/reference/IRT.cv.html
index 5ca1efd..b29e1d5 100644
--- a/docs/reference/IRT.cv.html
+++ b/docs/reference/IRT.cv.html
@@ -8,21 +8,25 @@
 
 Cross-Validation of a Fitted IRT Model — IRT.cv • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
            
         
          • 
   
-    
+    
      
   
 
            • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
            
@@ -105,13 +111,11 @@ 
            Cross-Validation of a Fitted IRT Model
            
     
            
 
     
            
-    
     
            This S3 method performs a cross-validation of a fitted item response model.
            
-    
     
            
 
     
            IRT.cv(object, ...)
            
-    
+
     
            Arguments
            
     
@@ -120,41 +124,43 @@ 
            Arg
       
            
-      
+      
            
     
            Object
            		
     
            
     
            …...
       
            Further arguments to be passed
            		
     
            
     
            
-    
+
     
            Value
            
 
     
            Numeric value: the cross-validated deviance value
            
-    
 
   
   
          
 
   
 
 
+
       
          
       
          
   
          Developed by Alexander Robitzsch.
          
 
          
 
 
          
-  
          Site built with pkgdown 1.3.0.
          
+  
          Site built with pkgdown 1.4.1.
          
 
          
+
       
          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.data.tam.html b/docs/reference/IRT.data.tam.html
index 2fb8300..2acb4ff 100644
--- a/docs/reference/IRT.data.tam.html
+++ b/docs/reference/IRT.data.tam.html
@@ -8,21 +8,25 @@
 
 Extracting Item Response Dataset — IRT.data.tam • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
            
         
          • 
   
-    
+    
      
   
 
            • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
            
@@ -107,22 +113,20 @@ 
            Extracting Item Response Dataset
            
     
            
 
     
            
-    
     
            Extracts the used data set for models
-fitted in TAM. See CDM::IRT.data
+fitted in TAM. See CDM::IRT.data
  for more details.
            
-    
     
            
 
-    
            # S3 method for tam.mml
-IRT.data(object, …)
+    
            # S3 method for tam.mml
+IRT.data(object, ...)
 
-# S3 method for tam.mml.3pl
-IRT.data(object, …)
+# S3 method for tam.mml.3pl
+IRT.data(object, ...)
+
+# S3 method for tamaan
+IRT.data(object, ...)
            
 
-# S3 method for tamaan
-IRT.data(object, …)
            
-    
     
            Arguments
            
     
@@ -132,58 +136,59 @@ 
            Arg
 tam.mml.3pl or tamaan.
            
     
     
            
-      
+      
            
     
            …...
       
            Further arguments to be passed
            		
     
            
     
            
-    
+
     
            Value
            
 
     
            A dataset with item responses
            
-    
 
     
            Examples
            
-    
            # NOT RUN {
+    
            if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 dat <- data.sim.rasch
 
 # estimate model
-mod1 <- TAM::tam.mml(dat)
+mod1 <- TAM::tam.mml(dat)
 # extract dataset (and weights and group if available)
 dmod1 <- IRT.data(mod1)
-str(dmod1)
-# }
            
+str(dmod1)
+}
            
   
   
 
 
+
       
            
       
            
   
            Developed by Alexander Robitzsch.
            
 
            
 
 
            
-  
            Site built with pkgdown 1.3.0.
            
+  
            Site built with pkgdown 1.4.1.
            
 
            
+
       
            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.drawPV.html b/docs/reference/IRT.drawPV.html
index cf2a4ba..fd2c772 100644
--- a/docs/reference/IRT.drawPV.html
+++ b/docs/reference/IRT.drawPV.html
@@ -8,21 +8,25 @@
 
 Function for Drawing Plausible Values — IRT.drawPV • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
              
         
            • 
   
-    
+    
      
   
 
              • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
              
@@ -108,22 +114,20 @@ 
              Function for Drawing Plausible Values
              
     
              
 
     
              
-    
     
              This function draws plausible values of a continuous latent variable
 based on a fitted object for which the
-CDM::IRT.posterior method
+CDM::IRT.posterior method
 is defined.
              
-    
     
              
 
     
              IRT.drawPV(object,NPV=5)
              
-    
+
     
              Arguments
              
     
-      
@@ -131,54 +135,55 @@ 
              Arg
       
              
     
     
              
       object
              Object for which the method CDM::IRT.posterior
+      
              Object for which the method CDM::IRT.posterior
   does exist.
              		
     
              
     
              Number of plausible values to be drawn.
              		
     
              
     
              
-    
+
     
              Value
              
 
     
              Matrix with plausible values
              
-    
 
     
              Examples
              
-    
              # NOT RUN {
+    
              if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Plausible value imputation for Rasch model in sirt
 #############################################################################
 
-library(sirt)
-data(data.read,package="sirt")
+library(sirt)
+data(data.read,package="sirt")
 dat <- data.read
 
 # fit Rasch model
-mod <- rasch.mml2(dat)
+mod <- rasch.mml2(dat)
 # draw 10 plausible values
-pv1 <- TAM::IRT.drawPV(mod, NPV=10)
-# }
              
+pv1 <- TAM::IRT.drawPV(mod, NPV=10)
+}
              
   
   
 
 
+
       
              
       
              
   
              Developed by Alexander Robitzsch.
              
 
              
 
 
              
-  
              Site built with pkgdown 1.3.0.
              
+  
              Site built with pkgdown 1.4.1.
              
 
              
+
       
              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.expectedCounts.tam.html b/docs/reference/IRT.expectedCounts.tam.html
index f1ab91c..d4f6bd4 100644
--- a/docs/reference/IRT.expectedCounts.tam.html
+++ b/docs/reference/IRT.expectedCounts.tam.html
@@ -8,21 +8,25 @@
 
 Extracting Expected Counts — IRT.expectedCounts • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                
         
              • 
   
-    
+    
      
   
 
                • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                
@@ -107,28 +113,26 @@ 
                Extracting Expected Counts
                
     
                
 
     
                
-    
     
                Extracts expected counts for models
-fitted in TAM. See CDM::IRT.expectedCounts
+fitted in TAM. See CDM::IRT.expectedCounts
 for more details.
                
-    
     
                
 
-    
                # S3 method for tam
-IRT.expectedCounts(object, …)
+    
                # S3 method for tam
+IRT.expectedCounts(object, ...)
 
-# S3 method for tam.mml
-IRT.expectedCounts(object, …)
+# S3 method for tam.mml
+IRT.expectedCounts(object, ...)
 
-# S3 method for tam.mml.3pl
-IRT.expectedCounts(object, …)
+# S3 method for tam.mml.3pl
+IRT.expectedCounts(object, ...)
 
-# S3 method for tamaan
-IRT.expectedCounts(object, …)
+# S3 method for tamaan
+IRT.expectedCounts(object, ...)
+
+# S3 method for tam.np
+IRT.expectedCounts(object, ...)
                
 
-# S3 method for tam.np
-IRT.expectedCounts(object, …)
                
-    
     
                Arguments
                
     
@@ -138,55 +142,56 @@ 
                Arg
 tam.mml.3pl, tam.np or tamaan.
                
     
     
                
-      
+      
                
     
                …...
       
                Further arguments to be passed
                		
     
                
     
                
-    
+
     
                Value
                
 
-    
                See CDM::IRT.expectedCounts.
                
-    
+    
                See CDM::IRT.expectedCounts.
                
 
     
                Examples
                
-    
                # NOT RUN {
+    
                if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch - extract expected counts
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # 1PL estimation
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 # extract expected counts
 IRT.expectedCounts(mod1)
-# }
                
+}
                
   
   
 
 
+
       
                
       
                
   
                Developed by Alexander Robitzsch.
                
 
                
 
 
                
-  
                Site built with pkgdown 1.3.0.
                
+  
                Site built with pkgdown 1.4.1.
                
 
                
+
       
                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.factor.scores.tam.html b/docs/reference/IRT.factor.scores.tam.html
index 517165c..62fc10b 100644
--- a/docs/reference/IRT.factor.scores.tam.html
+++ b/docs/reference/IRT.factor.scores.tam.html
@@ -8,21 +8,25 @@
 
 Extracting Factor Scores in <span class="pkg">TAM</span> — IRT.factor.scores • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM — IRT.factor.scores" />
 
+TAM — IRT.factor.scores" />
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                  
         
                • 
   
-    
+    
      
   
 
                  • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                  
@@ -107,25 +113,23 @@ 
                  Extracting Factor Scores in TAM
                  
     
                  
 
     
                  
-    
     
                  Extracts factor scores for models
-fitted in TAM. See CDM::IRT.factor.scores
+fitted in TAM. See CDM::IRT.factor.scores
 for more details.
                  
-    
     
                  
 
-    
                  # S3 method for tam
-IRT.factor.scores(object, type="EAP", …)
+    
                  # S3 method for tam
+IRT.factor.scores(object, type="EAP", ...)
 
-# S3 method for tam.mml
-IRT.factor.scores(object, type="EAP", …)
+# S3 method for tam.mml
+IRT.factor.scores(object, type="EAP", ...)
 
-# S3 method for tam.mml.3pl
-IRT.factor.scores(object, type="EAP", …)
+# S3 method for tam.mml.3pl
+IRT.factor.scores(object, type="EAP", ...)
+
+# S3 method for tamaan
+IRT.factor.scores(object, type="EAP", ...)
                  
 
-# S3 method for tamaan
-IRT.factor.scores(object, type="EAP", …)
                  
-    
     
                  Arguments
                  
     
@@ -141,62 +145,63 @@ 
                  Arg
     type="MLE" can only be used for objects of class
      tam.mml. Further arguments to the
      used function tam.wle can be specified
-     with ….
                  
+     with ....
                  
     
     
                  
-      
+      
                  
     
                  …...
       
                  Further arguments to be passed
                  		
     
                  
     
                  
-    
+
     
                  Value
                  
 
-    
                  See CDM::IRT.factor.scores.
                  
-    
+    
                  See CDM::IRT.factor.scores.
                  
 
     
                  Examples
                  
-    
                  # NOT RUN {
+    
                  if (FALSE) {
 #############################################################################
 # EXAMPLE 1: data.sim.rasch - Different factor scores in Rasch model
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # 1PL estimation
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 # EAP
 pmod1 <- IRT.factor.scores( mod1 )
 # WLE
 pmod1 <- IRT.factor.scores( mod1, type="WLE" )
 # MLE
 pmod1 <- IRT.factor.scores( mod1, type="MLE" )
-# }
                  
+}
                  
   
   
 
 
+
       
                  
       
                  
   
                  Developed by Alexander Robitzsch.
                  
 
                  
 
 
                  
-  
                  Site built with pkgdown 1.3.0.
                  
+  
                  Site built with pkgdown 1.4.1.
                  
 
                  
+
       
                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.frequencies.tam.html b/docs/reference/IRT.frequencies.tam.html
index afc3460..1613f31 100644
--- a/docs/reference/IRT.frequencies.tam.html
+++ b/docs/reference/IRT.frequencies.tam.html
@@ -8,21 +8,25 @@
 
 Observed and Expected Frequencies for Univariate and Bivariate Distributions — IRT.frequencies.tam • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                    
         
                  • 
   
-    
+    
      
   
 
                    • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                    
@@ -107,22 +113,20 @@ 
                    Observed and Expected Frequencies for Univariate and Bivariate Distributions
     
                    
 
     
                    
-    
     
                    Computes observed and expected frequencies for univariate and bivariate distributions
-for models fitted in TAM. See CDM::IRT.frequencies
+for models fitted in TAM. See CDM::IRT.frequencies
  for more details.
                    
-    
     
                    
 
-    
                    # S3 method for tam.mml
-IRT.frequencies(object, …)
+    
                    # S3 method for tam.mml
+IRT.frequencies(object, ...)
 
-# S3 method for tam.mml.3pl
-IRT.frequencies(object, …)
+# S3 method for tam.mml.3pl
+IRT.frequencies(object, ...)
+
+# S3 method for tamaan
+IRT.frequencies(object, ...)
                    
 
-# S3 method for tamaan
-IRT.frequencies(object, …)
                    
-    
     
                    Arguments
                    
     
@@ -132,64 +136,63 @@ 
                    Arg
 tam.mml.3pl or tamaan.
                    
     
     
                    
-      
+      
                    
     
                    …...
       
                    Further arguments to be passed
                    		
     
                    
     
                    
-    
+
     
                    Value
                    
 
-    
                    See CDM::IRT.frequencies.
                    
-    
+    
                    See CDM::IRT.frequencies.
                    
     
                    See also
                    
 
-    
                    CDM::IRT.frequencies
                    
-    
+    
                    CDM::IRT.frequencies
                    
 
     
                    Examples
                    
-    
                    # NOT RUN {
+    
                    if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 dat <- data.sim.rasch
 
 # estimate model
-mod1 <- TAM::tam.mml(dat)
+mod1 <- TAM::tam.mml(dat)
 # compute observed and expected frequencies
 fmod1 <- IRT.frequencies(mod1)
-str(fmod1)
-# }
                    
+str(fmod1)
+}
                    
   
   
 
 
+
       
                    
       
                    
   
                    Developed by Alexander Robitzsch.
                    
 
                    
 
 
                    
-  
                    Site built with pkgdown 1.3.0.
                    
+  
                    Site built with pkgdown 1.4.1.
                    
 
                    
+
       
                    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.informationCurves.html b/docs/reference/IRT.informationCurves.html
index 8a89661..35e68d9 100644
--- a/docs/reference/IRT.informationCurves.html
+++ b/docs/reference/IRT.informationCurves.html
@@ -8,21 +8,25 @@
 
 Item and Test Information Curve — IRT.informationCurves • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                      
         
                    • 
   
-    
+    
      
   
 
                      • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                      
@@ -105,32 +111,30 @@ 
                      Item and Test Information Curve
                      
     
                      
 
     
                      
-    
     
                      An S3 method which computes item and test information curves, see Muraki (1993).
                      
-    
     
                      
 
-    
                      IRT.informationCurves(object, ...)
+    
                      IRT.informationCurves(object, ...)
 
-# S3 method for tam.mml
-IRT.informationCurves( object, h=.0001, iIndex=NULL,
-          theta=NULL, ... )
+# S3 method for tam.mml
+IRT.informationCurves( object, h=.0001, iIndex=NULL,
+          theta=NULL, ... )
 
-# S3 method for tam.mml.2pl
-IRT.informationCurves( object, h=.0001, iIndex=NULL,
-          theta=NULL, ... )
+# S3 method for tam.mml.2pl
+IRT.informationCurves( object, h=.0001, iIndex=NULL,
+          theta=NULL, ... )
 
-# S3 method for tam.mml.mfr
-IRT.informationCurves( object, h=.0001, iIndex=NULL,
-          theta=NULL, ... )
+# S3 method for tam.mml.mfr
+IRT.informationCurves( object, h=.0001, iIndex=NULL,
+          theta=NULL, ... )
 
-# S3 method for tam.mml.3pl
-IRT.informationCurves( object, h=.0001, iIndex=NULL,
-          theta=NULL, ... )
+# S3 method for tam.mml.3pl
+IRT.informationCurves( object, h=.0001, iIndex=NULL,
+          theta=NULL, ... )
+
+# S3 method for IRT.informationCurves
+plot(x, curve_type="test", ...)
                      
 
-# S3 method for IRT.informationCurves
-plot(x, curve_type="test", …)
                      
-    
     
                      Arguments
                      
     
@@ -140,7 +144,7 @@ 
                      Arg
 or tam.mml.3pl.
                      
     
     
                      
-      
+      
@@ -169,7 +173,7 @@ 
                      Arg
 or tam.mml.3pl.
                      
     
                      
     
                      …...
       
                      Further arguments to be passed
                      		
     
                      
     
                      
     
                      
-    
+
     
                      Value
                      
 
     
                      List with following entries
                      
@@ -179,116 +183,115 @@ 
                      Value
                      
   
                      info_curves_categories
                      Item-category information curves
                      
   
                      theta
                      Used \(\theta\) grid
                      
 
-    
     
                      References
                      
 
     
                      Muraki, E. (1993). Information functions of the generalized partial credit
 model. Applied Psychological Measurement, 17(4), 351-363.
                      
-    
 
     
                      Examples
                      
-    
                      # NOT RUN {
+    
                      if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Dichotomous data | data.read
 #############################################################################
 
-data(data.read, package="sirt")
+data(data.read, package="sirt")
 dat <- data.read
 
 # fit 2PL model
-mod1 <- TAM::tam.mml.2pl( dat )
-summary(mod1)
+mod1 <- TAM::tam.mml.2pl( dat )
+summary(mod1)
 
 # compute information curves at grid seq(-5,5,length=100)
-imod1 <- TAM::IRT.informationCurves( mod1, theta=seq(-5,5,len=100)  )
-str(imod1)
+imod1 <- TAM::IRT.informationCurves( mod1, theta=seq(-5,5,len=100)  )
+str(imod1)
 # plot test information
-plot( imod1 )
+plot( imod1 )
 # plot standard error curve
-plot( imod1, curve_type="se", xlim=c(-3,2) )
+plot( imod1, curve_type="se", xlim=c(-3,2) )
 # cutomized plot
-plot( imod1, curve_type="se", xlim=c(-3,2), ylim=c(0,2), lwd=2, lty=3)
+plot( imod1, curve_type="se", xlim=c(-3,2), ylim=c(0,2), lwd=2, lty=3)
 
 #############################################################################
 # EXAMPLE 2: Mixed dichotomous and polytomous data
 #############################################################################
 
-data(data.timssAusTwn.scored, package="TAM")
+data(data.timssAusTwn.scored, package="TAM")
 dat <- data.timssAusTwn.scored
 # select item response data
-items <- grep( "M0", colnames(dat), value=TRUE )
+items <- grep( "M0", colnames(dat), value=TRUE )
 resp <- dat[, items ]
 
 #*** Model 1: Partial credit model
-mod1 <- TAM::tam.mml( resp )
-summary(mod1)
+mod1 <- TAM::tam.mml( resp )
+summary(mod1)
 # information curves
-imod1 <- TAM::IRT.informationCurves( mod1, theta=seq(-3,3,len=20)  )
+imod1 <- TAM::IRT.informationCurves( mod1, theta=seq(-3,3,len=20)  )
 
 #*** Model 2: Generalized partial credit model
-mod2 <- TAM::tam.mml.2pl( resp, irtmodel="GPCM")
-summary(mod2)
-imod2 <- TAM::IRT.informationCurves( mod2 )
+mod2 <- TAM::tam.mml.2pl( resp, irtmodel="GPCM")
+summary(mod2)
+imod2 <- TAM::IRT.informationCurves( mod2 )
 
 #*** Model 3: Mixed 3PL and generalized partial credit model
-psych::describe(resp)
-maxK <- apply( resp, 2, max, na.rm=TRUE )
-I <- ncol(resp)
+psych::describe(resp)
+maxK <- apply( resp, 2, max, na.rm=TRUE )
+I <- ncol(resp)
 # specify guessing parameters, including a prior distribution
 est.guess <- 1:I
 est.guess[ maxK > 1 ] <- 0
 guess <- .2*(est.guess >0)
-guess.prior <- matrix( 0, nrow=I, ncol=2 )
+guess.prior <- matrix( 0, nrow=I, ncol=2 )
 guess.prior[ est.guess  > 0, 1] <- 5
 guess.prior[ est.guess  > 0, 2] <- 17
 
 # fit model
-mod3 <- TAM::tam.mml.3pl( resp, gammaslope.des="2PL", est.guess=est.guess, guess=guess,
+mod3 <- TAM::tam.mml.3pl( resp, gammaslope.des="2PL", est.guess=est.guess, guess=guess,
            guess.prior=guess.prior,
-           control=list( maxiter=100, Msteps=10, fac.oldxsi=0.1,
-                        nodes=seq(-8,8,len=41) ),  est.variance=FALSE )
-summary(mod3)
+           control=list( maxiter=100, Msteps=10, fac.oldxsi=0.1,
+                        nodes=seq(-8,8,len=41) ),  est.variance=FALSE )
+summary(mod3)
 
 # information curves
-imod3 <- TAM::IRT.informationCurves( mod3 )
+imod3 <- TAM::IRT.informationCurves( mod3 )
 imod3
 
 #*** estimate model in mirt package
-library(mirt)
-itemtype <- rep("gpcm", I)
+library(mirt)
+itemtype <- rep("gpcm", I)
 itemtype[ maxK==1] <- "3PL"
-mod3b <- mirt::mirt(resp, 1, itemtype=itemtype, verbose=TRUE )
-print(mod3b)
-# }
                      
+mod3b <- mirt::mirt(resp, 1, itemtype=itemtype, verbose=TRUE )
+print(mod3b)
+}
                      
   
   
 
 
+
       
                      
       
                      
   
                      Developed by Alexander Robitzsch.
                      
 
                      
 
 
                      
-  
                      Site built with pkgdown 1.3.0.
                      
+  
                      Site built with pkgdown 1.4.1.
                      
 
                      
+
       
                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.irfprob.tam.html b/docs/reference/IRT.irfprob.tam.html
index 0ca575d..f9dd0da 100644
--- a/docs/reference/IRT.irfprob.tam.html
+++ b/docs/reference/IRT.irfprob.tam.html
@@ -8,21 +8,25 @@
 
 Extracting Item Response Functions — IRT.irfprob • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                        
         
                      • 
   
-    
+    
      
   
 
                        • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                        
@@ -107,28 +113,26 @@ 
                        Extracting Item Response Functions
                        
     
                        
 
     
                        
-    
     
                        Extracts item response functions for models
-fitted in TAM. See CDM::IRT.irfprob
+fitted in TAM. See CDM::IRT.irfprob
  for more details.
                        
-    
     
                        
 
-    
                        # S3 method for tam
-IRT.irfprob(object, …)
+    
                        # S3 method for tam
+IRT.irfprob(object, ...)
 
-# S3 method for tam.mml
-IRT.irfprob(object, …)
+# S3 method for tam.mml
+IRT.irfprob(object, ...)
 
-# S3 method for tam.mml.3pl
-IRT.irfprob(object, …)
+# S3 method for tam.mml.3pl
+IRT.irfprob(object, ...)
 
-# S3 method for tamaan
-IRT.irfprob(object, …)
+# S3 method for tamaan
+IRT.irfprob(object, ...)
+
+# S3 method for tam.np
+IRT.irfprob(object, ...)
                        
 
-# S3 method for tam.np
-IRT.irfprob(object, …)
                        
-    
     
                        Arguments
                        
     
@@ -138,54 +142,53 @@ 
                        Arg
 tam.mml.3pl, tam.np or tamaan.
                        
     
     
                        
-      
+      
                        
     
                        …...
       
                        Further arguments to be passed
                        		
     
                        
     
                        
-    
+
     
                        Value
                        
 
-    
                        See CDM::IRT.irfprob.
                        
-    
+    
                        See CDM::IRT.irfprob.
                        
 
     
                        Examples
                        
-    
                        # NOT RUN {
-#############################################################################
+    
                        #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch - item response functions
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # 1PL estimation
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
-IRT.irfprob(mod1)
-# }
                        
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+IRT.irfprob(mod1)
                        
   
   
 
 
+
       
                        
       
                        
   
                        Developed by Alexander Robitzsch.
                        
 
                        
 
 
                        
-  
                        Site built with pkgdown 1.3.0.
                        
+  
                        Site built with pkgdown 1.4.1.
                        
 
                        
+
       
                        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.itemfit.html b/docs/reference/IRT.itemfit.html
index 4271f3f..4fdbaf8 100644
--- a/docs/reference/IRT.itemfit.html
+++ b/docs/reference/IRT.itemfit.html
@@ -8,21 +8,25 @@
 
 RMSD Item Fit Statistics for <span class="pkg">TAM</span> Objects — IRT.itemfit.tam • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM Objects — IRT.itemfit.tam" />
 
+TAM Objects — IRT.itemfit.tam" />
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
                          
         
                        • 
   
-    
+    
      
   
 
                          • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
                          
@@ -109,27 +115,25 @@ 
                          RMSD Item Fit Statistics for TAM Objects
                          
     
                          
 
     
                          
-    
     
                          Computes the RMSD item fit statistic (formerly labeled as RMSEA;
 Yamamoto, Khorramdel, & von Davier, 2013) for fitted objects in the
 TAM package, see
-CDM::IRT.itemfit and
-CDM::IRT.RMSD.
                          
-    
+CDM::IRT.itemfit and
+CDM::IRT.RMSD.
                          
     
                          
 
-    
                          # S3 method for tam.mml
-IRT.itemfit(object, method="RMSD", …)
+    
                          # S3 method for tam.mml
+IRT.itemfit(object, method="RMSD", ...)
 
-# S3 method for tam.mml.2pl
-IRT.itemfit(object, method="RMSD", …)
+# S3 method for tam.mml.2pl
+IRT.itemfit(object, method="RMSD", ...)
 
-# S3 method for tam.mml.mfr
-IRT.itemfit(object, method="RMSD", …)
+# S3 method for tam.mml.mfr
+IRT.itemfit(object, method="RMSD", ...)
+
+# S3 method for tam.mml.3pl
+IRT.itemfit(object, method="RMSD", ...)
                          
 
-# S3 method for tam.mml.3pl
-IRT.itemfit(object, method="RMSD", …)
                          
-    
     
                          Arguments
                          
     
@@ -142,102 +146,101 @@ 
                          Arg
       
                          
-      
+      
                          
     
                          method
       
                          Requested method for item fit calculation. Currently,
 only the RMSD fit statistic (formerly labeled as the RMSEA statistic,
-see CDM::IRT.RMSD)
+see CDM::IRT.RMSD)
 can be used.
                          		
     
                          
     
                          …...
       
                          Further arguments to be passed.
                          		
     
                          
     
                          
-    
+
     
                          References
                          
 
     
                          Yamamoto, K., Khorramdel, L., & von Davier, M. (2013). Scaling PIAAC cognitive data.
 In OECD (Eds.). Technical Report of the Survey of Adults Skills (PIAAC) (Ch. 17).
 Paris: OECD.
                          
-    
     
                          See also
                          
 
-    
                          CDM::IRT.itemfit,
-CDM::IRT.RMSD
                          
-    
+    
                          CDM::IRT.itemfit,
+CDM::IRT.RMSD
                          
 
     
                          Examples
                          
-    
                          # NOT RUN {
+    
                          if (FALSE) {
 #############################################################################
 # EXAMPLE 1: RMSD item fit statistic data.read
 #############################################################################
 
-library(sirt)
-data(data.read,package="sirt")
+library(sirt)
+data(data.read,package="sirt")
 dat <- data.read
 
 #*** fit 1PL model
-mod1 <- TAM::tam.mml( dat )
-summary(mod1)
+mod1 <- TAM::tam.mml( dat )
+summary(mod1)
 
 #*** fit 2PL model
-mod2 <- TAM::tam.mml.2pl( dat )
-summary(mod2)
+mod2 <- TAM::tam.mml.2pl( dat )
+summary(mod2)
 
 #*** assess RMSEA item fit
 fmod1 <- IRT.itemfit(mod1)
 fmod2 <- IRT.itemfit(mod2)
 # summary of fit statistics
-summary( fmod1 )
-summary( fmod2 )
+summary( fmod1 )
+summary( fmod2 )
 
 #############################################################################
 # EXAMPLE 2: Simulated 2PL data and fit of 1PL model
 #############################################################################
 
-set.seed(987)
+set.seed(987)
 N <- 1000    # 1000 persons
 I <- 10      # 10 items
 # define item difficulties and item slopes
-b <- seq(-2,2,len=I)
-a <- rep(1,I)
-a[c(3,8)] <- c( 1.7, .4 )
+b <- seq(-2,2,len=I)
+a <- rep(1,I)
+a[c(3,8)] <- c( 1.7, .4 )
 # simulate 2PL data
-dat <- sirt::sim.raschtype( theta=rnorm(N), b=b, fixed.a=a)
+dat <- sirt::sim.raschtype( theta=rnorm(N), b=b, fixed.a=a)
 
 # fit 1PL model
-mod <- TAM::tam.mml( dat )
+mod <- TAM::tam.mml( dat )
 
 # RMSEA item fit
 fmod <- IRT.itemfit(mod)
-round( fmod, 3 )
-# }
                          
+round( fmod, 3 )
+}
                          
   
   
 
 
+
       
                          
       
                          
   
                          Developed by Alexander Robitzsch.
                          
 
                          
 
 
                          
-  
                          Site built with pkgdown 1.3.0.
                          
+  
                          Site built with pkgdown 1.4.1.
                          
 
                          
+
       
                          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.likelihood.tam.html b/docs/reference/IRT.likelihood.tam.html
index ddc26d5..ac7209c 100644
--- a/docs/reference/IRT.likelihood.tam.html
+++ b/docs/reference/IRT.likelihood.tam.html
@@ -8,21 +8,25 @@
 
 Extracting Individual Likelihood and Individual Posterior — IRT.likelihood • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                            
         
                          • 
   
-    
+    
      
   
 
                            • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                            
@@ -107,43 +113,41 @@ 
                            Extracting Individual Likelihood and Individual Posterior
                            
     
                            
 
     
                            
-    
     
                            Extracts individual likelihood and posterior for models
-fitted in TAM. See CDM::IRT.likelihood
+fitted in TAM. See CDM::IRT.likelihood
 for more details.
                            
-    
     
                            
 
-    
                            # S3 method for tam
-IRT.likelihood(object, …)
-# S3 method for tam
-IRT.posterior(object, …)
-
-# S3 method for tam.mml
-IRT.likelihood(object, …)
-# S3 method for tam.mml
-IRT.posterior(object, …)
-
-# S3 method for tam.mml.3pl
-IRT.likelihood(object, …)
-# S3 method for tam.mml.3pl
-IRT.posterior(object, …)
-
-# S3 method for tamaan
-IRT.likelihood(object, …)
-# S3 method for tamaan
-IRT.posterior(object, …)
-
-# S3 method for tam.latreg
-IRT.likelihood(object, …)
-# S3 method for tam.latreg
-IRT.posterior(object, …)
-
-# S3 method for tam.np
-IRT.likelihood(object, …)
-# S3 method for tam.np
-IRT.posterior(object, …)
                            
-    
+    
                            # S3 method for tam
+IRT.likelihood(object, ...)
+# S3 method for tam
+IRT.posterior(object, ...)
+
+# S3 method for tam.mml
+IRT.likelihood(object, ...)
+# S3 method for tam.mml
+IRT.posterior(object, ...)
+
+# S3 method for tam.mml.3pl
+IRT.likelihood(object, ...)
+# S3 method for tam.mml.3pl
+IRT.posterior(object, ...)
+
+# S3 method for tamaan
+IRT.likelihood(object, ...)
+# S3 method for tamaan
+IRT.posterior(object, ...)
+
+# S3 method for tam.latreg
+IRT.likelihood(object, ...)
+# S3 method for tam.latreg
+IRT.posterior(object, ...)
+
+# S3 method for tam.np
+IRT.likelihood(object, ...)
+# S3 method for tam.np
+IRT.posterior(object, ...)
                            
+
     
                            Arguments
                            
     
@@ -154,57 +158,56 @@ 
                            Arg
 or tam.latreg.
                            
     
     
                            
-      
+      
                            
     
                            …...
       
                            Further arguments to be passed
                            		
     
                            
     
                            
-    
+
     
                            Value
                            
 
-    
                            See CDM::IRT.likelihood.
                            
-    
+    
                            See CDM::IRT.likelihood.
                            
 
     
                            Examples
                            
-    
                            # NOT RUN {
-#############################################################################
+    
                            #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch - extracting likelihood/posterior
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # 1PL estimation
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 lmod1 <- IRT.likelihood(mod1)
-str(lmod1)
+str(lmod1)
 pmod1 <- IRT.posterior(mod1)
-str(pmod1)
-# }
                            
+str(pmod1)
                            
   
   
 
 
+
       
                            
       
                            
   
                            Developed by Alexander Robitzsch.
                            
 
                            
 
 
                            
-  
                            Site built with pkgdown 1.3.0.
                            
+  
                            Site built with pkgdown 1.4.1.
                            
 
                            
+
       
                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.linearCFA.html b/docs/reference/IRT.linearCFA.html
index 64bba63..c7acbdd 100644
--- a/docs/reference/IRT.linearCFA.html
+++ b/docs/reference/IRT.linearCFA.html
@@ -8,21 +8,25 @@
 
 Linear Approximation of a Confirmatory Factor Analysis — IRT.linearCFA • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
                              
         
                            • 
   
-    
+    
      
   
 
                              • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
                              
@@ -109,20 +115,18 @@ 
                              Linear Approximation of a Confirmatory Factor Analysis
                              
     
                              
 
     
                              
-    
     
                              This function approximates a fitted item response model by a linear
 confirmatory factor analysis. I.e., given item response functions, the
 expectation \(E(X_i | \theta_1, \ldots, \theta_D)\) is
 linearly approximated by \(a_{i1} \theta _1 + \ldots + a_{iD} \theta_D\).
 See Vermunt and Magidson (2005) for details.
                              
-    
     
                              
 
-    
                              IRT.linearCFA( object, group=1)
+    
                              IRT.linearCFA( object, group=1)
+
+# S3 method for IRT.linearCFA
+summary(object,  ...)
                              
 
-# S3 method for IRT.linearCFA
-summary(object,  …)
                              
-    
     
                              Arguments
                              
     
@@ -136,11 +140,11 @@ 
                              Arg
       
                              
-      
+      
                              
     
                              Group identifier which defines the selected group.
                              		
     
                              
     
                              …...
       
                              Further arguments to be passed.
                              		
     
                              
     
                              
-    
+
     
                              Value
                              
 
     
                              A list with following entries
                              
@@ -152,7 +156,6 @@ 
                              Value
                              
 
                              M.trait
                              Mean of factors
                              
 
                              SD.trait
                              Standard deviations of factors
                              
 
-    
     
                              References
                              
 
     
                              Vermunt, J. K., & Magidson, J. (2005). Factor Analysis with categorical
@@ -160,105 +163,43 @@ 
                              R
 In A. Van der Ark, M.A. Croon & K. Sijtsma (Eds.),
 New Developments in Categorical Data Analysis for the Social and
 Behavioral Sciences (pp. 41-62). Mahwah: Erlbaum
                              
-    
     
                              See also
                              
 
     
                              See tam.fa for confirmatory factor analysis in TAM.
                              
-    
 
     
                              Examples
                              
-    
                              # NOT RUN {
-library(lavaan)
-
-#############################################################################
-# EXAMPLE 1: Two-dimensional confirmatory factor analysis data.Students
-#############################################################################
-
-data(data.Students, package="CDM")
-# select variables
-vars <- scan(nlines=1, what="character")
-    sc1 sc2 sc3 sc4 mj1 mj2 mj3 mj4
-dat <- data.Students[, vars]
-
-# define Q-matrix
-Q <- matrix( 0, nrow=8, ncol=2 )
-Q[1:4,1] <- Q[5:8,2] <- 1
-
-#*** Model 1: Two-dimensional 2PL model
-mod1 <- TAM::tam.mml.2pl( dat, Q=Q, control=list( nodes=seq(-4,4,len=12) ) )
-summary(mod1)
-
-# linear approximation CFA
-cfa1 <- TAM::IRT.linearCFA(mod1)
-summary(cfa1)
-
-# linear CFA in lavaan package
-lavmodel <- "
-    sc=~ sc1+sc2+sc3+sc4
-    mj=~ mj1+mj2+mj3+mj4
-    sc1 ~ 1
-    sc ~~ mj
-    "
-mod1b <- lavaan::sem( lavmodel, data=dat, missing="fiml", std.lv=TRUE)
-summary(mod1b, standardized=TRUE, fit.measures=TRUE )
-
-#############################################################################
-# EXAMPLE 2: Unidimensional confirmatory factor analysis data.Students
-#############################################################################
-
-data(data.Students, package="CDM")
-# select variables
-vars <- scan(nlines=1, what="character")
-    sc1 sc2 sc3 sc4
-dat <- data.Students[, vars]
-
-#*** Model 1: 2PL model
-mod1 <- TAM::tam.mml.2pl( dat )
-summary(mod1)
-
-# linear approximation CFA
-cfa1 <- TAM::IRT.linearCFA(mod1)
-summary(cfa1)
-
-# linear CFA
-lavmodel <- "
-    sc=~ sc1+sc2+sc3+sc4
-    "
-mod1b <- lavaan::sem( lavmodel, data=dat, missing="fiml", std.lv=TRUE)
-summary(mod1b, standardized=TRUE, fit.measures=TRUE )
-# }
-
                              
+    
                              
   
   
 
 
+
       
                              
       
                              
   
                              Developed by Alexander Robitzsch.
                              
 
                              
 
 
                              
-  
                              Site built with pkgdown 1.3.0.
                              
+  
                              Site built with pkgdown 1.4.1.
                              
 
                              
+
       
                              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.residuals.html b/docs/reference/IRT.residuals.html
index 4681439..3d2682c 100644
--- a/docs/reference/IRT.residuals.html
+++ b/docs/reference/IRT.residuals.html
@@ -8,21 +8,25 @@
 
 Residuals in an IRT Model — IRT.residuals • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
                                
         
                              • 
   
-    
+    
      
   
 
                                • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
                                
@@ -109,13 +115,11 @@ 
                                Residuals in an IRT Model
                                
     
                                
 
     
                                
-    
     
                                Defines an S3 method for the computation of observed residual values.
 The computation of residuals is based on weighted likelihood estimates as
 person parameters, see tam.wle.
 IRT.residuals can only be applied for unidimensional IRT models.
 The methods IRT.residuals and residuals are equivalent.
                                
-    
     
                                
 
     
                                IRT.residuals(object, ...)
@@ -123,23 +127,23 @@ 
                                Residuals in an IRT Model
                                
 # S3 method for tam.mml
 IRT.residuals(object, ...)
 # S3 method for tam.mml
-residuals(object, ...)
+residuals(object, ...)
 
 # S3 method for tam.mml.2pl
 IRT.residuals(object, ...)
 # S3 method for tam.mml.2pl
-residuals(object, ...)
+residuals(object, ...)
 
 # S3 method for tam.mml.mfr
 IRT.residuals(object, ...)
 # S3 method for tam.mml.mfr
-residuals(object, ...)
+residuals(object, ...)
 
 # S3 method for tam.jml
 IRT.residuals(object, ...)
 # S3 method for tam.jml
-residuals(object, ...)
                                
-    
+residuals(object, ...)
    
+
     
    Arguments
    
     
@@ -148,11 +152,11 @@ 
    Arg
       
    
-      
+      
    
     
    Object of class tam.mml, tam.mml.2pl or tam.mml.mfr.
    		
     
    
     
    …...
       
    Further arguments to be passed
    		
     
    
     
    
-    
+
     
    Value
    
 
     
    List with following entries
    
@@ -163,68 +167,63 @@ 
    Value
    
   
    theta
    Used person parameter estimate
    
 
    probs
    Expected item response probabilities
    
 
-    
     
    Note
    
 
     
    Residuals can be used to inspect local dependencies in the item
 response data, for example using principle component analysis or
 factor analysis (see Example 1).
    
-    
     
    See also
    
 
     
    See also the eRm::residuals (eRm) or
-residuals (mirt)
+residuals (mirt)
 functions.
    
 
    See also predict.tam.mml.
    
-    
 
     
    Examples
    
-    
    # NOT RUN {
-#############################################################################
+    
    #############################################################################
 # EXAMPLE 1: Residuals data.read
 #############################################################################
 
-library(sirt)
-data(data.read,package="sirt")
+library(sirt)
+data(data.read,package="sirt")
 dat <- data.read
 
 # for Rasch model
-mod <- TAM::tam.mml( dat )
+mod <- TAM::tam.mml( dat )
 # extract residuals
-res <- TAM::IRT.residuals( mod )
-str(res)
-# }
    
+res <- TAM::IRT.residuals( mod )
+str(res)
    
   
   
 
 
+
       
    
       
    
   
    Developed by Alexander Robitzsch.
    
 
    
 
 
    
-  
    Site built with pkgdown 1.3.0.
    
+  
    Site built with pkgdown 1.4.1.
    
 
    
+
       
    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.simulate.html b/docs/reference/IRT.simulate.html
index a933e2d..45d2517 100644
--- a/docs/reference/IRT.simulate.html
+++ b/docs/reference/IRT.simulate.html
@@ -8,21 +8,25 @@
 
 Simulating Item Response Models — IRT.simulate • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
      
         
    • 
   
-    
+    
      
   
 
      • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
      
@@ -105,25 +111,23 @@ 
      Simulating Item Response Models
      
     
      
 
     
      
-    
     
      Defines an S3 method for simulation of item response models.
      
-    
     
      
 
-    
      IRT.simulate(object, ...)
+    
      IRT.simulate(object, ...)
 
-# S3 method for tam.mml
-IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, …)
+# S3 method for tam.mml
+IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)
 
-# S3 method for tam.mml.2pl
-IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, …)
+# S3 method for tam.mml.2pl
+IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)
 
-# S3 method for tam.mml.mfr
-IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, …)
+# S3 method for tam.mml.mfr
+IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)
+
+# S3 method for tam.mml.3pl
+IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)
      
 
-# S3 method for tam.mml.3pl
-IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, …)
      
-    
     
      Arguments
      
     
@@ -145,149 +149,149 @@ 
      Arg
       
      
-      
+      
      
     
      Optional numeric containing the number of observations to be simulated.
      		
     
      
     
      …...
       
      Further objects to be passed
      		
     
      
     
      
-    
+
     
      Value
      
 
     
      Data frame with simulated item responses
      
-    
 
     
      Examples
      
-    
      # NOT RUN {
-#############################################################################
+    
      #############################################################################
 # EXAMPLE 1: Simulating Rasch model
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 
 #** (1) estimate model
-mod1 <- TAM::tam.mml(resp=data.sim.rasch )
+mod1 <- TAM::tam.mml(resp=data.sim.rasch )
 
 #** (2) simulate data
-sim.dat <- TAM::IRT.simulate(mod1)
+sim.dat <- TAM::IRT.simulate(mod1)
 
-# }# NOT RUN {
+if (FALSE) {
 #** (3) use a subset of items with the argument iIndex
-set.seed(976)
-iIndex <- sort(sample(ncol(data.sim.rasch), 15))  # randomly select 15 items
-sim.dat <- TAM::IRT.simulate(mod1, iIndex=iIndex)
-mod.sim.dat <- TAM::tam.mml(sim.dat)
+set.seed(976)
+iIndex <- sort(sample(ncol(data.sim.rasch), 15))  # randomly select 15 items
+sim.dat <- TAM::IRT.simulate(mod1, iIndex=iIndex)
+mod.sim.dat <- TAM::tam.mml(sim.dat)
 
 #** (4) specify number of persons in addition
-sim.dat <- TAM::IRT.simulate(mod1, nobs=1500, iIndex=iIndex)
+sim.dat <- TAM::IRT.simulate(mod1, nobs=1500, iIndex=iIndex)
 
 # Rasch - constraint="items" ----
-mod1 <- TAM::tam.mml(resp=data.sim.rasch,  constraint="items",
-                control=list( xsi.start0=1, fac.oldxsi=.5) )
+mod1 <- TAM::tam.mml(resp=data.sim.rasch,  constraint="items",
+                control=list( xsi.start0=1, fac.oldxsi=.5) )
 
 # provide abilities
-theta0 <- matrix( rnorm(1500, mean=0.5, sd=sqrt(mod1$variance)), ncol=1 )
+theta0 <- matrix( rnorm(1500, mean=0.5, sd=sqrt(mod1$variance)), ncol=1 )
 # simulate data
-data <- TAM::IRT.simulate(mod1, theta=theta0 )
+data <- TAM::IRT.simulate(mod1, theta=theta0 )
 # estimate model based on simulated data
-xsi.fixed <- cbind(1:nrow(mod1$item), mod1$item$xsi.item)
-mod2 <- TAM::tam.mml(data, xsi.fixed=xsi.fixed )
-summary(mod2)
+xsi.fixed <- cbind(1:nrow(mod1$item), mod1$item$xsi.item)
+mod2 <- TAM::tam.mml(data, xsi.fixed=xsi.fixed )
+summary(mod2)
 
 #############################################################################
 # EXAMPLE 2: Simulating 2PL model
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # estimate 2PL
-mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch, irtmodel="2PL")
+mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch, irtmodel="2PL")
 # simulate 2PL
-sim.dat <- TAM::IRT.simulate(mod2)
-mod.sim.dat <- TAM::tam.mml.2pl(resp=sim.dat, irtmodel="2PL")
+sim.dat <- TAM::IRT.simulate(mod2)
+mod.sim.dat <- TAM::tam.mml.2pl(resp=sim.dat, irtmodel="2PL")
 
 #############################################################################
 # EXAMPLE 3: Simulate multiple group model
 #############################################################################
 
 # Multi-Group ----
-set.seed(6778)
+set.seed(6778)
 N <- 3000
-theta <- c( stats::rnorm(N/2,mean=0,sd=1.5), stats::rnorm(N/2,mean=.5,sd=1)  )
+theta <- c( stats::rnorm(N/2,mean=0,sd=1.5), stats::rnorm(N/2,mean=.5,sd=1)  )
 I <- 20
-p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
-resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
-colnames(resp) <- paste("I", 1:I, sep="")
-group <- rep(1:2, each=N/2 )
-mod3 <- TAM::tam.mml(resp, group=group)
+p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
+resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+colnames(resp) <- paste("I", 1:I, sep="")
+group <- rep(1:2, each=N/2 )
+mod3 <- TAM::tam.mml(resp, group=group)
 
 # simulate data
-sim.dat.g1 <- TAM::IRT.simulate(mod3,
-                   theta=matrix( stats::rnorm(N/2, mean=0, sd=1.5), ncol=1) )
-sim.dat.g2 <- TAM::IRT.simulate(mod3,
-                   theta=matrix( stats::rnorm(N/2, mean=.5, sd=1), ncol=1) )
-sim.dat <- rbind( sim.dat.g1, sim.dat.g2)
+sim.dat.g1 <- TAM::IRT.simulate(mod3,
+                   theta=matrix( stats::rnorm(N/2, mean=0, sd=1.5), ncol=1) )
+sim.dat.g2 <- TAM::IRT.simulate(mod3,
+                   theta=matrix( stats::rnorm(N/2, mean=.5, sd=1), ncol=1) )
+sim.dat <- rbind( sim.dat.g1, sim.dat.g2)
 # estimate model
-mod3s <- TAM::tam.mml( sim.dat, group=group)
+mod3s <- TAM::tam.mml( sim.dat, group=group)
 
 #############################################################################
 # EXAMPLE 4: Multidimensional model and latent regression
 #############################################################################
 
-set.seed(6778)
+set.seed(6778)
 N <- 2000
-Y <- cbind( stats::rnorm(N), stats::rnorm(N))
-theta <- mvtnorm::rmvnorm(N, mean=c(0,0), sigma=matrix(c(1,.5,.5,1), 2, 2))
+Y <- cbind( stats::rnorm(N), stats::rnorm(N))
+theta <- mvtnorm::rmvnorm(N, mean=c(0,0), sigma=matrix(c(1,.5,.5,1), 2, 2))
 theta[,1] <- theta[,1] + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
 theta[,2] <- theta[,2] + .8 * Y[,1] + .5 * Y[,2]  # latent regression model
 I <- 20
-p1 <- stats::plogis(outer(theta[, 1], seq(-2, 2, len=I), "-"))
-resp1 <- 1 * (p1 > matrix(stats::runif(N * I), nrow=N, ncol=I))
-p1 <- stats::plogis(outer(theta[, 2], seq(-2, 2, len=I ), "-" ))
-resp2 <- 1 * (p1 > matrix(stats::runif(N * I), nrow=N, ncol=I))
-resp <- cbind(resp1, resp2)
-colnames(resp) <- paste("I", 1 : (2 * I), sep="")
+p1 <- stats::plogis(outer(theta[, 1], seq(-2, 2, len=I), "-"))
+resp1 <- 1 * (p1 > matrix(stats::runif(N * I), nrow=N, ncol=I))
+p1 <- stats::plogis(outer(theta[, 2], seq(-2, 2, len=I ), "-" ))
+resp2 <- 1 * (p1 > matrix(stats::runif(N * I), nrow=N, ncol=I))
+resp <- cbind(resp1, resp2)
+colnames(resp) <- paste("I", 1 : (2 * I), sep="")
 
 # (2) define loading Matrix
-Q <- array(0, dim=c(2 * I, 2))
-Q[cbind(1:(2*I), c(rep(1, I), rep(2, I)))] <- 1
+Q <- array(0, dim=c(2 * I, 2))
+Q[cbind(1:(2*I), c(rep(1, I), rep(2, I)))] <- 1
 Q
 
 # (3) estimate models
-mod4 <- TAM::tam.mml(resp=resp, Q=Q, Y=Y, control=list(  maxiter=15))
+mod4 <- TAM::tam.mml(resp=resp, Q=Q, Y=Y, control=list(  maxiter=15))
 
 # simulate new item responses
-theta <- mvtnorm::rmvnorm(N, mean=c(0,0), sigma=matrix(c(1,.5,.5,1), 2, 2))
+theta <- mvtnorm::rmvnorm(N, mean=c(0,0), sigma=matrix(c(1,.5,.5,1), 2, 2))
 theta[,1] <- theta[,1] + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
 theta[,2] <- theta[,2] + .8 * Y[,1] + .5 * Y[,2]  # latent regression model
 
-sim.dat <- TAM::IRT.simulate(mod4, theta=theta)
-mod.sim.dat <- TAM::tam.mml(resp=sim.dat, Q=Q, Y=Y, control=list( maxiter=15))
-# }
      
+sim.dat <- TAM::IRT.simulate(mod4, theta=theta)
+mod.sim.dat <- TAM::tam.mml(resp=sim.dat, Q=Q, Y=Y, control=list( maxiter=15))
+}
      
   
   
 
 
+
       
      
       
      
   
      Developed by Alexander Robitzsch.
      
 
      
 
 
      
-  
      Site built with pkgdown 1.3.0.
      
+  
      Site built with pkgdown 1.4.1.
      
 
      
+
       
      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.threshold.html b/docs/reference/IRT.threshold.html
index 658444d..4bbe78d 100644
--- a/docs/reference/IRT.threshold.html
+++ b/docs/reference/IRT.threshold.html
@@ -8,21 +8,25 @@
 
 Thurstonian Thresholds and Wright Map for Item Response Models — IRT.threshold • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -53,6 +58,7 @@
 
 
 
+
   
 
   
@@ -77,7 +83,7 @@
       
-      
       
        
         
      • 
   
-    
+    
      
   
 
        • 
@@ -100,6 +105,7 @@
 
 
       
+
       
 
 
        
@@ -111,27 +117,25 @@ 
        Thurstonian Thresholds and Wright Map for Item Response Models
        
     
        
 
     
        
-    
     
        The function IRT.threshold computes Thurstonian thresholds
 for item response models. It is only based on fitted models
 for which the IRT.irfprob does exist.
        
 
        The function IRT.WrightMap creates a Wright map and works as a wrapper to the
-WrightMap::wrightMap function in
+WrightMap::wrightMap function in
 the WrightMap package. Wright maps operate
 on objects of class IRT.threshold.
        
-    
     
        
 
-    
        IRT.threshold(object, prob.lvl=.5, type="category")
+    
        IRT.threshold(object, prob.lvl=.5, type="category")
 
-# S3 method for IRT.threshold
-print(x, …)
+# S3 method for IRT.threshold
+print(x, ...)
 
-IRT.WrightMap(object, …)
+IRT.WrightMap(object, ...)
+
+# S3 method for IRT.threshold
+IRT.WrightMap(object, label.items=NULL, ...)
        
 
-# S3 method for IRT.threshold
-IRT.WrightMap(object, label.items=NULL, …)
        
-    
     
        Arguments
        
     
@@ -161,128 +165,125 @@ 
        Arg
       
        
-      
+      
        
     
        Vector of item labels
        		
     
        
     
        …...
       
        Further arguments to be passed.
        		
     
        
     
        
-    
+
     
        Value
        
 
     
        Function IRT.threshold:
        
 Matrix with Thurstonian thresholds
        
 
        Function IRT.WrightMap: 
        
 A Wright map generated by the WrightMap package.
        
-    
     
        References
        
 
     
        Ali, U. S., Chang, H.-H., & Anderson, C. J. (2015). Location indices for ordinal
 polytomous items based on item response theory (Research
 Report No. RR-15-20). Princeton, NJ: Educational Testing Service.
        
-    
     
        See also
        
 
-    
        See the WrightMap::wrightMap function in
+    
        See the WrightMap::wrightMap function in
 the WrightMap package.
        
-    
 
     
        Examples
        
-    
        # NOT RUN {
+    
        if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Fitted unidimensional model with gdm
 #############################################################################
 
-data(data.Students)
+data(data.Students)
 dat <- data.Students
 
 # select part of the dataset
-resp <- dat[, paste0("sc",1:4) ]
-resp[ paste(resp[,1])==3,1] <-  2
-psych::describe(resp)
+resp <- dat[, paste0("sc",1:4) ]
+resp[ paste(resp[,1])==3,1] <-  2
+psych::describe(resp)
 
 # Model 1: Partial credit model in gdm
-theta.k <- seq( -5, 5, len=21 )   # discretized ability
-mod1 <- CDM::gdm( dat=resp, irtmodel="1PL", theta.k=theta.k, skillspace="normal",
+theta.k <- seq( -5, 5, len=21 )   # discretized ability
+mod1 <- CDM::gdm( dat=resp, irtmodel="1PL", theta.k=theta.k, skillspace="normal",
               centered.latent=TRUE)
 
 # compute thresholds
-thresh1 <- TAM::IRT.threshold(mod1)
-print(thresh1)
+thresh1 <- TAM::IRT.threshold(mod1)
+print(thresh1)
 IRT.WrightMap(thresh1)
 
 #############################################################################
 # EXAMPLE 2: Fitted mutidimensional model with gdm
 #############################################################################
 
-data( data.fraction2 )
+data( data.fraction2 )
 dat <- data.fraction2$data
 Qmatrix <- data.fraction2$q.matrix3
 
 # Model 1: 3-dimensional Rasch Model (normal distribution)
-theta.k <- seq( -4, 4, len=11 )   # discretized ability
-mod1 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, Qmatrix=Qmatrix,
+theta.k <- seq( -4, 4, len=11 )   # discretized ability
+mod1 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, Qmatrix=Qmatrix,
               centered.latent=TRUE, maxiter=10 )
-summary(mod1)
+summary(mod1)
 
 # compute thresholds
-thresh1 <- TAM::IRT.threshold(mod1)
-print(thresh1)
+thresh1 <- TAM::IRT.threshold(mod1)
+print(thresh1)
 
 #############################################################################
 # EXAMPLE 3: Item-wise thresholds
 #############################################################################
 
-data(data.timssAusTwn.scored)
+data(data.timssAusTwn.scored)
 dat <- data.timssAusTwn.scored
-dat <- dat[, grep("M03", colnames(dat) ) ]
-summary(dat)
+dat <- dat[, grep("M03", colnames(dat) ) ]
+summary(dat)
 
 # fit partial credit model
-mod <- TAM::tam.mml( dat )
+mod <- TAM::tam.mml( dat )
 # compute thresholds with tam.threshold function
-t1mod <- TAM::tam.threshold( mod )
+t1mod <- TAM::tam.threshold( mod )
 t1mod
 # compute thresholds with IRT.threshold function
-t2mod <- TAM::IRT.threshold( mod )
+t2mod <- TAM::IRT.threshold( mod )
 t2mod
 # compute item-wise thresholds
-t3mod <- TAM::IRT.threshold( mod, type="item")
+t3mod <- TAM::IRT.threshold( mod, type="item")
 t3mod
-# }
        
+}
        
   
        
   
 
 
+
       
        
       
        
   
        Developed by Alexander Robitzsch.
        
 
        
 
 
        
-  
        Site built with pkgdown 1.3.0.
        
+  
        Site built with pkgdown 1.4.1.
        
 
        
+
       
        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRT.truescore.html b/docs/reference/IRT.truescore.html
index b8f3e25..4852286 100644
--- a/docs/reference/IRT.truescore.html
+++ b/docs/reference/IRT.truescore.html
@@ -8,21 +8,25 @@
 
 Converts a \(\theta\) Score into a True Score \(\tau ( \theta)\) — IRT.truescore • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -52,6 +57,7 @@
 
 
 
+
   
 
   
@@ -76,7 +82,7 @@
       
-      
       
          
         
        • 
   
-    
+    
      
   
 
          • 
@@ -99,6 +104,7 @@
 
 
       
+
       
 
 
          
@@ -110,25 +116,23 @@ 
          Converts a \(\theta\) Score into a True Score \(\tau ( \theta)\)
          
     
          
 
     
          
-    
     
          Converts a \(\theta\) score into an unweighted true score
 \(\tau ( \theta)=\sum_i \sum_h h P_i ( \theta ) \).
 In addition, a weighted true score
 \(\tau ( \theta)=\sum_i \sum_h q_{ih} P_i ( \theta ) \)
 can also be computed by specifying item-category weights
 \(q_{ih}\) in the matrix Q.
          
-    
     
          
 
     
          IRT.truescore(object, iIndex=NULL, theta=NULL, Q=NULL)
          
-    
+
     
          Arguments
          
     
+CDM::IRT.irfprob S3 method is defined
          
@@ -143,96 +147,94 @@ 
          Arg
       
          
     
     
          
       object
       
          Object for which the
-CDM::IRT.irfprob S3 method is defined
          		
     
          
     
          
       iIndex
          Optional weighting matrix
          		
     
          
     
          
-    
+
     
          Value
          
 
     
          Data frame containing \(\theta\) values and corresponding
 true scores \(\tau( \theta ) \).
          
-    
     
          See also
          
 
-    
          See also sirt::truescore.irt
+    
          See also sirt::truescore.irt
 for a conversion function for generalized partial credit models.
          
-    
 
     
          Examples
          
-    
          # NOT RUN {
-#############################################################################
+    
          #############################################################################
 # EXAMPLE 1: True score conversion for a test with polytomous items
 #############################################################################
 
-data(data.Students, package="CDM")
-dat <- data.Students[, paste0("mj",1:4) ]
+data(data.Students, package="CDM")
+dat <- data.Students[, paste0("mj",1:4) ]
 
 # fit partial credit model
-mod1 <- TAM::tam.mml( dat,control=list(maxiter=20) )
-summary(mod1)
+mod1 <- TAM::tam.mml( dat,control=list(maxiter=20) )
+summary(mod1)
 
 # true score conversion
-tmod1 <- TAM::IRT.truescore( mod1 )
-round( tmod1, 4 )
+tmod1 <- TAM::IRT.truescore( mod1 )
+round( tmod1, 4 )
 # true score conversion with user-defined theta grid
-tmod1b <- TAM::IRT.truescore( mod1, theta=seq( -8,8, len=33 ) )
+tmod1b <- TAM::IRT.truescore( mod1, theta=seq( -8,8, len=33 ) )
 # plot results
-plot( tmod1$theta, tmod1$truescore, type="l",
-            xlab=expression(theta), ylab=expression(tau( theta ) ) )
-points( tmod1b$theta, tmod1b$truescore, pch=16, col="brown" )
+plot( tmod1$theta, tmod1$truescore, type="l",
+            xlab=expression(theta), ylab=expression(tau( theta ) ) )
+points( tmod1b$theta, tmod1b$truescore, pch=16, col="brown" )
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: True scores with different category weightings
 #############################################################################
 
-data(data.timssAusTwn.scored)
+data(data.timssAusTwn.scored)
 dat <- data.timssAusTwn.scored
 # extract item response data
-dat <- dat[, grep("M03", colnames(dat) ) ]
+dat <- dat[, grep("M03", colnames(dat) ) ]
 # select items with do have maximum score of 2 (polytomous items)
-ind <- which( apply( dat,  2, max, na.rm=TRUE )==2 )
-I <- ncol(dat)
+ind <- which( apply( dat,  2, max, na.rm=TRUE )==2 )
+I <- ncol(dat)
 # define Q-matrix with scoring variant
-Q <- matrix( 1, nrow=I, ncol=1 )
+Q <- matrix( 1, nrow=I, ncol=1 )
 Q[ ind, 1 ] <- .5    # score of 0.5 for polyomous items
 
 # estimate model
-mod1 <- TAM::tam.mml( dat, Q=Q, irtmodel="PCM2", control=list( nodes=seq(-10,10,len=61) ) )
-summary(mod1)
+mod1 <- TAM::tam.mml( dat, Q=Q, irtmodel="PCM2", control=list( nodes=seq(-10,10,len=61) ) )
+summary(mod1)
 
 # true score with scoring (0,1,2) which is the default of the function
-tmod1 <- TAM::IRT.truescore(mod1)
+tmod1 <- TAM::IRT.truescore(mod1)
 # true score with user specified weighting matrix
 Q <- mod1$B[,,1]
-tmod2 <- TAM::IRT.truescore(mod1, Q=Q)
-# }
          
+tmod2 <- TAM::IRT.truescore(mod1, Q=Q)
+}
          
   
          
   
 
 
+
       
          
       
          
   
          Developed by Alexander Robitzsch.
          
 
          
 
 
          
-  
          Site built with pkgdown 1.3.0.
          
+  
          Site built with pkgdown 1.4.1.
          
 
          
+
       
          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRTLikelihood.cfa.html b/docs/reference/IRTLikelihood.cfa.html
index 0ac7fe5..3245e34 100644
--- a/docs/reference/IRTLikelihood.cfa.html
+++ b/docs/reference/IRTLikelihood.cfa.html
@@ -8,21 +8,25 @@
 
 Individual Likelihood for Confirmatory Factor Analysis — IRTLikelihood.cfa • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -54,6 +59,7 @@
 
 
 
+
   
 
   
@@ -78,7 +84,7 @@
       
-      
       
            
         
          • 
   
-    
+    
      
   
 
            • 
@@ -101,6 +106,7 @@
 
 
       
+
       
 
 
            
@@ -112,7 +118,6 @@ 
            Individual Likelihood for Confirmatory Factor Analysis
            
     
            
 
     
            
-    
     
            This function computes the individual likelihood evaluated
 at a theta grid for confirmatory factor analysis
 under the normality assumption of residuals. Either
@@ -121,12 +126,11 @@ 
            Individual Likelihood for Confirmatory Factor Analysis
            
 or a fitted cfa object from the lavaan
 package can be provided. The individual likelihood
 can be used for drawing plausible values.
            
-    
     
            
 
     
            IRTLikelihood.cfa(data, cfaobj=NULL, theta=NULL, L=NULL, nu=NULL,
     psi=NULL, snodes=NULL, snodes.adj=2, version=1)
            
-    
+
     
            Arguments
            
     
@@ -136,7 +140,7 @@ 
            Arg
     
     
            
-      
+      
@@ -173,29 +177,27 @@ 
            Arg
     a pure R implementation.
            
     
            
     
            
       cfaobj
            Fitted lavaan::cfa (lavaan) object
            Fitted lavaan::cfa (lavaan) object
            		
     
            
     
            
       theta
            
     
            
-    
+
     
            Value
            
 
     
            Individual likelihood evaluated at theta
            
-    
     
            See also
            
 
-    
            CDM::IRT.likelihood
            
-    
+    
            CDM::IRT.likelihood
            
 
     
            Examples
            
-    
            # NOT RUN {
+    
            if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Two-dimensional CFA data.Students
 #############################################################################
 
-library(lavaan)
-library(CDM)
+library(lavaan)
+library(CDM)
 
-data(data.Students, package="CDM")
+data(data.Students, package="CDM")
 dat <- data.Students
 
-dat2 <- dat[, c(paste0("mj",1:4), paste0("sc",1:4)) ]
+dat2 <- dat[, c(paste0("mj",1:4), paste0("sc",1:4)) ]
 # lavaan model with DO operator
 lavmodel <- "
 DO(1,4,1)
@@ -206,58 +208,59 @@ 
            Examp
    mj ~~ 1*mj
    sc ~~ 1*sc
      "
-lavmodel <- TAM::lavaanify.IRT( lavmodel, data=dat2 )$lavaan.syntax
-cat(lavmodel)
+lavmodel <- TAM::lavaanify.IRT( lavmodel, data=dat2 )$lavaan.syntax
+cat(lavmodel)
 
-mod4 <- lavaan::cfa( lavmodel, data=dat2, std.lv=TRUE )
-summary(mod4, standardized=TRUE, rsquare=TRUE )
+mod4 <- lavaan::cfa( lavmodel, data=dat2, std.lv=TRUE )
+summary(mod4, standardized=TRUE, rsquare=TRUE )
 # extract item parameters
-res4 <- TAM::cfa.extract.itempars( mod4 )
+res4 <- TAM::cfa.extract.itempars( mod4 )
 # create theta grid
-theta0 <- seq( -6, 6, len=15)
-theta <- expand.grid( theta0, theta0 )
+theta0 <- seq( -6, 6, len=15)
+theta <- expand.grid( theta0, theta0 )
 L <- res4$L
 nu <- res4$nu
 psi <- res4$psi
 data <- dat2
 # evaluate likelihood using item parameters
-like2 <- TAM::IRTLikelihood.cfa( data=dat2, theta=theta, L=L, nu=nu, psi=psi )
+like2 <- TAM::IRTLikelihood.cfa( data=dat2, theta=theta, L=L, nu=nu, psi=psi )
 # The likelihood can also be obtained by direct evaluation
 # of the fitted cfa object "mod4"
-like4 <- TAM::IRTLikelihood.cfa( data=dat2, cfaobj=mod4 )
-attr( like4, "theta")
+like4 <- TAM::IRTLikelihood.cfa( data=dat2, cfaobj=mod4 )
+attr( like4, "theta")
 # the theta grid is automatically created if theta is not
 # supplied as an argument
-# }
            
+}
            
   
   
 
 
+
       
            
       
            
   
            Developed by Alexander Robitzsch.
            
 
            
 
 
            
-  
            Site built with pkgdown 1.3.0.
            
+  
            Site built with pkgdown 1.4.1.
            
 
            
+
       
            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/IRTLikelihood.ctt.html b/docs/reference/IRTLikelihood.ctt.html
index 6bcd247..3a6b14f 100644
--- a/docs/reference/IRTLikelihood.ctt.html
+++ b/docs/reference/IRTLikelihood.ctt.html
@@ -8,21 +8,25 @@
 
 Computes Individual Likelihood from Classical Test Theory Estimates — IRTLikelihood.ctt • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
              
         
            • 
   
-    
+    
      
   
 
              • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
              
@@ -106,14 +112,12 @@ 
              Computes Individual Likelihood from Classical Test Theory Estimates
              
     
              
 
     
              
-    
     
              Computes individual likelihood from classical test theory estimates
 under a unidimensional normal distribution of measurement errors.
              
-    
     
              
 
     
              IRTLikelihood.ctt(y, errvar, theta=NULL)
              
-    
+
     
              Arguments
              
     
@@ -130,81 +134,81 @@ 
              Arg
       
              
     
              Optional vector for \(\theta\) grid.
              		
     
              
     
              
-    
+
     
              Value
              
 
     
              Object of class IRT.likelihood
              
-    
 
     
              Examples
              
-    
              # NOT RUN {
-#############################################################################
+    
              #############################################################################
 # EXAMPLE 1: Individual likelihood and latent regression in CTT
 #############################################################################
 
-set.seed(75)
+set.seed(75)
 
 #--- simulate data
 N <- 2000
-x1 <- stats::rnorm(N)
-x2 <- .7 * x1 + stats::runif(N)
+x1 <- stats::rnorm(N)
+x2 <- .7 * x1 + stats::runif(N)
 # simulate true score
-theta <- 1.2 + .6*x1 + .3 *x2 + stats::rnorm(N, sd=sqrt(.50) )
-var(theta)
+theta <- 1.2 + .6*x1 + .3 *x2 + stats::rnorm(N, sd=sqrt(.50) )
+var(theta)
 # simulate measurement error variances
-errvar <- stats::runif( N, min=.6, max=.9 )
+errvar <- stats::runif( N, min=.6, max=.9 )
 # simulate observed scores
-y <- theta + stats::rnorm( N, sd=sqrt( errvar) )
+y <- theta + stats::rnorm( N, sd=sqrt( errvar) )
 
 #--- create likelihood object
-like1 <- TAM::IRTLikelihood.ctt( y=y, errvar=errvar, theta=NULL )
+like1 <- TAM::IRTLikelihood.ctt( y=y, errvar=errvar, theta=NULL )
 
 #--- estimate latent regression
-X <- data.frame(x1,x2)
-mod1 <- TAM::tam.latreg( like=like1, Y=X )
+X <- data.frame(x1,x2)
+mod1 <- TAM::tam.latreg( like=like1, Y=X )
 
-# }# NOT RUN {
+if (FALSE) {
 #--- draw plausible values
-pv1 <- TAM::tam.pv( mod1, normal.approx=TRUE )
+pv1 <- TAM::tam.pv( mod1, normal.approx=TRUE )
 
 #--- create datalist
-datlist1 <- TAM::tampv2datalist( pv1, pvnames="thetaPV", Y=X )
+datlist1 <- TAM::tampv2datalist( pv1, pvnames="thetaPV", Y=X )
 
 #--- statistical inference on plausible values using mitools package
-library(mitools)
-datlist1a <- mitools::imputationList(datlist1)
+library(mitools)
+datlist1a <- mitools::imputationList(datlist1)
 
 # fit linear regression and apply Rubin formulas
-mod2 <- with( datlist1a, stats::lm( thetaPV ~ x1 + x2 ) )
-summary( mitools::MIcombine(mod2) )
-# }
              
+mod2 <- with( datlist1a, stats::lm( thetaPV ~ x1 + x2 ) )
+summary( mitools::MIcombine(mod2) )
+}
              
   
   
 
 
+
       
              
       
              
   
              Developed by Alexander Robitzsch.
              
 
              
 
 
              
-  
              Site built with pkgdown 1.3.0.
              
+  
              Site built with pkgdown 1.4.1.
              
 
              
+
       
              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/Scale.html b/docs/reference/Scale.html
index 3399784..f8ab7d2 100644
--- a/docs/reference/Scale.html
+++ b/docs/reference/Scale.html
@@ -8,21 +8,25 @@
 
 S3 Method for Standardizations and Transformations of Variables — Scale • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                
         
              • 
   
-    
+    
      
   
 
                • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                
@@ -105,13 +111,11 @@ 
                S3 Method for Standardizations and Transformations of Variables
                
     
                
 
     
                
-    
     
                S3 method for standardizations and transformations of variables
                
-    
     
                
 
     
                Scale(object, ...)
                
-    
+
     
                Arguments
                
     
@@ -120,41 +124,43 @@ 
                Arg
       
                
-      
+      
                
     
                An object
                		
     
                
     
                …...
       
                Further arguments to be passed
                		
     
                
     
                
-    
+
     
                See also
                
 
-    
                base::scale
                
-    
+    
                base::scale
                
 
   
   
              
 
   
 
 
+
       
              
       
              
   
              Developed by Alexander Robitzsch.
              
 
              
 
 
              
-  
              Site built with pkgdown 1.3.0.
              
+  
              Site built with pkgdown 1.4.1.
              
 
              
+
       
              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/TAM-defunct.html b/docs/reference/TAM-defunct.html
index 6c6b99d..5a7a1e5 100644
--- a/docs/reference/TAM-defunct.html
+++ b/docs/reference/TAM-defunct.html
@@ -8,21 +8,25 @@
 
 Defunct <span class="pkg">TAM</span> Functions — TAM-defunct • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-TAM Functions — TAM-defunct" />
 
+TAM Functions — TAM-defunct" />
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                
         
              • 
   
-    
+    
      
   
 
                • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                
@@ -106,54 +112,54 @@ 
                Defunct TAM Functions
                
     
                
 
     
                
-    
     
                These functions have been removed or replaced in the tam.jml2
 package.
                
-    
     
                
 
     
                tam.jml2(...)
                
-    
+
     
                Arguments
                
     
-      
+      
                
     
     
                …...
       
                Arguments to be passed.
                		
     
                
     
                
-    
+
     
                Details
                
 
     
                The tam.jml2 is included as the default in
 tam.jml.
                
-    
 
   
   
              
 
   
 
 
+
       
              
       
              
   
              Developed by Alexander Robitzsch.
              
 
              
 
 
              
-  
              Site built with pkgdown 1.3.0.
              
+  
              Site built with pkgdown 1.4.1.
              
 
              
+
       
              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/TAM-package.html b/docs/reference/TAM-package.html
index 4296fd2..8ef3183 100644
--- a/docs/reference/TAM-package.html
+++ b/docs/reference/TAM-package.html
@@ -8,21 +8,25 @@
 
 Test Analysis Modules — TAM-package • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -58,6 +63,7 @@
 
 
 
+
   
 
   
@@ -82,7 +88,7 @@
       
-      
       
                
         
              • 
   
-    
+    
      
   
 
                • 
@@ -105,6 +110,7 @@
 
 
       
+
       
 
 
                
@@ -116,7 +122,6 @@ 
                Test Analysis Modules
                
     
                
 
     
                
-    
     
                Includes marginal maximum likelihood estimation and joint maximum
     likelihood estimation for unidimensional and multidimensional 
     item response models. The package functionality covers the 
@@ -129,62 +134,66 @@ 
                Test Analysis Modules
                
     Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
     Formann, 1982 <doi:10.1002/bimj.4710240209>,
     Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
                
-    
     
                
 
-        
+
+
     
                Details
                
 
     
                See http://www.edmeasurementsurveys.com/TAM/Tutorials/ for
 tutorials of the TAM package.
                
-    
     
                References
                
 
     
                Adams, R. J., Wilson, M., & Wang, W. C. (1997). The multidimensional random coefficients
-multinomial logit model. Applied Psychological Measurement, 21(1), 1-23.
                
+multinomial logit model. Applied Psychological Measurement, 21(1), 1-23.
+doi: 10.1177/0146621697211001
                
 
                Adams, R. J., Wilson, M., & Wu, M. (1997).
 Multilevel item response models: An approach to errors in
 variables regression. Journal of Educational and Behavioral
-Statistics, 22, 47-76.
                
+Statistics, 22(1), 47-76. doi: 10.3102/10769986022001047
                
 
                Adams, R. J., & Wu, M. L. (2007). The mixed-coefficients multinomial logit model.
 A generalized form of the Rasch model. In M. von Davier & C. H. Carstensen (Eds.):
 Multivariate and mixture distribution Rasch models: Extensions and applications
-(pp. 55-76). New York: Springer.
                
+(pp. 55-76). New York: Springer.
+doi: 10.1007/978-0-387-49839-3_4
                
 
                Formann, A. K. (1982). Linear logistic latent class analysis.
-Biometrical Journal, 24(2), 171-190.
                
+Biometrical Journal, 24(2), 171-190.
+doi: 10.1002/bimj.4710240209
                
 
                Formann, A. K. (1992). Linear logistic latent class analysis for polytomous data.
-Journal of the American Statistical Association, 87, 476-486.
                
-    
+Journal of the American Statistical Association, 87(418), 476-486.
+doi: 10.1080/01621459.1992.10475229
                
 
   
   
              
 
     
              Author
              
     
              Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Margaret Wu [aut]
              
-
              Maintainer: Alexander Robitzsch <robitzsch@leibniz-ipn.de>
              
+
              Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>
              
   
 
 
+
       
              
       
              
   
              Developed by Alexander Robitzsch.
              
 
              
 
 
              
-  
              Site built with pkgdown 1.3.0.
              
+  
              Site built with pkgdown 1.4.1.
              
 
              
+
       
              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/TAM-utilities.html b/docs/reference/TAM-utilities.html
index 1a5863a..94d37e7 100644
--- a/docs/reference/TAM-utilities.html
+++ b/docs/reference/TAM-utilities.html
@@ -8,21 +8,25 @@
 
 Utility Functions in <span class="pkg">TAM</span> — TAM-utilities • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-TAM — TAM-utilities" />
 
+TAM — TAM-utilities" />
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                
         
              • 
   
-    
+    
      
   
 
                • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                
@@ -105,9 +111,7 @@ 
                Utility Functions in TAM
                
     
                
 
     
                
-    
     
                Utility functions in TAM.
                
-    
     
                
 
     
                ## RISE item fit statistic of two models
@@ -124,11 +128,11 @@ 
                Utility Functions in TAM
                
 ## requireNamespace with message of needed installation
 require_namespace_msg(pkg)
 ## add leading zeroes
-add.lead(x, width=max(nchar(x)))
+add.lead(x, width=max(nchar(x)))
 ## round some columns in a data frame
-tam_round_data_frame(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
+tam_round_data_frame(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
 ## round some columns in a data frame and print this data frame
-tam_round_data_frame_print(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
+tam_round_data_frame_print(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
 ## copy of CDM::osink
 tam_osink(file, suffix="__SUMMARY.Rout")
 ## copy of CDM::csink
@@ -179,7 +183,7 @@ 
                Utility Functions in TAM
                
 tam_difference_quotient(d0, d0p, d0m, h)
 ## assign elements of a list in an environment
 tam_assign_list_elements(x, envir)
                
-    
+
     
                Arguments
                
     
@@ -205,7 +209,7 @@ 
                Arg
     
     
                
-      
+      
@@ -269,7 +273,7 @@ 
                Arg
     
                
-      
+      
@@ -315,7 +319,7 @@ 
                Arg
     
                
-      
+      
@@ -410,31 +414,35 @@ 
                Arg
       
                
     
                
       args_CALL
                Arguments obtained from as.list( sys.call() )
                Arguments obtained from as.list( sys.call() )
                		
     
                
     
                
       variable
                
     
                
       op
                An operation "*", "+" or "-"
                An operation "*", "+" or "-"
                		
     
                
     
                
       group
                
     
                
       method
                Method, see stats::cov.wt
                Method, see stats::cov.wt
                		
     
                
     
                
       rn
                Element of a list
                		
     
                
     
                
-    
+
 
   
   
              
 
   
 
 
+
       
              
       
              
   
              Developed by Alexander Robitzsch.
              
 
              
 
 
              
-  
              Site built with pkgdown 1.3.0.
              
+  
              Site built with pkgdown 1.4.1.
              
 
              
+
       
              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/WLErel.html b/docs/reference/WLErel.html
index e543c60..ca2060a 100644
--- a/docs/reference/WLErel.html
+++ b/docs/reference/WLErel.html
@@ -8,21 +8,25 @@
 
 Reliability Estimation in <span class="pkg">TAM</span> — WLErel • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-TAM — WLErel" />
 
+TAM — WLErel" />
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                
         
              • 
   
-    
+    
      
   
 
                • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                
@@ -105,15 +111,13 @@ 
                Reliability Estimation in TAM
                
     
                
 
     
                
-    
     
                Functions for computing reliability estimates.
                
-    
     
                
 
-    
                WLErel(theta, error, w=rep(1, length(theta)), select=NULL)
+    
                WLErel(theta, error, w=rep(1, length(theta)), select=NULL)
+
+EAPrel(theta, error, w=rep(1, length(theta)), select=NULL)
                
 
-EAPrel(theta, error, w=rep(1, length(theta)), select=NULL)
                
-    
     
                Arguments
                
     
@@ -134,7 +138,7 @@ 
                Arg
       
                
     
                Optional vector for selecting cases
                		
     
                
     
                
-    
+
     
                Details
                
 
     
                The reliability formulas follow Adams (2005). Let \(v\) denote
@@ -142,64 +146,59 @@ 
                Details
 the average of the squared error. Then, the WLE reliability is
 defined as \(1-s/v=(v-s)/v\) while the EAP reliability is defined as
 \(1 - s/(s+v)=v/(s+v)\).
                
-    
     
                Value
                
 
     
                Numeric value
                
-    
     
                References
                
 
     
                Adams, R. J. (2005). Reliability as a measurement design effect.
 Studies in Educational Evaluation, 31(2), 162-172.
                
-    
 
     
                Examples
                
-    
                # NOT RUN {
-#############################################################################
+    
                #############################################################################
 # EXAMPLE 1: Toy example for reliability functions
 #############################################################################
 
-set.seed(9897)
+set.seed(9897)
 N <- 100
 # simulate theta and error SDs
-x <- stats::rnorm(N,sd=2)
-error <- stats::runif(N, .7, 1.3)
+x <- stats::rnorm(N,sd=2)
+error <- stats::runif(N, .7, 1.3)
 # compute WLE reliability
 WLErel(x,error)
 # compute EAP reliaility
-EAPrel(x,error)
-# }
                
+EAPrel(x,error)
                
   
   
 
 
+
       
                
       
                
   
                Developed by Alexander Robitzsch.
                
 
                
 
 
                
-  
                Site built with pkgdown 1.3.0.
                
+  
                Site built with pkgdown 1.4.1.
                
 
                
+
       
                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/anova.tam.html b/docs/reference/anova.tam.html
index 4be23c4..1b400da 100644
--- a/docs/reference/anova.tam.html
+++ b/docs/reference/anova.tam.html
@@ -8,21 +8,25 @@
 
 Likelihood Ratio Test for Model Comparisons and Log-Likelihood Value — anova-logLik • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -52,6 +57,7 @@
 
 
 
+
   
 
   
@@ -76,7 +82,7 @@
       
-      
       
                  
         
                • 
   
-    
+    
      
   
 
                  • 
@@ -99,6 +104,7 @@
 
 
       
+
       
 
 
                  
@@ -110,46 +116,44 @@ 
                  Likelihood Ratio Test for Model Comparisons and Log-Likelihood Value
                  
     
                  
 
     
                  
-    
     
                  The anova function compares two models estimated of class tam,
 tam.mml or tam.mml.3pl using a likelihood ratio test.
 The logLik function extracts the value of the log-Likelihood.
                  
 
                  The function can be applied for values of tam.mml,
 tam.mml.2pl, tam.mml.mfr, tam.fa,
 tam.mml.3pl, tam.latreg or tamaan.
                  
-    
     
                  
 
-    
                  # S3 method for tam
-anova(object, …)
-# S3 method for tam
-logLik(object, …)
-
-# S3 method for tam.mml
-anova(object, …)
-# S3 method for tam.mml
-logLik(object, …)
-
-# S3 method for tam.mml.3pl
-anova(object, …)
-# S3 method for tam.mml.3pl
-logLik(object, …)
-
-# S3 method for tamaan
-anova(object, …)
-# S3 method for tamaan
-logLik(object, …)
-
-# S3 method for tam.latreg
-anova(object, …)
-# S3 method for tam.latreg
-logLik(object, …)
-
-# S3 method for tam.np
-anova(object, …)
-# S3 method for tam.np
-logLik(object, …)
                  
-    
+    
                  # S3 method for tam
+anova(object, ...)
+# S3 method for tam
+logLik(object, ...)
+
+# S3 method for tam.mml
+anova(object, ...)
+# S3 method for tam.mml
+logLik(object, ...)
+
+# S3 method for tam.mml.3pl
+anova(object, ...)
+# S3 method for tam.mml.3pl
+logLik(object, ...)
+
+# S3 method for tamaan
+anova(object, ...)
+# S3 method for tamaan
+logLik(object, ...)
+
+# S3 method for tam.latreg
+anova(object, ...)
+# S3 method for tam.latreg
+logLik(object, ...)
+
+# S3 method for tam.np
+anova(object, ...)
+# S3 method for tam.np
+logLik(object, ...)
                  
+
     
                  Arguments
                  
     
@@ -161,81 +165,81 @@ 
                  Arg
 (fitted models) must be provided.
                  
     
     
                  
-      
+      
                  
     
                  …...
       
                  Further arguments to be passed
                  		
     
                  
     
                  
-    
+
     
                  Value
                  
 
     
                  A data frame containing the likelihood ratio test statistic and
 information criteria.
                  
-    
 
     
                  Examples
                  
-    
                  # NOT RUN {
-#############################################################################
+    
                  #############################################################################
 # EXAMPLE 1: Dichotomous data sim.rasch - 1PL vs. 2PL model
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # 1PL estimation
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
-logLik(mod1)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+logLik(mod1)
 # 2PL estimation
-mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch, irtmodel="2PL")
-logLik(mod2)
+mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch, irtmodel="2PL")
+logLik(mod2)
 # Model comparison
-anova( mod1, mod2 )
+anova( mod1, mod2 )
   ##     Model   loglike Deviance Npars      AIC      BIC    Chisq df       p
   ##   1  mod1 -42077.88 84155.77    41 84278.77 84467.40 54.05078 39 0.05508
   ##   2  mod2 -42050.86 84101.72    80 84341.72 84709.79       NA NA      NA
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: Dataset reading (sirt package): 1- vs. 2-dimensional model
 #############################################################################
 
-data(data.read,package="sirt")
+data(data.read,package="sirt")
 
 # 1-dimensional model
-mod1 <- TAM::tam.mml.2pl(resp=data.read )
+mod1 <- TAM::tam.mml.2pl(resp=data.read )
 # 2-dimensional model
-mod2 <- TAM::tam.fa(resp=data.read, irtmodel="efa", nfactors=2,
-             control=list(maxiter=150) )
+mod2 <- TAM::tam.fa(resp=data.read, irtmodel="efa", nfactors=2,
+             control=list(maxiter=150) )
 # Model comparison
-anova( mod1, mod2 )
+anova( mod1, mod2 )
   ##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
   ##   1    mod1 -1954.888 3909.777    24 3957.777 4048.809 76.66491 11  0
   ##   2    mod2 -1916.556 3833.112    35 3903.112 4035.867       NA NA NA
-# }
                  
+}
                  
   
   
 
 
+
       
                  
       
                  
   
                  Developed by Alexander Robitzsch.
                  
 
                  
 
 
                  
-  
                  Site built with pkgdown 1.3.0.
                  
+  
                  Site built with pkgdown 1.4.1.
                  
 
                  
+
       
                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/cfa.extract.itempars.html b/docs/reference/cfa.extract.itempars.html
index f05b25c..858b73c 100644
--- a/docs/reference/cfa.extract.itempars.html
+++ b/docs/reference/cfa.extract.itempars.html
@@ -8,21 +8,25 @@
 
 Extracting Item Parameters from a Fitted <code>cfa</code> Object in <span class="pkg">lavaan</span> — cfa.extract.itempars • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-lavaan — cfa.extract.itempars" />
 
+lavaan — cfa.extract.itempars" />
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
                    
         
                  • 
   
-    
+    
      
   
 
                    • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
                    
@@ -109,17 +115,15 @@ 
                    Extracting Item Parameters from a Fitted cfa Object in 
 
     
                    
-    
     
                    This function extract item parameters from a fitted
-lavaan::cfa object in lavaan. It
+lavaan::cfa object in lavaan. It
 extract item loadings, item intercepts and the mean
 and covariance matrix of latent variables in a
 confirmatory factor analysis model.
                    
-    
     
                    
 
     
                    cfa.extract.itempars(object)
                    
-    
+
     
                    Arguments
                    
     
@@ -128,7 +132,7 @@ 
                    Arg
       
                    
     
                    Fitted cfa object
                    		
     
                    
     
                    
-    
+
     
                    Value
                    
 
     
                    List with following entries
                    
@@ -137,51 +141,48 @@ 
                    Value
                    
 
                    psi
                    Residual covariance matrix
                    
 
                    Sigma
                    Covariance matrix of latent variables
                    
 
                    nu
                    Vector of means of latent variables
                    
-
                    …
                    Further values
                    
+
                    ...
                    Further values
                    
 
-    
     
                    See also
                    
 
     
                    See IRTLikelihood.cfa for extracting the
 individual likelihood from fitted confirmatory
 factor analyses.
                    
-
                    lavaan::cfa
                    
-    
+
                    lavaan::cfa
                    
 
     
                    Examples
                    
-    
                    # NOT RUN {
-#############################################################################
+    
                    #############################################################################
 # EXAMPLE 1: CFA data.Students
 #############################################################################
 
-library(lavaan)
-library(CDM)
+library(lavaan)
+library(CDM)
 
-data(data.Students, package="CDM")
+data(data.Students, package="CDM")
 dat <- data.Students
 
-dat1 <- dat[, paste0( "mj", 1:4 ) ]
+dat1 <- dat[, paste0( "mj", 1:4 ) ]
 
 #*** Model 1: Unidimensional model scale mj
 lavmodel <- "
    mj=~ mj1 + mj2 + mj3 + mj4
    mj ~~ mj
      "
-mod1 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
-summary(mod1, standardized=TRUE, rsquare=TRUE )
+mod1 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
+summary(mod1, standardized=TRUE, rsquare=TRUE )
 # extract parameters
-res1 <- TAM::cfa.extract.itempars( mod1 )
+res1 <- TAM::cfa.extract.itempars( mod1 )
 
-# }# NOT RUN {
+if (FALSE) {
 #*** Model 2: Scale mj - explicit modelling of item intercepts
 lavmodel <- "
    mj=~ mj1 + mj2 + mj3 + mj4
    mj ~~ mj
    mj1 ~ 1
      "
-mod2 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
-summary(mod2, standardized=TRUE, rsquare=TRUE )
-res2 <- TAM::cfa.extract.itempars( mod2 )
+mod2 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
+summary(mod2, standardized=TRUE, rsquare=TRUE )
+res2 <- TAM::cfa.extract.itempars( mod2 )
 
 #*** Model 3: Tau-parallel measurements scale mj
 lavmodel <- "
@@ -192,12 +193,12 @@ 
                    Examp
    mj3 ~ b*1
    mj4 ~ b*1
      "
-mod3 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
-summary(mod3, standardized=TRUE, rsquare=TRUE )
-res3 <- TAM::cfa.extract.itempars( mod3 )
+mod3 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
+summary(mod3, standardized=TRUE, rsquare=TRUE )
+res3 <- TAM::cfa.extract.itempars( mod3 )
 
 #*** Model 4: Two-dimensional CFA with scales mj and sc
-dat2 <- dat[, c(paste0("mj",1:4), paste0("sc",1:4)) ]
+dat2 <- dat[, c(paste0("mj",1:4), paste0("sc",1:4)) ]
 # lavaan model with shortage "__" operator
 lavmodel <- "
    mj=~ mj1__mj4
@@ -206,41 +207,42 @@ 
                    Examp
    mj ~~ 1*mj
    sc ~~ 1*sc
      "
-lavmodel <- TAM::lavaanify.IRT( lavmodel, data=dat2 )$lavaan.syntax
-cat(lavmodel)
-mod4 <- lavaan::cfa( lavmodel, data=dat2, std.lv=TRUE )
-summary(mod4, standardized=TRUE, rsquare=TRUE )
-res4 <- TAM::cfa.extract.itempars( mod4 )
-# }
                    
+lavmodel <- TAM::lavaanify.IRT( lavmodel, data=dat2 )$lavaan.syntax
+cat(lavmodel)
+mod4 <- lavaan::cfa( lavmodel, data=dat2, std.lv=TRUE )
+summary(mod4, standardized=TRUE, rsquare=TRUE )
+res4 <- TAM::cfa.extract.itempars( mod4 )
+}
                    
   
   
 
 
+
       
                    
       
                    
   
                    Developed by Alexander Robitzsch.
                    
 
                    
 
 
                    
-  
                    Site built with pkgdown 1.3.0.
                    
+  
                    Site built with pkgdown 1.4.1.
                    
 
                    
+
       
                    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.cqc.html b/docs/reference/data.cqc.html
index ee92ee8..1452c48 100644
--- a/docs/reference/data.cqc.html
+++ b/docs/reference/data.cqc.html
@@ -8,21 +8,25 @@
 
 More Datasets and Examples (Similar to ConQuest Examples) — data.cqc • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                      
         
                    • 
   
-    
+    
      
   
 
                      • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                      
@@ -106,22 +112,22 @@ 
                      More Datasets and Examples (Similar to ConQuest Examples)
                      
     
                      
 
     
                      
-    
     
                      Datasets and examples similar to the ones in the ConQuest manual
 (Wu, Adams, Wilson, & Haldane, 2007).
                      
-    
     
                      
 
-    
                      data(data.cqc01)
-data(data.cqc02)
-data(data.cqc03)
-data(data.cqc04)
-data(data.cqc05)
                      
-        
+    
                      data(data.cqc01)
+data(data.cqc02)
+data(data.cqc03)
+data(data.cqc04)
+data(data.cqc05)
                      
+
+
     
                      Format
                      
 
     
-    
                        
+
+
                          
 
                        • data.cqc01 contains 512 persons on
 12 dichotomous items of following format
                          
 
                          'data.frame':   512 obs. of  12 variables: 
                          
@@ -168,149 +174,148 @@ 
                          Format $ A003  : num  0 0 0 0 1 1 1 0 0 1 ... 
                          
 [...] 
                          • 
 
                        
-    
+
     
                        See also
                        
 
-    
                        See the sirt::R2conquest function
+    
                        See the sirt::R2conquest function
 for running ConQuest software from within R.
                        
-
                        See the WrightMap package for functions
+
                        See the WrightMap package for functions
 connected to reading ConQuest files and creating Wright maps.
 ConQuest output files can be read into R with the help of
-the WrightMap::CQmodel function.
+the WrightMap::CQmodel function.
 See also the IRT.WrightMap function in TAM.
                        
 
                        See also the eat package (https://r-forge.r-project.org/projects/eat/)
 for elaborate functionality for communication of ConQuest with R.
                        
-    
     
                        References
                        
 
     
                        Wu, M. L., Adams, R. J., Wilson, M. R. & Haldane, S. (2007).
 ACER ConQuest Version 2.0. Mulgrave.
 https://shop.acer.edu.au/acer-shop/group/CON3.
                        
-    
 
     
                        Examples
                        
-    
                        # NOT RUN {
-library(sirt)
-library(WrightMap)
+    
                        if (FALSE) {
+
+library(sirt)
+library(WrightMap)
 # In the following, ConQuest will also be used for estimation.
 path.conquest <- "C:/Conquest"             # path of the ConQuest console.exe
-setwd( "p:/my_files/ConQuest_analyses" )  # working directory
+setwd( "p:/my_files/ConQuest_analyses" )  # working directory
 
 #############################################################################
 # EXAMPLE 01: Rasch model data.cqc01
 #############################################################################
 
-data(data.cqc01)
+data(data.cqc01)
 dat <- data.cqc01
 
 #********************************************
 #*** Model 01: Estimate Rasch model
-mod01 <- TAM::tam.mml(dat)
-summary(mod01)
+mod01 <- TAM::tam.mml(dat)
+summary(mod01)
 
 #------- ConQuest
 
 # estimate model
-cmod01 <- sirt::R2conquest( dat, name="mod01", path.conquest=path.conquest)
-summary(cmod01)   # summary output
+cmod01 <- sirt::R2conquest( dat, name="mod01", path.conquest=path.conquest)
+summary(cmod01)   # summary output
 # read shw file with some terms
-shw01a <- sirt::read.show( "mod01.shw" )
+shw01a <- sirt::read.show( "mod01.shw" )
 cmod01$shw.itemparameter
 # read person item maps
-pi01a <- sirt::read.pimap( "mod01.shw" )
+pi01a <- sirt::read.pimap( "mod01.shw" )
 cmod01$shw.pimap
 # read plausible values (npv=10 plausible values)
-pv01a <- sirt::read.pv(pvfile="mod01.pv", npv=10)
+pv01a <- sirt::read.pv(pvfile="mod01.pv", npv=10)
 cmod01$person
 
 # read ConQuest model
-res01a <- WrightMap::CQmodel(p.est="mod01.wle", show="mod01.shw", p.type="WLE" )
-print(res01a)
+res01a <- WrightMap::CQmodel(p.est="mod01.wle", show="mod01.shw", p.type="WLE" )
+print(res01a)
 # plot item fit
-WrightMap::fitgraph(res01a)
+WrightMap::fitgraph(res01a)
 # Wright map
-plot(res01a, label.items.srt=90 )
+plot(res01a, label.items.srt=90 )
 
 #############################################################################
 # EXAMPLE 02: Partial credit model and rating scale model data.cqc02
 #############################################################################
 
-data(data.cqc02)
+data(data.cqc02)
 dat <- data.cqc02
 
 #********************************************
 # Model 02a: Partial credit model in ConQuest parametrization 'item+item*step'
-mod02a <- TAM::tam.mml( dat, irtmodel="PCM2" )
-summary(mod02a, file="mod02a")
-fit02a <- TAM::tam.fit(mod02a)
-summary(fit02a)
+mod02a <- TAM::tam.mml( dat, irtmodel="PCM2" )
+summary(mod02a, file="mod02a")
+fit02a <- TAM::tam.fit(mod02a)
+summary(fit02a)
 
 #--- ConQuest
 # estimate model
-maxK <- max( dat, na.rm=TRUE )
-cmod02a <- sirt::R2conquest( dat, itemcodes=0:maxK, model="item+item*step",
+maxK <- max( dat, na.rm=TRUE )
+cmod02a <- sirt::R2conquest( dat, itemcodes=0:maxK, model="item+item*step",
                name="mod02a", path.conquest=path.conquest)
-summary(cmod02a)   # summary output
+summary(cmod02a)   # summary output
 
 # read ConQuest model
-res02a <- WrightMap::CQmodel(p.est="mod02a.wle", show="mod02a.shw", p.type="WLE" )
-print(res02a)
+res02a <- WrightMap::CQmodel(p.est="mod02a.wle", show="mod02a.shw", p.type="WLE" )
+print(res02a)
 # Wright map
-plot(res02a, label.items.srt=90 )
-plot(res02a, item.table="item")
+plot(res02a, label.items.srt=90 )
+plot(res02a, item.table="item")
 
 #********************************************
 # Model 02b: Rating scale model
-mod02b <- TAM::tam.mml( dat, irtmodel="RSM" )
-summary( mod02b )
+mod02b <- TAM::tam.mml( dat, irtmodel="RSM" )
+summary( mod02b )
 
 #############################################################################
 # EXAMPLE 03: Faceted Rasch model for rating data data.cqc03
 #############################################################################
 
-data(data.cqc03)
+data(data.cqc03)
 # select items
-resp <- data.cqc03[, c("crit1","crit2") ]
+resp <- data.cqc03[, c("crit1","crit2") ]
 
 #********************************************
 # Model 03a: 'item+step+rater'
-mod03a <- TAM::tam.mml.mfr( resp, facets=data.cqc03[,"rater",drop=FALSE],
+mod03a <- TAM::tam.mml.mfr( resp, facets=data.cqc03[,"rater",drop=FALSE],
             formulaA=~ item+step+rater, pid=data.cqc03$pid )
-summary( mod03a )
+summary( mod03a )
 
 #--- ConQuest
 X <- data.cqc03[,"rater",drop=FALSE]
-X$rater <- as.numeric(substring( X$rater, 2 )) # convert 'rater' in numeric format
-maxK <- max( resp, na.rm=TRUE)
-cmod03a <- sirt::R2conquest( resp,  X=X, regression="",  model="item+step+rater",
+X$rater <- as.numeric(substring( X$rater, 2 )) # convert 'rater' in numeric format
+maxK <- max( resp, na.rm=TRUE)
+cmod03a <- sirt::R2conquest( resp,  X=X, regression="",  model="item+step+rater",
              name="mod03a", path.conquest=path.conquest, set.constraints="cases" )
-summary(cmod03a)   # summary output
+summary(cmod03a)   # summary output
 
 # read ConQuest model
-res03a <- WrightMap::CQmodel(p.est="mod03a.wle", show="mod03a.shw", p.type="WLE" )
-print(res03a)
+res03a <- WrightMap::CQmodel(p.est="mod03a.wle", show="mod03a.shw", p.type="WLE" )
+print(res03a)
 # Wright map
-plot(res03a)
+plot(res03a)
 
 #********************************************
 # Model 03b: 'item:step+rater'
-mod03b <- TAM::tam.mml.mfr( resp, facets=data.cqc03[,"rater",drop=FALSE],
+mod03b <- TAM::tam.mml.mfr( resp, facets=data.cqc03[,"rater",drop=FALSE],
             formulaA=~ item + item:step+rater, pid=data.cqc03$pid )
-summary( mod03b )
+summary( mod03b )
 
 #********************************************
 # Model 03c: 'step+rater' for first item 'crit1'
 # Restructuring the data is necessary.
 # Define raters as items in the new dataset 'dat1'.
-persons <- unique( data.cqc03$pid )
-raters <- unique( data.cqc03$rater )
-dat1 <- matrix( NA, nrow=length(persons), ncol=length(raters) + 1 )
-dat1 <- as.data.frame(dat1)
-colnames(dat1) <- c("pid", raters )
+persons <- unique( data.cqc03$pid )
+raters <- unique( data.cqc03$rater )
+dat1 <- matrix( NA, nrow=length(persons), ncol=length(raters) + 1 )
+dat1 <- as.data.frame(dat1)
+colnames(dat1) <- c("pid", raters )
 dat1$pid <- persons
 for (rr in raters){
     dat1.rr <- data.cqc03[ data.cqc03$rater==rr, ]
-    dat1[ match(dat1.rr$pid, persons),rr] <- dat1.rr$crit1
+    dat1[ match(dat1.rr$pid, persons),rr] <- dat1.rr$crit1
         }
   ##   > head(dat1)
   ##       pid R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26
@@ -321,42 +326,42 @@ 
                        Examp
   ##   5 10005  NA  NA   1   1  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
   ##   6 10006  NA  NA   1   1  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
 # estimate model 03c
-mod03c <- TAM::tam.mml( dat1[,-1], pid=dat1$pid )
-summary( mod03c )
+mod03c <- TAM::tam.mml( dat1[,-1], pid=dat1$pid )
+summary( mod03c )
 
 #############################################################################
 # EXAMPLE 04: Faceted Rasch model for rating data data.cqc04
 #############################################################################
 
-data(data.cqc04)
+data(data.cqc04)
 resp <- data.cqc04[,4:8]
-facets <- data.cqc04[, c("rater", "topic") ]
+facets <- data.cqc04[, c("rater", "topic") ]
 
 #********************************************
 # Model 04a: 'item*step+rater+topic'
 formulaA <- ~ item*step + rater + topic
-mod04a <- TAM::tam.mml.mfr( resp, facets=facets,
+mod04a <- TAM::tam.mml.mfr( resp, facets=facets,
             formulaA=formulaA, pid=data.cqc04$pid )
-summary( mod04a )
+summary( mod04a )
 
 #********************************************
 # Model 04b: 'item*step+rater+topic+item*rater+item*topic'
 formulaA <- ~ item*step + rater + topic + item*rater + item*topic
-mod04b <- TAM::tam.mml.mfr( resp, facets=facets,
+mod04b <- TAM::tam.mml.mfr( resp, facets=facets,
             formulaA=formulaA, pid=data.cqc04$pid )
-summary( mod04b )
+summary( mod04b )
 
 #********************************************
 # Model 04c: 'item*step' with fixing rater and topic parameters to zero
 formulaA <- ~ item*step + rater + topic
-mod04c0 <- TAM::tam.mml.mfr( resp, facets=facets,
-            formulaA=formulaA, pid=data.cqc04$pid, control=list(maxiter=4) )
-summary( mod04c0 )
+mod04c0 <- TAM::tam.mml.mfr( resp, facets=facets,
+            formulaA=formulaA, pid=data.cqc04$pid, control=list(maxiter=4) )
+summary( mod04c0 )
 # fix rater and topic parameter to zero
 xsi.est <- mod04c0$xsi
-xsi.fixed <- cbind( seq(1,nrow(xsi.est)), xsi.est$xsi )
-rownames(xsi.fixed) <- rownames(xsi.est)
-xsi.fixed <- xsi.fixed[ c(8:13),]
+xsi.fixed <- cbind( seq(1,nrow(xsi.est)), xsi.est$xsi )
+rownames(xsi.fixed) <- rownames(xsi.est)
+xsi.fixed <- xsi.fixed[ c(8:13),]
 xsi.fixed[,2] <- 0
   ##   > xsi.fixed
   ##             [,1] [,2]
@@ -366,17 +371,17 @@ 
                        Examp
   ##   topicFami   11    0
   ##   topicScho   12    0
   ##   topicSpor   13    0
-mod04c1 <- TAM::tam.mml.mfr( resp, facets=facets,
+mod04c1 <- TAM::tam.mml.mfr( resp, facets=facets,
              formulaA=formulaA, pid=data.cqc04$pid, xsi.fixed=xsi.fixed )
-summary( mod04c1 )
+summary( mod04c1 )
 
 #############################################################################
 # EXAMPLE 05: Partial credit model with latent regression and
 #             plausible value imputation
 #############################################################################
 
-data(data.cqc05)
-resp <- data.cqc05[, -c(1:3) ] # select item responses
+data(data.cqc05)
+resp <- data.cqc05[, -c(1:3) ] # select item responses
 
 #********************************************
 # Model 05a: Partial credit model
@@ -386,38 +391,38 @@ 
                        Examp
 # Model 05b: Partial credit model with latent regressors
 mod05b <-tam.mml(resp=resp, irtmodel="PCM2",  Y=data.cqc05[,1:3] )
 # Plausible value imputation
-pvmod05b <- TAM::tam.pv( mod05b )
-# }
                        
+pvmod05b <- TAM::tam.pv( mod05b )
+}
                        
   
                        
   
 
 
+
       
                        
       
                        
   
                        Developed by Alexander Robitzsch.
                        
 
                        
 
 
                        
-  
                        Site built with pkgdown 1.3.0.
                        
+  
                        Site built with pkgdown 1.4.1.
                        
 
                        
+
       
                        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.ctest.html b/docs/reference/data.ctest.html
index 2a0ac9c..e14486f 100644
--- a/docs/reference/data.ctest.html
+++ b/docs/reference/data.ctest.html
@@ -8,21 +8,25 @@
 
 Some C-Test Datasets — data.ctest • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                          
         
                        • 
   
-    
+    
      
   
 
                          • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                          
@@ -105,18 +111,18 @@ 
                          Some C-Test Datasets
                          
     
                          
 
     
                          
-    
     
                          Some C-Test datasets.
                          
-    
     
                          
 
-    
                          data(data.ctest1)
-data(data.ctest2)
                          
-        
+    
                          data(data.ctest1)
+data(data.ctest2)
                          
+
+
     
                          Format
                          
 
     
-    
                            
+
+
                              
 
                            • The dataset data.ctest1 contains item responses of C-tests
 at two time points. The format is
                              
 
                              'data.frame':   1675 obs. of  42 variables: 
                              
@@ -170,32 +176,35 @@ 
                              Format  ..$ ctest  : chr [1:100] "A" "A" "A" "A" ... 
                              
   ..$ testlet: int [1:100] 1 1 2 2 2 3 3 3 NA 4 ... 
                              • 
 
                            
-    
+
 
   
   
                          
 
   
 
 
+
       
                          
       
                          
   
                          Developed by Alexander Robitzsch.
                          
 
                          
 
 
                          
-  
                          Site built with pkgdown 1.3.0.
                          
+  
                          Site built with pkgdown 1.4.1.
                          
 
                          
+
       
                          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.examples.html b/docs/reference/data.examples.html
index 57020e1..6a557e4 100644
--- a/docs/reference/data.examples.html
+++ b/docs/reference/data.examples.html
@@ -8,21 +8,25 @@
 
 Datasets <code>data.ex</code> in <span class="pkg">TAM</span> Package — data.examples • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-TAM Package — data.examples" />
 
+TAM Package — data.examples" />
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                            
         
                          • 
   
-    
+    
      
   
 
                            • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                            
@@ -105,22 +111,22 @@ 
                            Datasets data.ex in TAM Package
                            
     
                            
 
     
                            
-    
     
                            Datasets included in the TAM package
                            
-    
     
                            
 
-    
                            data(data.ex08)
-data(data.ex10)
-data(data.ex11)
-data(data.ex12)
-data(data.ex14)
-data(data.ex15)
-data(data.exJ03)
                            
-        
+    
                            data(data.ex08)
+data(data.ex10)
+data(data.ex11)
+data(data.ex12)
+data(data.ex14)
+data(data.ex15)
+data(data.exJ03)
                            
+
+
     
                            Format
                            
 
-    
                              
+    
+
                                
 
                              • Data data.ex08 for Example 8 in tam.mml
 has the following format:
                                
 
                                List of 2 
                                
@@ -241,7 +247,8 @@ 
                                Format $ M1    : int  NA NA NA NA NA NA NA NA NA NA ... 
                                
  $ M2    : int  NA NA NA NA NA NA NA NA NA NA ... 
                                
  $ M3    : int  NA NA NA NA NA NA NA NA NA NA ... 
                                
-    
                                • 
+
+
                                
 
                              • Data data.ex17 contains polytomous item response data from
 three studies corresponding to three grades.
                                
 
                                'data.frame':   3235 obs. of  15 variables: 
                                
@@ -261,38 +268,38 @@ 
                                Format $ L     : num  NA NA NA NA NA NA NA NA NA NA ... 
                                
  $ M     : num  NA NA NA NA NA NA NA NA NA NA ... 
                                • 
 
                              
-    
     
                              See also
                              
 
     
                              These examples are used in the tam.mml Examples.
                              
-    
 
   
   
                            
 
   
 
 
+
       
                            
       
                            
   
                            Developed by Alexander Robitzsch.
                            
 
                            
 
 
                            
-  
                            Site built with pkgdown 1.3.0.
                            
+  
                            Site built with pkgdown 1.4.1.
                            
 
                            
+
       
                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.fims.Aus.Jpn.html b/docs/reference/data.fims.Aus.Jpn.html
index e3f5b53..805c323 100644
--- a/docs/reference/data.fims.Aus.Jpn.html
+++ b/docs/reference/data.fims.Aus.Jpn.html
@@ -8,21 +8,25 @@
 
 Dataset FIMS Study with Responses of Australian and Japanese Students — data.fims.Aus.Jpn.scored • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                              
         
                            • 
   
-    
+    
      
   
 
                              • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                              
@@ -107,16 +113,15 @@ 
                              Dataset FIMS Study with Responses of Australian and Japanese Students
                              
     
                              
 
     
                              
-    
     
                              Dataset FIMS study with raw responses (data.fims.Aus.Jpn.raw) or
 scored responses (data.fims.Aus.Jpn.scored) of Australian and
 Japanese Students.
                              
-    
     
                              
 
-    
                              data(data.fims.Aus.Jpn.raw)
-data(data.fims.Aus.Jpn.scored)
                              
-        
+    
                              data(data.fims.Aus.Jpn.raw)
+data(data.fims.Aus.Jpn.scored)
                              
+
+
     
                              Format
                              
 
     
                              A data frame with 6371 observations on the following 16 variables.
                              
@@ -136,16 +141,16 @@ 
                              FormatM1PTI22
                              A Mathematics item
                              
     
                              M1PTI23
                              A Mathematics item
                              
     
                              country
                              Country: 1 -- Australia, 2 -- Japan
                              
-  
                              
-    
+  
+
+
     
                              See also
                              
 
     
                              http://www.edmeasurementsurveys.com/TAM/Tutorials/7DIF.htm
                              
-    
 
     
                              Examples
                              
-    
                              # NOT RUN {
-data(data.fims.Aus.Jpn.scored)
+    
                              if (FALSE) {
+data(data.fims.Aus.Jpn.scored)
 #*****
 # Model 1: Differential Item Functioning Gender for Australian students
 
@@ -153,22 +158,22 @@ 
                              Examp
 scored <- data.fims.Aus.Jpn.scored[ data.fims.Aus.Jpn.scored$country==1, ]
 
 # select items
-items <- grep("M1", colnames(data.fims.Aus.Jpn.scored), value=TRUE)
+items <- grep("M1", colnames(data.fims.Aus.Jpn.scored), value=TRUE)
 ##   > items
 ##    [1] "M1PTI1"  "M1PTI2"  "M1PTI3"  "M1PTI6"  "M1PTI7"  "M1PTI11" "M1PTI12"
 ##    [8] "M1PTI14" "M1PTI17" "M1PTI18" "M1PTI19" "M1PTI21" "M1PTI22" "M1PTI23"
 
 # Run partial credit model
-mod1 <- TAM::tam.mml(scored[,items])
+mod1 <- TAM::tam.mml(scored[,items])
 
 # extract values of the gender variable into a variable called "gender".
 gender <- scored[,"SEX"]
 # computes the test score for each student by calculating the row sum
 # of each student's scored responses.
-raw_score <- rowSums(scored[,items] )
+raw_score <- rowSums(scored[,items] )
 
 # compute the mean test score for each gender group: 1=male, and 2=female
-stats::aggregate(raw_score,by=list(gender),FUN=mean)
+stats::aggregate(raw_score,by=list(gender),FUN=mean)
 # The mean test score is 6.12 for group 1 (males) and 6.27 for group 2 (females).
 # That is, the two groups performed similarly, with girls having a slightly
 # higher mean test score. The step of computing raw test scores is not necessary
@@ -179,39 +184,40 @@ 
                              Examp
 # To conduct a DIF analysis, we set up the variable "gender" as a facet and
 # re-run the IRT analysis.
 formulaA <- ~item+gender+item*gender    # define facets analysis
-facets <- as.data.frame(gender)         # data frame with student covariates
+facets <- as.data.frame(gender)         # data frame with student covariates
 # facets model for studying differential item functioning
-mod2 <- TAM::tam.mml.mfr( resp=scored[,items], facets=facets, formulaA=formulaA )
-summary(mod2)
-# }
                              
+mod2 <- TAM::tam.mml.mfr( resp=scored[,items], facets=facets, formulaA=formulaA )
+summary(mod2)
+}
                              
   
   
 
 
+
       
                              
       
                              
   
                              Developed by Alexander Robitzsch.
                              
 
                              
 
 
                              
-  
                              Site built with pkgdown 1.3.0.
                              
+  
                              Site built with pkgdown 1.4.1.
                              
 
                              
+
       
                              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.geiser.html b/docs/reference/data.geiser.html
index 9554ecf..8e46652 100644
--- a/docs/reference/data.geiser.html
+++ b/docs/reference/data.geiser.html
@@ -8,21 +8,25 @@
 
 Dataset from Geiser et al. (2006) — data.geiser • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                
         
                              • 
   
-    
+    
      
   
 
                                • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                
@@ -106,14 +112,13 @@ 
                                Dataset from Geiser et al. (2006)
                                
     
                                
 
     
                                
-    
     
                                This is a subsample of the dataset used in Geiser et al. (2006) and
 Geiser and Eid (2010).
                                
-    
     
                                
 
-    
                                data(data.geiser)
                                
-        
+    
                                data(data.geiser)
                                
+
+
     
                                Format
                                
 
     
                                A data frame with 519 observations on the following 24 variables
                                
@@ -125,7 +130,6 @@ 
                                Format[...] 
                                
  $ mrt23: num  0 0 0 0 0 0 0 1 0 0 ... 
                                
  $ mrt24: num  0 0 0 0 0 0 0 0 0 0 ... 
                                
-    
     
                                References
                                
 
     
                                Geiser, C., & Eid, M. (2010). Item-Response-Theorie.
@@ -134,15 +138,14 @@ 
                                R
 
                                Geiser, C., Lehmann, W., & Eid, M. (2006). Separating rotators from
 nonrotators in the mental rotations test: A multigroup latent class analysis.
 Multivariate Behavioral Research, 41(3), 261-293.
                                
-    
 
     
                                Examples
                                
-    
                                # NOT RUN {
+    
                                if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Latent trait and latent class models (Geiser et al., 2006, MBR)
 #############################################################################
 
-data(data.geiser)
+data(data.geiser)
 dat <- data.geiser
 
 #**********************************************
@@ -154,8 +157,8 @@ 
                                Examp
   ITEM TYPE:
     ALL(Rasch)
     "
-mod1 <- TAM::tamaan( tammodel, dat)
-summary(mod1)
+mod1 <- TAM::tamaan( tammodel, dat)
+summary(mod1)
 
 #**********************************************
 # Model 2: 2PL model
@@ -164,11 +167,11 @@ 
                                Examp
     F=~ mrt1__mrt12
     F ~~ 1*F
     "
-mod2 <- TAM::tamaan( tammodel, dat)
-summary(mod2)
+mod2 <- TAM::tamaan( tammodel, dat)
+summary(mod2)
 
 # model comparison Rasch vs. 2PL
-anova(mod1,mod2)
+anova(mod1,mod2)
 
 #*********************************************************************
 #*** Model 3: Latent class analysis with four classes
@@ -181,14 +184,14 @@ 
                                Examp
 LAVAAN MODEL:
   F=~ mrt1__mrt12
     "
-mod3 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod3)
+mod3 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod3)
 
 # extract item response functions
 imod2 <- IRT.irfprob(mod3)[,2,]
 # plot class specific probabilities
-matplot( imod2, type="o", pch=1:4, xlab="Item", ylab="Probability" )
-legend( 10,1, paste0("Class",1:4), lty=1:4, col=1:4, pch=1:4 )
+matplot( imod2, type="o", pch=1:4, xlab="Item", ylab="Probability" )
+legend( 10,1, paste0("Class",1:4), lty=1:4, col=1:4, pch=1:4 )
 
 #*********************************************************************
 #*** Model 4: Latent class analysis with five classes
@@ -201,12 +204,12 @@ 
                                Examp
 LAVAAN MODEL:
   F=~ mrt1__mrt12
     "
-mod4 <- TAM::tamaan( tammodel, resp=dat )
-summary(mod4)
+mod4 <- TAM::tamaan( tammodel, resp=dat )
+summary(mod4)
 
 # compare different models
-AIC(mod1); AIC(mod2); AIC(mod3); AIC(mod4)
-BIC(mod1); BIC(mod2); BIC(mod3); BIC(mod4)
+AIC(mod1); AIC(mod2); AIC(mod3); AIC(mod4)
+BIC(mod1); BIC(mod2); BIC(mod3); BIC(mod4)
 # more condensed form
 IRT.compareModels(mod1, mod2, mod3, mod4)
 
@@ -214,7 +217,7 @@ 
                                Examp
 # EXAMPLE 2: Rasch model and mixture Rasch model (Geiser & Eid, 2010)
 #############################################################################
 
-data(data.geiser)
+data(data.geiser)
 dat <- data.geiser
 
 #*********************************************************************
@@ -226,8 +229,8 @@ 
                                Examp
 ITEM TYPE:
   ALL(Rasch);
     "
-mod1 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod1)
+mod1 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod1)
 
 #*********************************************************************
 #*** Model 2: Mixed Rasch model with two classes
@@ -242,19 +245,19 @@ 
                                Examp
 ITEM TYPE:
   ALL(Rasch);
     "
-mod2 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod2)
+mod2 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod2)
 
 # plot item parameters
 ipars <- mod2$itempartable_MIXTURE[ 1:6, ]
-plot( 1:6, ipars[,3], type="o", ylim=c(-3,2), pch=16,
+plot( 1:6, ipars[,3], type="o", ylim=c(-3,2), pch=16,
         xlab="Item", ylab="Item difficulty")
-lines( 1:6, ipars[,4], type="l", col=2, lty=2)
-points( 1:6, ipars[,4],  col=2, pch=2)
+lines( 1:6, ipars[,4], type="l", col=2, lty=2)
+points( 1:6, ipars[,4],  col=2, pch=2)
 
 # extract individual posterior distribution
 post2 <- IRT.posterior(mod2)
-str(post2)
+str(post2)
 # num [1:519, 1:30] 0.000105 0.000105 0.000105 0.000105 0.000105 ...
 # - attr(*, "theta")=num [1:30, 1:30] 1 0 0 0 0 0 0 0 0 0 ...
 # - attr(*, "prob.theta")=num [1:30, 1] 1.21e-05 2.20e-04 2.29e-03 1.37e-02 4.68e-02 ...
@@ -266,7 +269,7 @@ 
                                Examp
 mod2$E[1,2,,"mrt1_F_load_Cl2"]
 
 # compute individual posterior probability for class 1 (first 15 columns)
-round( rowSums( post2[, 1:15] ), 3 )
+round( rowSums( post2[, 1:15] ), 3 )
 # columns 16 to 30 refer to class 2
 
 #*********************************************************************
@@ -282,49 +285,50 @@ 
                                Examp
 ITEM TYPE:
   ALL(Rasch);
     "
-mod3 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod3)
+mod3 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod3)
 
 # plot item parameters
 ipars <- mod3$itempartable_MIXTURE[ 1:6, ]
-plot( 1:6, ipars[,3], type="o", ylim=c(-3.7,2), pch=16,
+plot( 1:6, ipars[,3], type="o", ylim=c(-3.7,2), pch=16,
         xlab="Item", ylab="Item difficulty")
-lines( 1:6, ipars[,4], type="l", col=2, lty=2)
-points( 1:6, ipars[,4],  col=2, pch=2)
-lines( 1:6, ipars[,5], type="l", col=3, lty=3)
-points( 1:6, ipars[,5],  col=3, pch=17)
+lines( 1:6, ipars[,4], type="l", col=2, lty=2)
+points( 1:6, ipars[,4],  col=2, pch=2)
+lines( 1:6, ipars[,5], type="l", col=3, lty=3)
+points( 1:6, ipars[,5],  col=3, pch=17)
 
 # model comparison
 IRT.compareModels( mod1, mod2, mod3 )
-# }
                                
+}
                                
   
   
 
 
+
       
                                
       
                                
   
                                Developed by Alexander Robitzsch.
                                
 
                                
 
 
                                
-  
                                Site built with pkgdown 1.3.0.
                                
+  
                                Site built with pkgdown 1.4.1.
                                
 
                                
+
       
                                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.gpcm.html b/docs/reference/data.gpcm.html
index e7a5b53..d2eb588 100644
--- a/docs/reference/data.gpcm.html
+++ b/docs/reference/data.gpcm.html
@@ -8,21 +8,25 @@
 
 Dataset with Ordered Indicators — data.gpcm • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                  
         
                                • 
   
-    
+    
      
   
 
                                  • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                  
@@ -105,59 +111,57 @@ 
                                  Dataset with Ordered Indicators
                                  
     
                                  
 
     
                                  
-    
     
                                  Dataset with ordered values of 3 indicators
                                  
-    
     
                                  
 
-    
                                  data(data.gpcm)
                                  
-        
+    
                                  data(data.gpcm)
                                  
+
+
     
                                  Format
                                  
 
     
                                  A data frame with 392 observations on the following 3 items.
                                  
     
                                  Comfort
                                  a numeric vector
                                  
     
                                  Work
                                  a numeric vector
                                  
     
                                  Benefit
                                  a numeric vector
                                  
-  
                                  
-    
+  
+
+
     
                                  Source
                                  
 
     
                                  The dataset is copied from the ltm package.
                                  
-    
 
     
                                  Examples
                                  
-    
                                  # NOT RUN {
-data(data.gpcm)
-summary(data.gpcm)
-# }
                                  
+    
                                  data(data.gpcm)
+summary(data.gpcm)
                                  
   
   
 
 
+
       
                                  
       
                                  
   
                                  Developed by Alexander Robitzsch.
                                  
 
                                  
 
 
                                  
-  
                                  Site built with pkgdown 1.3.0.
                                  
+  
                                  Site built with pkgdown 1.4.1.
                                  
 
                                  
+
       
                                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.janssen.html b/docs/reference/data.janssen.html
index 6bb8c3f..fb7f0f7 100644
--- a/docs/reference/data.janssen.html
+++ b/docs/reference/data.janssen.html
@@ -8,21 +8,25 @@
 
 Dataset from Janssen and Geiser (2010) — data.janssen • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                    
         
                                  • 
   
-    
+    
      
   
 
                                    • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                    
@@ -105,17 +111,17 @@ 
                                    Dataset from Janssen and Geiser (2010)
                                    
     
                                    
 
     
                                    
-    
     
                                    Dataset used in Janssen and Geiser (2010).
                                    
-    
     
                                    
 
-    
                                    data(data.janssen)
-data(data.janssen2)
                                    
-        
+    
                                    data(data.janssen)
+data(data.janssen2)
                                    
+
+
     
                                    Format
                                    
 
-    
                                      
+    
+
                                        
 
                                      • data.janssen is a data frame with 346 observations on the 8
 items of the following format
                                        
 
                                        'data.frame':   346 obs. of  8 variables: 
                                        
@@ -135,24 +141,23 @@ 
                                        Format[...] 
                                        
  $ IST020: num  0 0 0 1 1 0 0 0 0 0 ... 
                                        • 
 
                                      
-    
+
     
                                      References
                                      
 
     
                                      Janssen, A. B., & Geiser, C. (2010). On the relationship between solution
 strategies in two mental rotation tasks.
 Learning and Individual Differences, 20(5), 473-478.
                                      
-    
 
     
                                      Examples
                                      
-    
                                      # NOT RUN {
+    
                                      if (FALSE) {
 #############################################################################
 # EXAMPLE 1: CCT data, Janssen and Geiser (2010, LID)
 #            Latent class analysis based on data.janssen
 #############################################################################
 
-data(data.janssen)
+data(data.janssen)
 dat <- data.janssen
-colnames(dat)
+colnames(dat)
   ##   [1] "PIS1"  "PIS3"  "PIS4"  "PIS5"  "SCR6"  "SCR9"  "SCR10" "SCR17"
 
 #*********************************************************************
@@ -167,15 +172,15 @@ 
                                      Examp
   # missing item numbers (e.g. PIS2) are ignored in the model
   F=~ PIS1__PIS5 + SCR6__SCR17
     "
-mod3 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod3)
+mod3 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod3)
 
 # extract item response functions
 imod2 <- IRT.irfprob(mod3)[,2,]
 # plot class specific probabilities
 ncl <- 2
-matplot( imod2, type="o", pch=1:ncl, xlab="Item", ylab="Probability" )
-legend( 1, .3, paste0("Class",1:ncl), lty=1:ncl, col=1:ncl, pch=1:ncl )
+matplot( imod2, type="o", pch=1:ncl, xlab="Item", ylab="Probability" )
+legend( 1, .3, paste0("Class",1:ncl), lty=1:ncl, col=1:ncl, pch=1:ncl )
 
 #*********************************************************************
 #*** Model 2: Latent class analysis with three classes
@@ -188,47 +193,48 @@ 
                                      Examp
 LAVAAN MODEL:
   F=~ PIS1__PIS5 + SCR6__SCR17
     "
-mod3 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod3)
+mod3 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod3)
 
 # extract item response functions
 imod2 <- IRT.irfprob(mod3)[,2,]
 # plot class specific probabilities
 ncl <- 3
-matplot( imod2, type="o", pch=1:ncl, xlab="Item", ylab="Probability" )
-legend( 1, .3, paste0("Class",1:ncl), lty=1:ncl, col=1:ncl, pch=1:ncl )
+matplot( imod2, type="o", pch=1:ncl, xlab="Item", ylab="Probability" )
+legend( 1, .3, paste0("Class",1:ncl), lty=1:ncl, col=1:ncl, pch=1:ncl )
 
 # compare models
-AIC(mod1); AIC(mod2)
-# }
                                      
+AIC(mod1); AIC(mod2)
+}
                                      
   
   
 
 
+
       
                                      
       
                                      
   
                                      Developed by Alexander Robitzsch.
                                      
 
                                      
 
 
                                      
-  
                                      Site built with pkgdown 1.3.0.
                                      
+  
                                      Site built with pkgdown 1.4.1.
                                      
 
                                      
+
       
                                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.mc.html b/docs/reference/data.mc.html
index 58afc9d..e53888c 100644
--- a/docs/reference/data.mc.html
+++ b/docs/reference/data.mc.html
@@ -8,21 +8,25 @@
 
 Dataset with Raw and Scored Responses from Multiple Choice Items — data.mc • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                        
         
                                      • 
   
-    
+    
      
   
 
                                        • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                        
@@ -106,14 +112,13 @@ 
                                        Dataset with Raw and Scored Responses from Multiple Choice Items
                                        
     
                                        
 
     
                                        
-    
     
                                        Dataset of responses from multiple choice items, containing 143 students
 on 30 items.
                                        
-    
     
                                        
 
-    
                                        data(data.mc)
                                        
-        
+    
                                        data(data.mc)
                                        
+
+
     
                                        Format
                                        
 
     
                                        The dataset is a list with two elements. The entry raw
@@ -131,32 +136,34 @@ 
                                        Format  [...] 
                                        
   ..$ I29: num [1:143] NA 0 1 0 1 0 0 0 0 0 ... 
                                        
   ..$ I30: num [1:143] NA NA 1 1 1 1 0 1 1 0 ... 
                                        
-    
 
   
   
                                      
 
   
 
 
+
       
                                      
       
                                      
   
                                      Developed by Alexander Robitzsch.
                                      
 
                                      
 
 
                                      
-  
                                      Site built with pkgdown 1.3.0.
                                      
+  
                                      Site built with pkgdown 1.4.1.
                                      
 
                                      
+
       
                                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.numeracy.html b/docs/reference/data.numeracy.html
index 9952f06..69fcf29 100644
--- a/docs/reference/data.numeracy.html
+++ b/docs/reference/data.numeracy.html
@@ -8,21 +8,25 @@
 
 Dataset Numeracy — data.numeracy • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                        
         
                                      • 
   
-    
+    
      
   
 
                                        • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                        
@@ -106,14 +112,13 @@ 
                                        Dataset Numeracy
                                        
     
                                        
 
     
                                        
-    
     
                                        Dataset numeracy with unscored (raw) and scored (scored)
 item responses of 876 persons and 15 items.
                                        
-    
     
                                        
 
-    
                                        data(data.numeracy)
                                        
-        
+    
                                        data(data.numeracy)
                                        
+
+
     
                                        Format
                                        
 
     
                                        The format is a list a two entries:
                                        
@@ -132,19 +137,17 @@ 
                                        Format  ..$ I4 : int [1:876] 0 1 0 0 1 1 1 1 1 1 ... 
                                        
   [...]  
                                        
   ..$ I15: int [1:876] 1 1 1 1 0 1 1 1 1 1 ... 
                                        
-    
 
     
                                        Examples
                                        
-    
                                        # NOT RUN {
-######################################################################
+    
                                        ######################################################################
 # (1) Scored numeracy data
 ######################################################################
 
-data(data.numeracy)
+data(data.numeracy)
 dat <- data.numeracy$scored
 
 #Run IRT analysis: Rasch model
-mod1 <- TAM::tam.mml(dat)
+mod1 <- TAM::tam.mml(dat)
 
 #Item difficulties
 mod1$xsi
@@ -152,31 +155,31 @@ 
                                        Examp
 ItemDiff
 
 #Ability estimate - Weighted Likelihood Estimate
-Abil <- TAM::tam.wle(mod1)
+Abil <- TAM::tam.wle(mod1)
 Abil
 PersonAbility <- Abil$theta
 PersonAbility
 
 #Descriptive statistics of item and person parameters
-hist(ItemDiff)
-hist(PersonAbility)
-mean(ItemDiff)
-mean(PersonAbility)
-stats::sd(ItemDiff)
-stats::sd(PersonAbility)
-
-# }# NOT RUN {
+hist(ItemDiff)
+hist(PersonAbility)
+mean(ItemDiff)
+mean(PersonAbility)
+stats::sd(ItemDiff)
+stats::sd(PersonAbility)
+
+if (FALSE) {
 #Extension
 #plot histograms of ability and item parameters in the same graph
-oldpar <- par(no.readonly=TRUE)          # save writable default graphic settings
-windows(width=4.45, height=4.45, pointsize=12)
-layout(matrix(c(1,1,2),3,byrow=TRUE))
-layout.show(2)
-hist(PersonAbility,xlim=c(-3,3),breaks=20)
-hist(ItemDiff,xlim=c(-3,3),breaks=20)
+oldpar <- par(no.readonly=TRUE)          # save writable default graphic settings
+windows(width=4.45, height=4.45, pointsize=12)
+layout(matrix(c(1,1,2),3,byrow=TRUE))
+layout.show(2)
+hist(PersonAbility,xlim=c(-3,3),breaks=20)
+hist(ItemDiff,xlim=c(-3,3),breaks=20)
 
-par( oldpar )  # restore default graphic settings
-hist(PersonAbility,xlim=c(-3,3),breaks=20)
+par( oldpar )  # restore default graphic settings
+hist(PersonAbility,xlim=c(-3,3),breaks=20)
 
 ######################################################################
 # (2) Raw numeracy data
@@ -185,54 +188,56 @@ 
                                        Examp
 raw_resp <- data.numeracy$raw
 
 #score responses
-key <- c(1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1)
-scored <- sapply( seq(1,length(key)),
+key <- c(1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1)
+scored <- sapply( seq(1,length(key)),
             FUN=function(ii){ 1*(raw_resp[,ii]==key[ii]) } )
 
 #run IRT analysis
-mod1 <- TAM::tam.mml(scored)
+mod1 <- TAM::tam.mml(scored)
 
 #Ability estimate - Weighted Likelihood Estimate
-Abil <- TAM::tam.wle(mod1)
+Abil <- TAM::tam.wle(mod1)
 
 #CTT statistics
-ctt1 <- TAM::tam.ctt(raw_resp, Abil$theta)
-write.csv(ctt1,"D1_ctt1.csv")       # write statistics into a file
+ctt1 <- TAM::tam.ctt(raw_resp, Abil$theta)
+write.csv(ctt1,"D1_ctt1.csv")       # write statistics into a file
         # use maybe write.csv2 if ';' should be the column separator
 
 #Fit statistics
-Fit <- TAM::tam.fit(mod1)
+Fit <- TAM::tam.fit(mod1)
 Fit
 
 # plot expected response curves
-plot( mod1, ask=TRUE )
-# }
                                        
+plot( mod1, ask=TRUE )
+}
                                        
   
   
 
 
+
       
                                        
       
                                        
   
                                        Developed by Alexander Robitzsch.
                                        
 
                                        
 
 
                                        
-  
                                        Site built with pkgdown 1.3.0.
                                        
+  
                                        Site built with pkgdown 1.4.1.
                                        
 
                                        
+
       
                                        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.sim.mfr.html b/docs/reference/data.sim.mfr.html
index 7eb101f..f1cac81 100644
--- a/docs/reference/data.sim.mfr.html
+++ b/docs/reference/data.sim.mfr.html
@@ -8,21 +8,25 @@
 
 Simulated Multifaceted Data — data.sim.mfr • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                          
         
                                        • 
   
-    
+    
      
   
 
                                          • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                          
@@ -105,14 +111,13 @@ 
                                          Simulated Multifaceted Data
                                          
     
                                          
 
     
                                          
-    
     
                                          Simulated data from multiple facets.
                                          
-    
     
                                          
 
-    
                                          data(data.sim.mfr)
- data(data.sim.facets)
                                          
-        
+    
                                          data(data.sim.mfr)
+ data(data.sim.facets)
                                          
+
+
     
                                          Format
                                          
 
     
                                          The format of data.sim.mfr is: 
                                          
@@ -125,44 +130,41 @@ 
                                          Format $ rater : num  1 2 3 4 5 1 2 3 4 5 ... 
                                          
  $ topic : num  3 1 3 1 3 2 3 2 2 1 ... 
                                          
  $ female: num  2 2 1 2 1 1 2 1 2 1 ... 
                                          
-    
     
                                          Source
                                          
 
     
                                          Simulated
                                          
-    
 
     
                                          Examples
                                          
-    
                                          # NOT RUN {
-#######
+    
                                          #######
 # sim multi faceted Rasch model
-data(data.sim.mfr)
-data(data.sim.facets)
+data(data.sim.mfr)
+data(data.sim.facets)
 
   # 1: A-matrix test_rater
-  test_1_items <- TAM::.A.matrix( data.sim.mfr, formulaA=~rater,
+  test_1_items <- TAM::.A.matrix( data.sim.mfr, formulaA=~rater,
             facets=data.sim.facets, constraint="items" )
-  test_1_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~rater,
+  test_1_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~rater,
             facets=data.sim.facets, constraint="cases" )
 
   # 2: test_item+rater
-  test_2_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+rater,
+  test_2_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+rater,
             facets=data.sim.facets, constraint="cases" )
 
   # 3: test_item+rater+topic+ratertopic
-  test_3_items <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+rater*topic,
+  test_3_items <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+rater*topic,
             facets=data.sim.facets, constraint="items" )
   # conquest uses a different way of ordering the rows
   # these are the first few rows of the conquest design matrix
   # test_3_items$A[grep("item1([[:print:]])*topic1", rownames(test_3_items)),]
 
   # 4: test_item+step
-  test_4_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+step,
+  test_4_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+step,
             facets=data.sim.facets, constraint="cases" )
 
   # 5: test_item+item:step
-  test_5_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+item:step,
+  test_5_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+item:step,
             facets=data.sim.facets, constraint="cases" )
-  test_5_cases$A[, grep("item1", colnames(test_5_cases)) ]
+  test_5_cases$A[, grep("item1", colnames(test_5_cases)) ]
 
   # 5+x: more
   #=> 6: is this even well defined in the conquest-design output
@@ -171,62 +173,62 @@ 
                                          Examp
   #        currently .A.matrix throws a warning
   # test_6_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+topic:step,
   #                 facets=data.sim.facets, constraint="cases" )
-  test_7_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+topic+topic:step,
+  test_7_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+topic+topic:step,
             facets=data.sim.facets, constraint="cases" )
 
-# }# NOT RUN {
+if (FALSE) {
   #=> 8: same as with 6
-  test_8_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+rater+item:rater:step,
+  test_8_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+rater+item:rater:step,
             facets=data.sim.facets, constraint="cases" )
 ## [1] "Can't proceed the estimation: Lower-order term is missing."
-  test_9_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+step+rater+item:step+item:rater,
+  test_9_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+step+rater+item:step+item:rater,
             facets=data.sim.facets, constraint="cases" )
-  test_10_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+female+item:female,
+  test_10_cases <- TAM::.A.matrix( data.sim.mfr, formulaA=~item+female+item:female,
             facets=data.sim.facets, constraint="cases" )
 
   ### All Design matrices
-  test_1_cases <- TAM::designMatrices.mfr( data.sim.mfr, formulaA=~rater,
+  test_1_cases <- TAM::designMatrices.mfr( data.sim.mfr, formulaA=~rater,
             facets=data.sim.facets, constraint="cases" )
-  test_4_cases <- TAM::designMatrices.mfr( data.sim.mfr, formulaA=~item+item:step,
+  test_4_cases <- TAM::designMatrices.mfr( data.sim.mfr, formulaA=~item+item:step,
             facets=data.sim.facets, constraint="cases" )
 
   ### TAM
-  test_4_cases <- TAM::tam.mml.mfr( data.sim.mfr, formulaA=~item+item:step )
-  test_tam <- TAM::tam.mml( data.sim.mfr )
+  test_4_cases <- TAM::tam.mml.mfr( data.sim.mfr, formulaA=~item+item:step )
+  test_tam <- TAM::tam.mml( data.sim.mfr )
 
-  test_1_cases <- TAM::tam.mml.mfr( data.sim.mfr, formulaA=~rater,
+  test_1_cases <- TAM::tam.mml.mfr( data.sim.mfr, formulaA=~rater,
             facets=data.sim.facets, constraint="cases" )
-  test_2_cases <- TAM::tam.mml.mfr( data.sim.mfr, formulaA=~item+rater,
-            facets=data.sim.facets, constraint="cases" )
-# }
                                          
+  test_2_cases <- TAM::tam.mml.mfr( data.sim.mfr, formulaA=~item+rater,
+            facets=data.sim.facets, constraint="cases" )}
                                          
   
   
 
 
+
       
                                          
       
                                          
   
                                          Developed by Alexander Robitzsch.
                                          
 
                                          
 
 
                                          
-  
                                          Site built with pkgdown 1.3.0.
                                          
+  
                                          Site built with pkgdown 1.4.1.
                                          
 
                                          
+
       
                                          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.sim.rasch.html b/docs/reference/data.sim.rasch.html
index c00c9ab..5060dce 100644
--- a/docs/reference/data.sim.rasch.html
+++ b/docs/reference/data.sim.rasch.html
@@ -8,21 +8,25 @@
 
 Simulated Rasch data — data.sim.rasch • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                            
         
                                          • 
   
-    
+    
      
   
 
                                            • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                            
@@ -105,15 +111,14 @@ 
                                            Simulated Rasch data
                                            
     
                                            
 
     
                                            
-    
     
                                            Simulated Rasch data under unidimensional trait distribution
                                            
-    
     
                                            
 
-    
                                            data(data.sim.rasch)
- data(data.sim.rasch.pweights)
- data(data.sim.rasch.missing)
                                            
-        
+    
                                            data(data.sim.rasch)
+ data(data.sim.rasch.pweights)
+ data(data.sim.rasch.missing)
                                            
+
+
     
                                            Format
                                            
 
     
                                            The format is: 
                                             
                                            
@@ -121,7 +126,6 @@ 
                                            Format - attr(*, "dimnames")=List of 2 
                                            
    ..$ : NULL 
                                            
    ..$ : chr [1:40] "I1" "I2" "I3" "I4" ... 
                                            
-    
     
                                            Details
                                            
 
     
                                            N <- 2000      
                                            
@@ -133,85 +137,82 @@ 
                                            Details
  resp[ theta < 0, c(1,seq(I/2+1, I)) ] <- NA 
                                            
  # define person weights 
                                            
  pweights <- c(  rep(3,N/2), rep( 1, N/2 ) ) 
                                            
-    
     
                                            Source
                                            
 
     
                                            Simulated data (see Details)
                                            
-    
 
     
                                            Examples
                                            
-    
                                            # NOT RUN {
-data(data.sim.rasch)
+    
                                            if (FALSE) {
+data(data.sim.rasch)
 N <- 2000
-Y <- cbind( stats::rnorm( N, sd=1.5), stats::rnorm(N, sd=.3 ) )
+Y <- cbind( stats::rnorm( N, sd=1.5), stats::rnorm(N, sd=.3 ) )
 
 # Loading Matrix
 # B <- array( 0, dim=c( I, 2, 1 )  )
 # B[1:(nrow(B)), 2, 1] <- 1
-B <- TAM::designMatrices(resp=data.sim.rasch)[["B"]]
+B <- TAM::designMatrices(resp=data.sim.rasch)[["B"]]
 
 # estimate Rasch model
-mod1_1 <- TAM::tam.mml(resp=data.sim.rasch, Y=Y)
+mod1_1 <- TAM::tam.mml(resp=data.sim.rasch, Y=Y)
 
 # standard errors
-res1 <- TAM::tam.se(mod1_1)
+res1 <- TAM::tam.se(mod1_1)
 
 # Compute fit statistics
 tam.fit(mod1_1)
 
 # plausible value imputation
 # PV imputation has to be adpated for multidimensional case!
-pv1 <- TAM::tam.pv( mod1_1, nplausible=7, # 7 plausible values
+pv1 <- TAM::tam.pv( mod1_1, nplausible=7, # 7 plausible values
                samp.regr=TRUE       # sampling of regression coefficients
               )
 
 # item parameter constraints
-xsi.fixed <- matrix( c( 1, -2,5, -.22,10, 2 ), nrow=3, ncol=2, byrow=TRUE)
+xsi.fixed <- matrix( c( 1, -2,5, -.22,10, 2 ), nrow=3, ncol=2, byrow=TRUE)
 xsi.fixed
-mod1_4 <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed )
+mod1_4 <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed )
 
 # missing value handling
-data(data.sim.rasch.missing)
-mod1_2 <- TAM::tam.mml(data.sim.rasch.missing, Y=Y)
+data(data.sim.rasch.missing)
+mod1_2 <- TAM::tam.mml(data.sim.rasch.missing, Y=Y)
 
 # handling of sample (person) weights
-data(data.sim.rasch.pweights)
+data(data.sim.rasch.pweights)
 N <- 1000
-pweights <- c(  rep(3,N/2), rep( 1, N/2 ) )
-mod1_3 <- TAM::tam.mml( data.sim.rasch.pweights, control=list(conv=.001),
+pweights <- c(  rep(3,N/2), rep( 1, N/2 ) )
+mod1_3 <- TAM::tam.mml( data.sim.rasch.pweights, control=list(conv=.001),
                pweights=pweights )
-
-# }
                                            
+  }
                                            
   
   
 
 
+
       
                                            
       
                                            
   
                                            Developed by Alexander Robitzsch.
                                            
 
                                            
 
 
                                            
-  
                                            Site built with pkgdown 1.3.0.
                                            
+  
                                            Site built with pkgdown 1.4.1.
                                            
 
                                            
+
       
                                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/data.timssAusTwn.html b/docs/reference/data.timssAusTwn.html
index e134d96..4964d2a 100644
--- a/docs/reference/data.timssAusTwn.html
+++ b/docs/reference/data.timssAusTwn.html
@@ -8,21 +8,25 @@
 
 Dataset TIMSS 2011 of Australian and Taiwanese Students — data.timssAusTwn • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
                                              
         
                                            • 
   
-    
+    
      
   
 
                                              • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
                                              
@@ -108,17 +114,16 @@ 
                                              Dataset TIMSS 2011 of Australian and Taiwanese Students
                                              
     
                                              
 
     
                                              
-    
     
                                              Mathematics items of TIMSS 2011 of 1773 Australian and
 Taiwanese students. The dataset data.timssAusTwn contains raw
 responses while data.timssAusTwn.scored contains scored item
 responses.
                                              
-    
     
                                              
 
-    
                                              data(data.timssAusTwn)
-data(data.timssAusTwn.scored)
                                              
-        
+    
                                              data(data.timssAusTwn)
+data(data.timssAusTwn.scored)
                                              
+
+
     
                                              Format
                                              
 
     
                                              A data frame with 1773 observations on the following 14 variables.
                                              
@@ -136,23 +141,22 @@ 
                                              FormatIDCNTRY
                                              Country identifier
                                              
     
                                              ITSEX
                                              Gender
                                              
     
                                              IDBOOK
                                              Booklet identifier
                                              
-  
                                              
-    
+  
+
+
     
                                              See also
                                              
 
     
                                              http://www.edmeasurementsurveys.com/TAM/Tutorials/5PartialCredit.htm
                                              
 
                                              http://www.edmeasurementsurveys.com/TAM/Tutorials/6Population.htm
                                              
-    
 
     
                                              Examples
                                              
-    
                                              # NOT RUN {
-data(data.timssAusTwn)
+    
                                              data(data.timssAusTwn)
 raw_resp <- data.timssAusTwn
 
 #Recode data
 resp <- raw_resp[,1:11]
       #Column 12 is country code. Column 13 is gender code. Column 14 is Book ID.
-all.na <- rowMeans( is.na(resp) )==1
+all.na <- rowMeans( is.na(resp) )==1
         #Find records where all responses are missing.
 resp <- resp[!all.na,]              #Delete records with all missing responses
 resp[resp==20 | resp==21] <- 2      #TIMSS double-digit coding: "20" or "21" is a score of 2
@@ -164,95 +168,96 @@ 
                                              Examp
 #Score multiple-choice items        #"resp" contains raw responses for MC items.
 Scored <- resp
 Scored[,9] <- (resp[,9]==4)*1       #Key for item 9 is D.
-Scored[,c(1,2)] <- (resp[,c(1,2)]==2)*1  #Key for items 1 and 2 is B.
-Scored[,c(10,11)] <- (resp[,c(10,11)]==3)*1  #Key for items 10 and 11 is C.
+Scored[,c(1,2)] <- (resp[,c(1,2)]==2)*1  #Key for items 1 and 2 is B.
+Scored[,c(10,11)] <- (resp[,c(10,11)]==3)*1  #Key for items 10 and 11 is C.
 
 #Run IRT analysis for partial credit model (MML estimation)
-mod1 <- TAM::tam.mml(Scored)
+mod1 <- TAM::tam.mml(Scored)
 
 #Item parameters
 mod1$xsi
 
 #Thurstonian thresholds
-tthresh <- TAM::tam.threshold(mod1)
+tthresh <- TAM::tam.threshold(mod1)
 tthresh
 
-# }# NOT RUN {
+if (FALSE) {
 #Plot Thurstonian thresholds
-windows (width=8, height=7)
-par(ps=9)
-dotchart(t(tthresh), pch=19)
+windows (width=8, height=7)
+par(ps=9)
+dotchart(t(tthresh), pch=19)
 # plot expected response curves
-plot( mod1, ask=TRUE)
+plot( mod1, ask=TRUE)
 
 #Re-run IRT analysis in JML
-mod1.2 <- TAM::tam.jml(Scored)
-stats::var(mod1.2$WLE)
+mod1.2 <- TAM::tam.jml(Scored)
+stats::var(mod1.2$WLE)
 
 #Re-run the model with "not-reached" coded as incorrect.
 Scored2 <- Scored
-Scored2[is.na(Scored2)] <- 0
+Scored2[is.na(Scored2)] <- 0
 
 #Prepare anchor parameter values
-nparam <- length(mod1$xsi$xsi)
+nparam <- length(mod1$xsi$xsi)
 xsi <- mod1$xsi$xsi
-anchor <- matrix(c(seq(1,nparam),xsi), ncol=2)
+anchor <- matrix(c(seq(1,nparam),xsi), ncol=2)
 
 #Run IRT with item parameters anchored on mod1 values
-mod2 <- TAM::tam.mml(Scored2, xsi.fixed=anchor)
+mod2 <- TAM::tam.mml(Scored2, xsi.fixed=anchor)
 
 #WLE ability estimates
-ability <- TAM::tam.wle(mod2)
+ability <- TAM::tam.wle(mod2)
 ability
 
 #CTT statistics
-ctt <- TAM::tam.ctt(resp, ability$theta)
-write.csv(ctt,"TIMSS_CTT.csv")
+ctt <- TAM::tam.ctt(resp, ability$theta)
+write.csv(ctt,"TIMSS_CTT.csv")
 
 #plot histograms of ability and item parameters in the same graph
-windows(width=4.45, height=4.45, pointsize=12)
-layout(matrix(c(1,1,2),3,byrow=TRUE))
-layout.show(2)
-hist(ability$theta,xlim=c(-3,3),breaks=20)
-hist(tthresh,xlim=c(-3,3),breaks=20)
+windows(width=4.45, height=4.45, pointsize=12)
+layout(matrix(c(1,1,2),3,byrow=TRUE))
+layout.show(2)
+hist(ability$theta,xlim=c(-3,3),breaks=20)
+hist(tthresh,xlim=c(-3,3),breaks=20)
 
 #Extension
 #Score equivalence table
-dummy <- matrix(0,nrow=16,ncol=11)
-dummy[lower.tri(dummy)] <- 1
-dummy[12:16,c(3,4,7,8)][lower.tri(dummy[12:16,c(3,4,7,8)])]<-2
+dummy <- matrix(0,nrow=16,ncol=11)
+dummy[lower.tri(dummy)] <- 1
+dummy[12:16,c(3,4,7,8)][lower.tri(dummy[12:16,c(3,4,7,8)])]<-2
 
-mod3 <- TAM::tam.mml(dummy, xsi.fixed=anchor)
-wle3 <- TAM::tam.wle(mod3)
-# }
                                              
+mod3 <- TAM::tam.mml(dummy, xsi.fixed=anchor)
+wle3 <- TAM::tam.wle(mod3)
+}
                                              
   
   
 
 
+
       
                                              
       
                                              
   
                                              Developed by Alexander Robitzsch.
                                              
 
                                              
 
 
                                              
-  
                                              Site built with pkgdown 1.3.0.
                                              
+  
                                              Site built with pkgdown 1.4.1.
                                              
 
                                              
+
       
                                              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/designMatrices.html b/docs/reference/designMatrices.html
index d38ec59..a9fa607 100644
--- a/docs/reference/designMatrices.html
+++ b/docs/reference/designMatrices.html
@@ -8,21 +8,25 @@
 
 Generation of Design Matrices — designMatrices • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                                
         
                                              • 
   
-    
+    
      
   
 
                                                • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                                
@@ -105,32 +111,30 @@ 
                                                Generation of Design Matrices
                                                
     
                                                
 
     
                                                
-    
     
                                                Generate design matrices, and display them at console.
                                                
-    
     
                                                
 
-    
                                                designMatrices(modeltype=c("PCM", "RSM"), maxKi=NULL, resp=resp,
+    
                                                designMatrices(modeltype=c("PCM", "RSM"), maxKi=NULL, resp=resp,
     ndim=1, A=NULL, B=NULL, Q=NULL, R=NULL, constraint="cases",...)
 
 print.designMatrices(X, ...)
 
 designMatrices.mfr(resp, formulaA=~ item + item:step, facets=NULL,
-    constraint=c("cases", "items"), ndim=1, Q=NULL, A=NULL, B=NULL,
+    constraint=c("cases", "items"), ndim=1, Q=NULL, A=NULL, B=NULL,
     progress=FALSE)
 designMatrices.mfr2(resp, formulaA=~ item + item:step, facets=NULL,
-    constraint=c("cases", "items"), ndim=1, Q=NULL, A=NULL, B=NULL,
+    constraint=c("cases", "items"), ndim=1, Q=NULL, A=NULL, B=NULL,
     progress=FALSE)
 
 .A.matrix(resp, formulaA=~ item + item*step, facets=NULL,
-    constraint=c("cases", "items"), progress=FALSE, maxKi=NULL)
+    constraint=c("cases", "items"), progress=FALSE, maxKi=NULL)
 rownames.design(X)
 
 .A.PCM2( resp, Kitem=NULL, constraint="cases", Q=NULL)
    # generates ConQuest parametrization of partial credit model
 
 .A.PCM3( resp, Kitem=NULL ) # parametrization for A matrix in the dispersion model
                                                
-    
+
     
                                                Arguments
                                                
     
@@ -203,84 +207,77 @@ 
                                                Arg
       
                                                
-      
+      
                                                
     
                                                Display progress for creation of design matrices
                                                		
     
                                                
     
                                                …...
       
                                                Further arguments
                                                		
     
                                                
     
                                                
-    
+
     
                                                Details
                                                
 
     
                                                The function .A.PCM2 generates the Conquest parametrization
 of the partial credit model.
                                                
 
                                                The function .A.PCM3 generates the parametrization for the \(A\)
 design matrix in the dispersion model for ordered data (Andrich, 1982).
                                                
-    
     
                                                References
                                                
 
     
                                                Andrich, D. (1982). An extension of the Rasch model for ratings providing
 both location and dispersion parameters. Psychometrika, 47,
 105-113.
                                                
-    
     
                                                Note
                                                
 
     
                                                The function designMatrices.mfr2 handles multi-faceted design for
 items with differing number of response options.
                                                
-    
     
                                                See also
                                                
 
     
                                                See data.sim.mfr for some examples for creating design matrices.
 
                                                
-    
 
     
                                                Examples
                                                
-    
                                                # NOT RUN {
-###########################################################
+    
                                                ###########################################################
 # different parametrizations for ordered data
-data( data.gpcm )
+data( data.gpcm )
 resp <- data.gpcm
 
 # parametrization for partial credit model
-A1 <- TAM::designMatrices( resp=resp )$A
+A1 <- TAM::designMatrices( resp=resp )$A
 # item difficulty and threshold parametrization
-A2 <- TAM::.A.PCM2( resp )
+A2 <- TAM::.A.PCM2( resp )
 # dispersion model of Andrich (1982)
-A3 <- TAM::.A.PCM3( resp )
+A3 <- TAM::.A.PCM3( resp )
 # rating scale model
-A4 <- TAM::designMatrices( resp=resp, modeltype="RSM" )$A
-# }
                                                
+A4 <- TAM::designMatrices( resp=resp, modeltype="RSM" )$A
                                                
   
   
 
 
+
       
                                                
       
                                                
   
                                                Developed by Alexander Robitzsch.
                                                
 
                                                
 
 
                                                
-  
                                                Site built with pkgdown 1.3.0.
                                                
+  
                                                Site built with pkgdown 1.4.1.
                                                
 
                                                
+
       
                                                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/doparse.html b/docs/reference/doparse.html
index 1c53f9f..320eb12 100644
--- a/docs/reference/doparse.html
+++ b/docs/reference/doparse.html
@@ -8,21 +8,25 @@
 
 Parsing a String with <code>DO</code> Statements — doparse • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -58,6 +63,7 @@
 
 
 
+
   
 
   
@@ -82,7 +88,7 @@
       
-      
       
                                                  
         
                                                • 
   
-    
+    
      
   
 
                                                  • 
@@ -105,6 +110,7 @@
 
 
       
+
       
 
 
                                                  
@@ -116,7 +122,6 @@ 
                                                  Parsing a String with DO Statements
                                                  
     
                                                  
 
     
                                                  
-    
     
                                                  This function parses a string and expands this string in case of DO
 statements which are shortcuts for writing loops. The statement
 DO(n,m,k) increments an index from n to m in steps
@@ -129,11 +134,10 @@ 
                                                  Parsing a String with DO Statements
                                                  
  for (ii in 1:(K-1)){  for (jj in (ii+1):K) { ... } } 
                                                  
 can be written as DO2(1,K-1,1,%1,K,1
 ). See Example 2.
                                                  
-    
     
                                                  
 
     
                                                  doparse(model)
                                                  
-    
+
     
                                                  Arguments
                                                  
     
@@ -142,20 +146,17 @@ 
                                                  Arg
       
                                                  
     
                                                  A string with DO or DO2 statements.
                                                  		
     
                                                  
     
                                                  
-    
+
     
                                                  Value
                                                  
 
     
                                                  Parsed string in which DO statements are expanded.
                                                  
-    
     
                                                  See also
                                                  
 
     
                                                  This function is also used in lavaanify.IRT and
 tamaanify.
                                                  
-    
 
     
                                                  Examples
                                                  
-    
                                                  # NOT RUN {
-#############################################################################
+    
                                                  #############################################################################
 # EXAMPLE 1: doparse example
 #############################################################################
 
@@ -179,8 +180,8 @@ 
                                                  Examp
  "
 
 # process string
-out <- TAM::doparse(model)
-cat(out)
+out <- TAM::doparse(model)
+cat(out)
   ##    # items I1,...,I10 load on G
   ##       G=~ lamg1 * I1
   ##       G=~ lamg2 * I2
@@ -226,8 +227,8 @@ 
                                                  Examp
  DOEND
  "
 # process string
-out <- TAM::doparse(model)
-cat(out)
+out <- TAM::doparse(model)
+cat(out)
   ##       G=~ lamg1 * I1
   ##       G=~ lamg2 * I2
   ##       G=~ lamg3 * I3
@@ -238,37 +239,37 @@ 
                                                  Examp
   ##       I1 ~~ I4
   ##       I2 ~~ I3
   ##       I2 ~~ I4
-  ##       I3 ~~ I4
-# }
                                                  
+  ##       I3 ~~ I4
                                                  
   
   
 
 
+
       
                                                  
       
                                                  
   
                                                  Developed by Alexander Robitzsch.
                                                  
 
                                                  
 
 
                                                  
-  
                                                  Site built with pkgdown 1.3.0.
                                                  
+  
                                                  Site built with pkgdown 1.4.1.
                                                  
 
                                                  
+
       
                                                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 71c74c7..b40562e 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -8,21 +8,25 @@
 
 Function reference • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,10 +34,12 @@
 
 
 
+
 
 
 
 
+
 
 
 
@@ -44,6 +50,7 @@
 
 
 
+
   
 
   
@@ -68,7 +75,7 @@
       
-      
       
                                                    
         
                                                  • 
   
-    
+    
      
   
 
                                                    • 
@@ -91,6 +97,7 @@
 
 
       
+
       
 
 
                                                    
@@ -466,33 +473,39 @@ 
                                                    tam_irf_3pl() 
                                                    
+          
                                                    tamaan() summary(<tamaan>) print(<tamaan>) 
                                                    
         
-        
                                                    Item Response Function for the 3PL Model
                                                    
+        
                                                    Wrapper Function for TAM Language
                                                    
       
         
         
-          
                                                    tam_NA_pattern() tam_01_pattern() 
                                                    
+          
                                                    tamaanify() 
                                                    
         
-        
                                                    Missing Data Patterns
                                                    
+        
                                                    Function for Parsing TAM Input
                                                    
       
         
         
-          
                                                    tamaan() summary(<tamaan>) print(<tamaan>) 
                                                    
+          
                                                    tampv2datalist() 
                                                    
         
-        
                                                    Wrapper Function for TAM Language
                                                    
+        
                                                    Conversion of Plausible Value Object into Datalist
                                                    
       
         
         
-          
                                                    tamaanify() 
                                                    
+          
                                                    tam_downcode() 
                                                    
         
-        
                                                    Function for Parsing TAM Input
                                                    
+        
                                                    Downcoding an Item Response Dataset
                                                    
       
         
         
-          
                                                    tampv2datalist() 
                                                    
+          
                                                    tam_irf_3pl() 
                                                    
         
-        
                                                    Conversion of Plausible Value Object into Datalist
                                                    
+        
                                                    Item Response Function for the 3PL Model
                                                    
+      
+        
+        
+          
                                                    tam_NA_pattern() tam_01_pattern() 
                                                    
+        
+        
                                                    Missing Data Patterns
                                                    
       
         
         
@@ -518,19 +531,23 @@ 
                                                    Contents
                                                    
   
                                                    
 
 
+
       
                                                    
       
                                                    
   
                                                    Developed by Alexander Robitzsch.
                                                    
 
                                                    
 
 
                                                    
-  
                                                    Site built with pkgdown 1.3.0.
                                                    
+  
                                                    Site built with pkgdown 1.4.1.
                                                    
 
                                                    
+
       
                                                    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/lavaanify.IRT.html b/docs/reference/lavaanify.IRT.html
index 16449f4..1308b67 100644
--- a/docs/reference/lavaanify.IRT.html
+++ b/docs/reference/lavaanify.IRT.html
@@ -8,21 +8,25 @@
 
 Slight Extension of the <code>lavaan</code> Syntax, with Focus on Item Response Models — lavaanify.IRT • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -64,6 +69,7 @@
 
 
 
+
   
 
   
@@ -88,7 +94,7 @@
       
-      
       
                                                      
         
                                                    • 
   
-    
+    
      
   
 
                                                      • 
@@ -111,6 +116,7 @@
 
 
       
+
       
 
 
                                                      
@@ -122,10 +128,9 @@ 
                                                      Slight Extension of the lavaan Syntax, with Focus on Item Respo
     
                                                      
 
     
                                                      
-    
     
                                                      This functions slightly extends the lavaan
 syntax implemented in the lavaan package
-(see lavaan::lavaanify).
                                                      
+(see lavaan::lavaanify).
                                                      
 
                                                      Guessing and slipping parameters can be specified
 by using the operators ?=g1 and ?=s1,
 respectively.
                                                      
@@ -141,12 +146,11 @@ 
                                                      Slight Extension of the lavaan Syntax, with Focus on Item Respo
 model specification for variables which contains known measurement
 error (see Example 6). While MEASERR1 can be used for endogenous
 variables, MEASERR0 provides the specification for exogeneous variables.
                                                      
-    
     
                                                      
 
     
                                                      lavaanify.IRT(lavmodel, items=NULL, data=NULL, include.residuals=TRUE,
     doparse=TRUE)
                                                      
-    
+
     
                                                      Arguments
                                                      
     
@@ -175,7 +179,7 @@ 
                                                      Arg
 should be parsed for DO statements.
                                                      
     
     
                                                      
     
                                                      
-    
+
     
                                                      Value
                                                      
 
     
                                                      A list with following entries
                                                      
@@ -186,21 +190,19 @@ 
                                                      Value
                                                      
 
                                                      nonlin_syntable
                                                      Data frame with original and modified
 syntax if nonlinear factors are used.
                                                      
 
-    
     
                                                      See also
                                                      
 
-    
                                                      lavaan::lavaanify
                                                      
-    
                                                      See sirt::tam2mirt for
+    
                                                      lavaan::lavaanify
                                                      
+
+
                                                      See sirt::tam2mirt for
 converting objects of class tam into mirt
 objects.
                                                      
-
                                                      See sirt::lavaan2mirt
+
                                                      See sirt::lavaan2mirt
 for estimating models in the mirt package using lavaan syntax.
                                                      
 
                                                      See doparse for the DO and DO2 statements.
                                                      
-    
 
     
                                                      Examples
                                                      
-    
                                                      # NOT RUN {
-library(lavaan)
+    
                                                      library(lavaan)
 
 #############################################################################
 # EXAMPLE 1: lavaan syntax with guessing and slipping parameters
@@ -223,7 +225,7 @@ 
                                                      Examp
       "
 
 # process lavaan syntax
-lavpartable <- TAM::lavaanify.IRT(lavmodel)$lavpartable
+lavpartable <- TAM::lavaanify.IRT(lavmodel)$lavpartable
   ##     id lhs op rhs user group free ustart exo label eq.id unco
   ##  1   1   F=~  A1    1     1    1     NA   0           0    1
   ##  2   2   F=~  A2    1     1    2     NA   0     c     0    2
@@ -240,15 +242,15 @@ 
                                                      Examp
   ##  13 13  A3 ?=s1    1     1    0   0.01   0           0    0
   ##  14 14  A4 ?=g1    1     1   10     NA   0    c2     0   11
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: Usage of "__" and "?=" operators
 #############################################################################
 
-library(sirt)
-data(data.read, package="sirt")
+library(sirt)
+data(data.read, package="sirt")
 dat <- data.read
-items <- colnames(dat)
+items <- colnames(dat)
 
 lavmodel <- "
    F1=~ A1+A2+ A3+lam4*A4
@@ -268,9 +270,9 @@ 
                                                      Examp
    B1__B3 ~~ errB*B1__B3
    A2__A4 ~~ erra1__erra3*A2__A4
     "
-lav2 <- TAM::lavaanify.IRT( lavmodel, data=dat)
+lav2 <- TAM::lavaanify.IRT( lavmodel, data=dat)
 lav2$lavpartable
-cat( lav2$lavaan.syntax )
+cat( lav2$lavaan.syntax )
 
 #** simplified example
 lavmodel <- "
@@ -280,16 +282,16 @@ 
                                                      Examp
    F1 ~~ 1*F1
    F2 ~~ 1*F2
     "
-lav3 <- TAM::lavaanify.IRT( lavmodel, data=dat)
+lav3 <- TAM::lavaanify.IRT( lavmodel, data=dat)
 lav3$lavpartable
-cat( lav3$lavaan.syntax )
+cat( lav3$lavaan.syntax )
 
 #############################################################################
 # EXAMPLE 3: Nonlinear terms
 #############################################################################
 
 #*** define items
-items <- paste0("I",1:12)
+items <- paste0("I",1:12)
 
 #*** define lavaan model
 lavmodel <- "
@@ -310,11 +312,11 @@ 
                                                      Examp
     "
 
 #*** process lavaan syntax
-lav3 <- TAM::lavaanify.IRT( lavmodel, items=items)
+lav3 <- TAM::lavaanify.IRT( lavmodel, items=items)
 
 #*** inspect results
 lav3$lavpartable
-cat( lav3$lavaan.syntax )
+cat( lav3$lavaan.syntax )
 lav3$nonlin_syntable
 lav3$nonlin_factors
 
@@ -322,13 +324,13 @@ 
                                                      Examp
 # EXAMPLE 4: Using lavaanify.IRT for estimation with lavaan
 #############################################################################
 
-data(data.big5, package="sirt")
+data(data.big5, package="sirt")
 # extract first 10 openness items
-items <- which( substring( colnames(data.big5), 1, 1 )=="O"  )[1:10]
-dat <- as.data.frame( data.big5[, items ] )
+items <- which( substring( colnames(data.big5), 1, 1 )=="O"  )[1:10]
+dat <- as.data.frame( data.big5[, items ] )
   ##   > colnames(dat)
   ##    [1] "O3"  "O8"  "O13" "O18" "O23" "O28" "O33" "O38" "O43" "O48"
-apply(dat,2,var)  # variances
+apply(dat,2,var)  # variances
 
 #*** Model 1: Confirmatory factor analysis with one factor
 lavmodel <- "
@@ -336,11 +338,12 @@ 
                                                      Examp
    O ~~ 1*O
    "
 # process lavaan syntax
-res <- TAM::lavaanify.IRT( lavmodel, data=dat )
+res <- TAM::lavaanify.IRT( lavmodel, data=dat )
 # estimate lavaan model
-mod1 <- lavaan::lavaan( model=res$lavaan.syntax, data=dat)
-summary(mod1, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE )
-# }# NOT RUN {
+mod1 <- lavaan::lavaan( model=res$lavaan.syntax, data=dat)
+summary(mod1, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE )
+}
+
 #############################################################################
 # EXAMPLE 5: lavaanify.IRT with do statements
 #############################################################################
@@ -362,8 +365,8 @@ 
                                                      Examp
   F ~~ 0*B
   A ~~ 0*B
    "
-res <- TAM::lavaanify.IRT( lavmodel, items=paste("I",1:6) )
-cat(res$lavaan.syntax)
+res <- TAM::lavaanify.IRT( lavmodel, items=paste("I",1:6) )
+cat(res$lavaan.syntax)
 
 #############################################################################
 # EXAMPLE 6: Single indicator models with measurement error (MEASERR operator)
@@ -379,10 +382,10 @@ 
                                                      Examp
   ytrue ~~ ytrue
     "
 # observed items
-items <- c("y","x","z")
+items <- c("y","x","z")
 # lavaanify
-res <- TAM::lavaanify.IRT( lavmodel, items )
-cat(res$lavaan.syntax)
+res <- TAM::lavaanify.IRT( lavmodel, items )
+cat(res$lavaan.syntax)
   ##   > cat(res$lavaan.syntax)
   ##   ytrue~xtrue
   ##   ytrue~z
@@ -392,37 +395,37 @@ 
                                                      Examp
   ##   x~~0.35*x
   ##   xtrue~~xtrue
   ##   ytrue~~ytrue
-  ##   z~~z
-# }
                                                      
+  ##   z~~z
                                                      
   
                                                      
   
 
 
+
       
                                                      
       
                                                      
   
                                                      Developed by Alexander Robitzsch.
                                                      
 
                                                      
 
 
                                                      
-  
                                                      Site built with pkgdown 1.3.0.
                                                      
+  
                                                      Site built with pkgdown 1.4.1.
                                                      
 
                                                      
+
       
                                                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/msq.itemfit.html b/docs/reference/msq.itemfit.html
index a72e95f..6d67974 100644
--- a/docs/reference/msq.itemfit.html
+++ b/docs/reference/msq.itemfit.html
@@ -8,21 +8,25 @@
 
 Mean Squared Residual Based Item Fit Statistics (Infit, Outfit) — msq.itemfit • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -53,6 +58,7 @@
 
 
 
+
   
 
   
@@ -77,7 +83,7 @@
       
-      
       
                                                        
         
                                                      • 
   
-    
+    
      
   
 
                                                        • 
@@ -100,6 +105,7 @@
 
 
       
+
       
 
 
                                                        
@@ -111,7 +117,6 @@ 
                                                        Mean Squared Residual Based Item Fit Statistics (Infit, Outfit)
                                                        
     
                                                        
 
     
                                                        
-    
     
                                                        The function msq.itemfit computes computed the outfit and infit statistic
 for items or item groups. Contrary to tam.fit, the function
 msq.itemfit is not based on simulation from individual posterior distributions
@@ -119,19 +124,18 @@ 
                                                        Mean Squared Residual Based Item Fit Statistics (Infit, Outfit)
                                                        
 
                                                        The function msq.itemfit also computes the outfit and infit statistics
 but these are based on weighted likelihood estimates obtained from
 tam.wle.
                                                        
-    
     
                                                        
 
     
                                                        msq.itemfit( object, fitindices=NULL)
 
 # S3 method for msq.itemfit
-summary(object, file=NULL, ... )
+summary(object, file=NULL, ... )
 
 msq.itemfitWLE( tamobj, fitindices=NULL, ... )
 
 # S3 method for msq.itemfitWLE
-summary(object, file=NULL, ... )
                                                        
-    
+summary(object, file=NULL, ... )
                                                
+
     
                                                Arguments
                                                
     
@@ -155,11 +159,11 @@ 
                                                Arg
       
                                                
-      
+      
                                                
     
                                                Optional name of a file to which the summary should be written
                                                		
     
                                                
     
                                                …...
       
                                                Further arguments to be passed
                                                		
     
                                                
     
                                                
-    
+
     
                                                Value
                                                
 
     
                                                List with following entries
                                                
@@ -167,174 +171,173 @@ 
                                                Value
                                                
 
                                                summary_itemfit
                                                Summary statistics of outfit
 and infit
                                                
 
-    
     
                                                See also
                                                
 
     
                                                See also tam.fit for simulation based assessment of item fit.
                                                
-
                                                See also eRm::itemfit or mirt::itemfit.
                                                
-    
+
                                                See also eRm::itemfit or mirt::itemfit.
                                                
 
     
                                                Examples
                                                
-    
                                                # NOT RUN {
-#############################################################################
+    
                                                #############################################################################
 # EXAMPLE 1: Simulated data Rasch model
 #############################################################################
 
 #*** simulate data
-library(sirt)
-set.seed(9875)
+library(sirt)
+set.seed(9875)
 N <- 2000
 I <- 20
-b <- sample( seq( -2, 2, length=I ) )
-a <- rep( 1, I )
+b <- sample( seq( -2, 2, length=I ) )
+a <- rep( 1, I )
 # create some misfitting items
-a[c(1,3)] <- c(.5, 1.5 )
+a[c(1,3)] <- c(.5, 1.5 )
 # simulate data
-dat <- sirt::sim.raschtype( rnorm(N), b=b, fixed.a=a )
+dat <- sirt::sim.raschtype( rnorm(N), b=b, fixed.a=a )
 #*** estimate Rasch model
-mod1 <- TAM::tam.mml(resp=dat)
+mod1 <- TAM::tam.mml(resp=dat)
 # compute WLEs
-wmod1 <- TAM::tam.wle(mod1)$theta
+wmod1 <- TAM::tam.wle(mod1)$theta
 
 #--- item fit from "msq.itemfit" function
-fit1 <- TAM::msq.itemfit(mod1)
-summary( fit1 )
+fit1 <- TAM::msq.itemfit(mod1)
+summary( fit1 )
 
-# }# NOT RUN {
+if (FALSE) {
 #--- item fit using simulation in "tam.fit"
-fit0 <- TAM::tam.fit( mod1 )
-summary(fit0)
+fit0 <- TAM::tam.fit( mod1 )
+summary(fit0)
 
 #--- item fit based on WLEs
-fit2a <- TAM::msq.itemfitWLE( mod1 )
-summary(fit2a)
+fit2a <- TAM::msq.itemfitWLE( mod1 )
+summary(fit2a)
 
 #++ fit assessment in mirt package
-library(mirt)
-mod1b <- mirt::mirt( dat, model=1, itemtype="Rasch", verbose=TRUE )
-print(mod1b)
-sirt::mirt.wrapper.coef(mod1b)
-fmod1b <- mirt::itemfit(mod1b, Theta=as.matrix(wmod1,ncol=1),
+library(mirt)
+mod1b <- mirt::mirt( dat, model=1, itemtype="Rasch", verbose=TRUE )
+print(mod1b)
+sirt::mirt.wrapper.coef(mod1b)
+fmod1b <- mirt::itemfit(mod1b, Theta=as.matrix(wmod1,ncol=1),
                  Zh=TRUE, X2=FALSE, S_X2=FALSE )
-cbind( fit2a$fit_data, fmod1b )
+cbind( fit2a$fit_data, fmod1b )
 
 #++ fit assessment in eRm package
-library(eRm)
-mod1c <- eRm::RM( dat )
-summary(mod1c)
-eRm::plotPImap(mod1c)    # person-item map
-pmod1c <- eRm::person.parameter(mod1c)
-fmod1c <- eRm::itemfit(pmod1c)
-print(fmod1c)
-plot(fmod1c)
+library(eRm)
+mod1c <- eRm::RM( dat )
+summary(mod1c)
+eRm::plotPImap(mod1c)    # person-item map
+pmod1c <- eRm::person.parameter(mod1c)
+fmod1c <- eRm::itemfit(pmod1c)
+print(fmod1c)
+plot(fmod1c)
 
 #--- define some item groups for fit assessment
 
 # bases on evaluating the posterior
-fitindices <- rep( paste0("IG",c(1,2)), each=10)
-fit2 <- TAM::msq.itemfit( mod1, fitindices )
-summary(fit2)
+fitindices <- rep( paste0("IG",c(1,2)), each=10)
+fit2 <- TAM::msq.itemfit( mod1, fitindices )
+summary(fit2)
 
 # using WLEs
-fit2b <- TAM::msq.itemfitWLE( mod1, fitindices )
-summary(fit2b)
-# }# NOT RUN {
+fit2b <- TAM::msq.itemfitWLE( mod1, fitindices )
+summary(fit2b)
+}
+
 #############################################################################
 # EXAMPLE 2: data.read | fit statistics assessed for testlets
 #############################################################################
 
-library(sirt)
-data(data.read,package="sirt")
+library(sirt)
+data(data.read,package="sirt")
 dat <- data.read
 
 # fit Rasch model
-mod <- TAM::tam.mml( dat )
+mod <- TAM::tam.mml( dat )
 
 #***** item fit for each item
 # based on posterior
-res1 <- TAM::msq.itemfit( mod  )
-summary(res1)
+res1 <- TAM::msq.itemfit( mod  )
+summary(res1)
 # based on WLEs
-res2 <- TAM::msq.itemfitWLE( mod  )
-summary(res2)
+res2 <- TAM::msq.itemfitWLE( mod  )
+summary(res2)
 
 #***** item fit for item groups
 # define item groups
-fitindices <- substring( colnames(dat), 1, 1 )
+fitindices <- substring( colnames(dat), 1, 1 )
 # based on posterior
-res1 <- TAM::msq.itemfit( mod, fitindices )
-summary(res1)
+res1 <- TAM::msq.itemfit( mod, fitindices )
+summary(res1)
 # based on WLEs
-res2 <- TAM::msq.itemfitWLE( mod, fitindices )
-summary(res2)
+res2 <- TAM::msq.itemfitWLE( mod, fitindices )
+summary(res2)
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 3: Fit statistics for rater models
 #############################################################################
 
-library(sirt)
-data(data.ratings2, package="sirt")
+library(sirt)
+data(data.ratings2, package="sirt")
 dat <- data.ratings2
 
 # fit rater model "~ item*step + rater"
-mod <- TAM::tam.mml.mfr( resp=dat[, paste0( "k",1:5) ],
+mod <- TAM::tam.mml.mfr( resp=dat[, paste0( "k",1:5) ],
             facets=dat[, "rater", drop=FALSE],
             pid=dat$pid, formulaA=~ item*step + rater )
 
 # fit for parameter with "tam.fit" function
-fmod1a <- TAM::tam.fit( mod )
-fmod1b <- TAM::msq.itemfit( mod )
-summary(fmod1a)
-summary(fmod1b)
+fmod1a <- TAM::tam.fit( mod )
+fmod1b <- TAM::msq.itemfit( mod )
+summary(fmod1a)
+summary(fmod1b)
 
 # define item groups using pseudo items from object "mod"
-pseudo_items <- colnames(mod$resp)
-pss <- strsplit( pseudo_items, split="-" )
-item_parm <- unlist( lapply( pss, FUN=function(ll){ ll[1] } ) )
-rater_parm <- unlist( lapply( pss, FUN=function(ll){ ll[2] } ) )
+pseudo_items <- colnames(mod$resp)
+pss <- strsplit( pseudo_items, split="-" )
+item_parm <- unlist( lapply( pss, FUN=function(ll){ ll[1] } ) )
+rater_parm <- unlist( lapply( pss, FUN=function(ll){ ll[2] } ) )
 
 # fit for items with "msq.itemfit" functions
-res2a <- TAM::msq.itemfit( mod, item_parm )
-res2b <- TAM::msq.itemfitWLE( mod, item_parm )
-summary(res2a)
-summary(res2b)
+res2a <- TAM::msq.itemfit( mod, item_parm )
+res2b <- TAM::msq.itemfitWLE( mod, item_parm )
+summary(res2a)
+summary(res2b)
 
 # fit for raters
-res3a <- TAM::msq.itemfit( mod, rater_parm )
-res3b <- TAM::msq.itemfitWLE( mod, rater_parm )
-summary(res3a)
-summary(res3b)
-# }
                                                
+res3a <- TAM::msq.itemfit( mod, rater_parm )
+res3b <- TAM::msq.itemfitWLE( mod, rater_parm )
+summary(res3a)
+summary(res3b)
+}
                                                
   
   
 
 
+
       
                                                
       
                                                
   
                                                Developed by Alexander Robitzsch.
                                                
 
                                                
 
 
                                                
-  
                                                Site built with pkgdown 1.3.0.
                                                
+  
                                                Site built with pkgdown 1.4.1.
                                                
 
                                                
+
       
                                                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/plot.tam.html b/docs/reference/plot.tam.html
index 4579105..856f805 100644
--- a/docs/reference/plot.tam.html
+++ b/docs/reference/plot.tam.html
@@ -8,21 +8,25 @@
 
 Plot Function for Unidimensional Item Response Models — plot.tam • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                                  
         
                                                • 
   
-    
+    
      
   
 
                                                  • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                                  
@@ -106,30 +112,28 @@ 
                                                  Plot Function for Unidimensional Item Response Models
                                                  
     
                                                  
 
     
                                                  
-    
     
                                                  S3 plot method for objects of class tam, tam.mml
 or tam.mml.
                                                  
-    
     
                                                  
 
     
                                                  # S3 method for tam
-plot(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
+plot(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
                    groups_by_item=FALSE, wle=NULL, export=TRUE, export.type="png",
-                   export.args=list(), observed=TRUE, overlay=FALSE,
-                   ask=FALSE, package="lattice", fix.devices=TRUE, ...)
+                   export.args=list(), observed=TRUE, overlay=FALSE,
+                   ask=FALSE, package="lattice", fix.devices=TRUE, nnodes=100, ...)
 
 # S3 method for tam.mml
-plot(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
+plot(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
                        groups_by_item=FALSE, wle=NULL, export=TRUE, export.type="png",
-                       export.args=list(), observed=TRUE, overlay=FALSE,
-                       ask=FALSE,  package="lattice",  fix.devices=TRUE, ...)
+                       export.args=list(), observed=TRUE, overlay=FALSE,
+                       ask=FALSE,  package="lattice",  fix.devices=TRUE, nnodes=100, ...)
 
 # S3 method for tam.jml
-plot(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
+plot(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
                        groups_by_item=FALSE, wle=NULL, export=TRUE, export.type="png",
-                       export.args=list(), observed=TRUE, overlay=FALSE,
-                       ask=FALSE,  package="lattice", fix.devices=TRUE, ...)
                                                  
-    
+                       export.args=list(), observed=TRUE, overlay=FALSE,
+                       ask=FALSE,  package="lattice", fix.devices=TRUE, nnodes=100, ...)
+
     
                                                  Arguments
                                                  
     
@@ -181,7 +185,7 @@ 
                                                  Arg
       
                                                  
+grDevices::dev.new.
                                                  
@@ -213,63 +217,69 @@ 
                                                  Arg
      be saved.
                                                  
     
                                                  
-      
+      
+      
+    
+    
+      
                                                  
     
                                                  export.type
       
                                                  A string which indicates the type of the graphics export. For currently
   supported file types, see
-grDevices::dev.new.
                                                  		
     
                                                  
     
                                                  
       export.args
                                                  
     
                                                  …nnodes
                                                  Number of \(\theta\) points at which item response functions
+are evaluated
                                                  ...
       
                                                  Further arguments to be passed
                                                  		
     
                                                  
     
                                                  
-    
+
     
                                                  Details
                                                  
 
     
                                                  This plot method does not work for multidimensional item response
 models.
                                                  
-    
+    
                                                  Value
                                                  
+
+    
                                                  A plot and list of computed values for plot (if saved as an object)
                                                  
     
                                                  See also
                                                  
 
-    
                                                  See CDM::IRT.irfprobPlot
+    
                                                  See CDM::IRT.irfprobPlot
 for a general plot method.
                                                  
-    
 
     
                                                  Examples
                                                  
-    
                                                  # NOT RUN {
+    
                                                  if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
-mod <- TAM::tam.mml(data.sim.rasch)
+data(data.sim.rasch)
+mod <- TAM::tam.mml(data.sim.rasch)
 # expected response curves
-plot(mod, items=1:5, export=FALSE)
+plot(mod, items=1:5, export=FALSE)
+# export computed values
+out <- plot(mod, items=1:5, export=FALSE)
 # item response curves
-plot(mod, items=1:5, type="items", export=FALSE)
+plot(mod, items=1:5, type="items", export=FALSE)
 # plot with graphics package
-plot(mod, items=1:5, type="items", export=FALSE, ask=TRUE, package="graphics")
+plot(mod, items=1:5, type="items", export=FALSE, ask=TRUE, package="graphics")
 
 #############################################################################
 # EXAMPLE 2: Polytomous data
 #############################################################################
 
-data(data.Students, package="CDM")
-dat <- data.Students[, c("sc3","sc4", "mj1", "mj2" )]
-dat <- na.omit(dat)
+data(data.Students, package="CDM")
+dat <- data.Students[, c("sc3","sc4", "mj1", "mj2" )]
+dat <- na.omit(dat)
 dat[ dat[,1]==3, 1 ] <- 2   # modify data
 dat[ 1:20, 2 ] <- 4
 
 # estimate model
-mod1 <- TAM::tam.mml( dat )
+mod1 <- TAM::tam.mml( dat )
 # plot item response curves and expected response curves
-plot(mod1, type="items", export=FALSE)
-plot(mod1, type="expected", export=FALSE )
-# }
                                                  
+plot(mod1, type="items", export=FALSE)
+plot(mod1, type="expected", export=FALSE )
+}
                                                  
   
                                                  
   
 
 
+
       
                                                  
       
                                                  
   
                                                  Developed by Alexander Robitzsch.
                                                  
 
                                                  
 
 
                                                  
-  
                                                  Site built with pkgdown 1.3.0.
                                                  
+  
                                                  Site built with pkgdown 1.4.1.
                                                  
 
                                                  
+
       
                                                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/plotDevianceTAM.html b/docs/reference/plotDevianceTAM.html
index 262df7f..d2e3efa 100644
--- a/docs/reference/plotDevianceTAM.html
+++ b/docs/reference/plotDevianceTAM.html
@@ -8,21 +8,25 @@
 
 Deviance Plot for <span class="pkg">TAM</span> Objects — plotDevianceTAM • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-TAM Objects — plotDevianceTAM" />
 
+TAM Objects — plotDevianceTAM" />
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                                    
         
                                                  • 
   
-    
+    
      
   
 
                                                    • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                                    
@@ -105,13 +111,11 @@ 
                                                    Deviance Plot for TAM Objects
                                                    
     
                                                    
 
     
                                                    
-    
     
                                                    Plots the deviance change in every iteration.
                                                    
-    
     
                                                    
 
     
                                                    plotDevianceTAM(tam.obj, omitUntil=1, reverse=TRUE, change=TRUE)
                                                    
-    
+
     
                                                    Arguments
                                                    
     
@@ -135,28 +139,25 @@ 
                                                    Arg
 or the deviance should be plotted.
                                                    
     
     
                                                    
     
                                                    
-    
+
 
     
                                                    Examples
                                                    
-    
                                                    # NOT RUN {
-#############################################################################
+    
                                                    #############################################################################
 # EXAMPLE 1: deviance plot dichotomous data
 #############################################################################
-data(data.sim.rasch)
+data(data.sim.rasch)
 
 # 2PL model
-mod1 <- TAM::tam.mml.2pl(resp=data.sim.rasch )
+mod1 <- TAM::tam.mml.2pl(resp=data.sim.rasch )
 # plot deviance change
 plotDevianceTAM( mod1 )
 # plot deviance
-plotDevianceTAM( mod1, change=FALSE)
-# }
                                                    
+plotDevianceTAM( mod1, change=FALSE)
                                                    
   
   
 
 
+
       
                                                    
       
                                                    
   
                                                    Developed by Alexander Robitzsch.
                                                    
 
                                                    
 
 
                                                    
-  
                                                    Site built with pkgdown 1.3.0.
                                                    
+  
                                                    Site built with pkgdown 1.4.1.
                                                    
 
                                                    
+
       
                                                    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/predict.html b/docs/reference/predict.html
index 6ba5707..0ba1491 100644
--- a/docs/reference/predict.html
+++ b/docs/reference/predict.html
@@ -8,21 +8,25 @@
 
 Expected Values and Predicted Probabilities for Fitted <span class="pkg">TAM</span> Models — predict • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM Models — predict" />
 
+TAM Models — predict" />
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
                                                      
         
                                                    • 
   
-    
+    
      
   
 
                                                      • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
                                                      
@@ -108,23 +114,21 @@ 
                                                      Expected Values and Predicted Probabilities for Fitted TAM
     
                                                      
 
     
                                                      
-    
     
                                                      Extracts predicted values from the posterior distribution for models
 fitted in TAM.
                                                      
 
                                                      See CDM::predict
 for more details.
                                                      
-    
     
                                                      
 
-    
                                                      # S3 method for tam.mml
-predict(object, …)
+    
                                                      # S3 method for tam.mml
+predict(object, ...)
 
-# S3 method for tam.mml.3pl
-predict(object, …)
+# S3 method for tam.mml.3pl
+predict(object, ...)
+
+# S3 method for tamaan
+predict(object, ...)
                                                      
 
-# S3 method for tamaan
-predict(object, …)
                                                      
-    
     
                                                      Arguments
                                                      
     
@@ -134,58 +138,57 @@ 
                                                      Arg
 tam.mml.3pl or tamaan.
                                                      
     
     
                                                      
-      
+      
                                                      
     
                                                      …...
       
                                                      Further arguments to be passed
                                                      		
     
                                                      
     
                                                      
-    
+
     
                                                      Value
                                                      
 
     
                                                      List with entries for predicted values (expectations and probabilities)
 for each person and each item.
                                                      
 
                                                      See predict (CDM).
                                                      
-    
 
     
                                                      Examples
                                                      
-    
                                                      # NOT RUN {
-#############################################################################
+    
                                                      #############################################################################
 # EXAMPLE 1: Dichotomous data sim.rasch - predict method
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # 1PL estimation
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 # predict method
 prmod1 <- IRT.predict(mod1, data.sim.rasch)
-str(prmod1)
-# }
                                                      
+str(prmod1)
                                                      
   
   
 
 
+
       
                                                      
       
                                                      
   
                                                      Developed by Alexander Robitzsch.
                                                      
 
                                                      
 
 
                                                      
-  
                                                      Site built with pkgdown 1.3.0.
                                                      
+  
                                                      Site built with pkgdown 1.4.1.
                                                      
 
                                                      
+
       
                                                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.ctt.html b/docs/reference/tam.ctt.html
index 09cb569..6b0ee18 100644
--- a/docs/reference/tam.ctt.html
+++ b/docs/reference/tam.ctt.html
@@ -8,21 +8,25 @@
 
 Classical Test Theory Based Statistics and Plots — tam.ctt • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                                        
         
                                                      • 
   
-    
+    
      
   
 
                                                        • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                                        
@@ -106,10 +112,8 @@ 
                                                        Classical Test Theory Based Statistics and Plots
                                                        
     
                                                        
 
     
                                                        
-    
     
                                                        This function computes some item statistics based on classical test
 theory.
                                                        
-    
     
                                                        
 
     
                                                        tam.ctt(resp, wlescore=NULL, pvscores=NULL, group=NULL, progress=TRUE)
@@ -118,7 +122,7 @@ 
                                                        Classical Test Theory Based Statistics and Plots
                                                        
 
 plotctt( resp, theta, Ncuts=NULL, ask=FALSE, col.list=NULL,
        package="lattice", ... )
                                                        
-    
+
     
                                                        Arguments
                                                        
     
@@ -181,18 +185,17 @@ 
                                                        Arg
 or "graphics".
                                                        
     
     
                                                        
-      
+      
                                                        
     
                                                        …...
       
                                                        Further arguments to be passed.
                                                        		
     
                                                        
     
                                                        
-    
+
     
                                                        Details
                                                        
 
     
                                                        The functions tam.ctt2 and tam.ctt3 use Rcpp code
 and are slightly faster.
 However, only tam.ctt allows the input of wlescore and
 pvscores.
                                                        
-    
     
                                                        Value
                                                        
 
     
                                                        A data frame with following columns:
                                                        
@@ -211,90 +214,86 @@ 
                                                        Value
                                                        
 
                                                        M.PV
                                                        Mean of the PV of students in this item category
                                                        
 
                                                        SD.PV
                                                        Standard deviation of the PV of students in this item category
                                                        
 
-    
     
                                                        Note
                                                        
 
     
                                                        For dichotomously scored data, rpb.WLE is the ordinary point biserial
 correlation of an item and a test score (here the WLE).
                                                        
-    
     
                                                        See also
                                                        
 
     
                                                        http://www.edmeasurementsurveys.com/TAM/Tutorials/4CTT.htm
                                                        
-    
 
     
                                                        Examples
                                                        
-    
                                                        # NOT RUN {
+    
                                                        if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Multiple choice data data.mc
 #############################################################################
 
-data(data.mc)
+data(data.mc)
 # estimate Rasch model for scored data.mc data
-mod <- TAM::tam.mml( resp=data.mc$scored )
+mod <- TAM::tam.mml( resp=data.mc$scored )
 # estimate WLE
-w1 <- TAM::tam.wle( mod )
+w1 <- TAM::tam.wle( mod )
 # estimate plausible values
-set.seed(789)
-p1 <- TAM::tam.pv( mod, ntheta=500, normal.approx=TRUE )$pv
+set.seed(789)
+p1 <- TAM::tam.pv( mod, ntheta=500, normal.approx=TRUE )$pv
 
 # CTT results for raw data
-stat1 <- TAM::tam.ctt( resp=data.mc$raw, wlescore=w1$theta, pvscores=p1[,-1] )
-stat1a <- TAM::tam.ctt2( resp=data.mc$raw, wlescore=w1$theta )  # faster
-stat1b <- TAM::tam.ctt2( resp=data.mc$raw )  # only frequencies
-stat1c <- TAM::tam.ctt3( resp=data.mc$raw, wlescore=w1$theta )  # faster
+stat1 <- TAM::tam.ctt( resp=data.mc$raw, wlescore=w1$theta, pvscores=p1[,-1] )
+stat1a <- TAM::tam.ctt2( resp=data.mc$raw, wlescore=w1$theta )  # faster
+stat1b <- TAM::tam.ctt2( resp=data.mc$raw )  # only frequencies
+stat1c <- TAM::tam.ctt3( resp=data.mc$raw, wlescore=w1$theta )  # faster
 
 # plot empirical item response curves
 plotctt( resp=data.mc$raw, theta=w1$theta, Ncuts=5, ask=TRUE)
 # use graphics for plot
 plotctt( resp=data.mc$raw, theta=w1$theta, Ncuts=5, ask=TRUE, package="graphics")
 # change colors
-col.list <- c( "darkred",  "darkslateblue", "springgreen4", "darkorange",
+col.list <- c( "darkred",  "darkslateblue", "springgreen4", "darkorange",
                 "hotpink4", "navy" )
 plotctt( resp=data.mc$raw, theta=w1$theta, Ncuts=5, ask=TRUE,
         package="graphics", col.list=col.list )
 
 # CTT results for scored data
-stat2 <- TAM::tam.ctt( resp=data.mc$scored, wlescore=w1$theta, pvscores=p1[,-1] )
+stat2 <- TAM::tam.ctt( resp=data.mc$scored, wlescore=w1$theta, pvscores=p1[,-1] )
 
 # descriptive statistics for different groups
 # define group identifier
-group <- c( rep(1,70), rep(2,73) )
-stat3 <- TAM::tam.ctt( resp=data.mc$raw, wlescore=w1$theta, pvscores=p1[,-1], group=group)
-stat3a <- TAM::tam.ctt2( resp=data.mc$raw, wlescore=w1$theta,  group=group)
-# }
                                                        
+group <- c( rep(1,70), rep(2,73) )
+stat3 <- TAM::tam.ctt( resp=data.mc$raw, wlescore=w1$theta, pvscores=p1[,-1], group=group)
+stat3a <- TAM::tam.ctt2( resp=data.mc$raw, wlescore=w1$theta,  group=group)
+}
                                                        
   
   
 
 
+
       
                                                        
       
                                                        
   
                                                        Developed by Alexander Robitzsch.
                                                        
 
                                                        
 
 
                                                        
-  
                                                        Site built with pkgdown 1.3.0.
                                                        
+  
                                                        Site built with pkgdown 1.4.1.
                                                        
 
                                                        
+
       
                                                        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.fa.html b/docs/reference/tam.fa.html
index 25fc8bd..db3f3e8 100644
--- a/docs/reference/tam.fa.html
+++ b/docs/reference/tam.fa.html
@@ -8,21 +8,25 @@
 
 Bifactor Model and Exploratory Factor Analysis — tam.fa • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
                                                          
         
                                                        • 
   
-    
+    
      
   
 
                                                          • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
                                                          
@@ -108,17 +114,15 @@ 
                                                          Bifactor Model and Exploratory Factor Analysis
                                                          
     
                                                          
 
     
                                                          
-    
     
                                                          Estimates the bifactor model and exploratory factor analysis with
 marginal maximum likelihood estimation.
                                                          
 
                                                          This function is simply a wrapper to tam.mml or
 tam.mml.2pl.
                                                          
-    
     
                                                          
 
     
                                                          tam.fa(resp, irtmodel, dims=NULL, nfactors=NULL, pid=NULL,
-    pweights=NULL, verbose=TRUE, control=list())
                                                          
-    
+    pweights=NULL, verbose=TRUE, control=list())
+
     
                                                          Arguments
                                                          
     
@@ -169,7 +173,7 @@ 
                                                          Arg
 QMC=TRUE).
                                                          
     
     
                                                          
     
                                                          
-    
+
     
                                                          Details
                                                          
 
     
                                                          The exploratory factor analysis (irtmodel="efa"
@@ -188,7 +192,6 @@ 
                                                          Details
 estimated.
                                                          
 
                                                          For polytomous data, the generalized partial credit model is used. The loading
 structure is defined in the same way as for dichotomous data.
                                                          
-    
     
                                                          Value
                                                          
 
     
                                                          The same list entries as in tam.mml but in addition the
@@ -208,7 +211,6 @@ 
                                                          Value
                                                          
    the bifactor model for dichotomous item responses is also
    included (Green & Yang, 2009).
                                                          
 
-    
     
                                                          References
                                                          
 
     
                                                          Green, S. B., & Yang, Y. (2009). Reliability of summed item
@@ -225,109 +227,106 @@ 
                                                          R
 Psychometrika, 74, 145-154.
                                                          
 
                                                          Wang, W.-C., & Wilson, M. (2005). The Rasch testlet model.
 Applied Psychological Measurement, 29, 126-149.
                                                          
-    
     
                                                          See also
                                                          
 
     
                                                          For more details see tam.mml because tam.fa is just
 a wrapper for tam.mml.2pl and tam.mml.
                                                          
 
                                                          logLik.tam, anova.tam
                                                          
-    
 
     
                                                          Examples
                                                          
-    
                                                          # NOT RUN {
+    
                                                          if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Dataset reading from sirt package
 #############################################################################
 
-data(data.read,package="sirt")
+data(data.read,package="sirt")
 resp <- data.read
 
 #***
 # Model 1a: Exploratory factor analysis with 2 factors
-mod1a <- TAM::tam.fa( resp=resp, irtmodel="efa", nfactors=2  )
-summary(mod1a)
+mod1a <- TAM::tam.fa( resp=resp, irtmodel="efa", nfactors=2  )
+summary(mod1a)
 # varimax rotation
-stats::varimax(mod1a$B.stand)
+stats::varimax(mod1a$B.stand)
 # promax rotation
-stats::promax(mod1a$B.stand)
+stats::promax(mod1a$B.stand)
 # more rotations are included in the GPArotation package
-library(GPArotation)
+library(GPArotation)
 # geomin rotation oblique
-GPArotation::geominQ( mod1a$B.stand )
+GPArotation::geominQ( mod1a$B.stand )
 # quartimin rotation
-GPArotation::quartimin( mod1a$B.stand )
+GPArotation::quartimin( mod1a$B.stand )
 
 #***
 # Model 1b: Rasch testlet model with 3 testlets
-dims <- substring( colnames(resp),1,1 )     # define dimensions
-mod1b <- TAM::tam.fa( resp=resp, irtmodel="bifactor1", dims=dims )
-summary(mod1b)
+dims <- substring( colnames(resp),1,1 )     # define dimensions
+mod1b <- TAM::tam.fa( resp=resp, irtmodel="bifactor1", dims=dims )
+summary(mod1b)
 
 #***
 # Model 1c: Bifactor model
-mod1c <- TAM::tam.fa( resp=resp, irtmodel="bifactor2", dims=dims )
-summary(mod1c)
+mod1c <- TAM::tam.fa( resp=resp, irtmodel="bifactor2", dims=dims )
+summary(mod1c)
 
 #***
 # Model 1d: reestimate Model 1c but assume that items 3 and 5 do not load on
 #           specific factors
 dims1 <- dims
-dims1[c(3,5)] <- NA
-mod1d <- TAM::tam.fa( resp=resp, irtmodel="bifactor2", dims=dims1 )
-summary(mod1d)
+dims1[c(3,5)] <- NA
+mod1d <- TAM::tam.fa( resp=resp, irtmodel="bifactor2", dims=dims1 )
+summary(mod1d)
 
 #############################################################################
 # EXAMPLE 2: Polytomous data
 #############################################################################
 
-data(data.timssAusTwn.scored, package="TAM")
+data(data.timssAusTwn.scored, package="TAM")
 dat <- data.timssAusTwn.scored
-resp <- dat[, grep("M0", colnames(dat))]
+resp <- dat[, grep("M0", colnames(dat))]
 
 #***
 # Model 1a: Rasch testlet model with 2 testlets
-dims <- c( rep(1,5), rep(2,6))
-mod1a <- TAM::tam.fa( resp=resp, irtmodel="bifactor1", dims=dims )
-summary(mod1a)
+dims <- c( rep(1,5), rep(2,6))
+mod1a <- TAM::tam.fa( resp=resp, irtmodel="bifactor1", dims=dims )
+summary(mod1a)
 
 #***
 # Model 1b: Bifactor model
-mod1b <- TAM::tam.fa( resp=resp, irtmodel="bifactor2", dims=dims )
-summary(mod1b)
-# }
                                                          
+mod1b <- TAM::tam.fa( resp=resp, irtmodel="bifactor2", dims=dims )
+summary(mod1b)
+}
                                                          
   
   
 
 
+
       
                                                          
       
                                                          
   
                                                          Developed by Alexander Robitzsch.
                                                          
 
                                                          
 
 
                                                          
-  
                                                          Site built with pkgdown 1.3.0.
                                                          
+  
                                                          Site built with pkgdown 1.4.1.
                                                          
 
                                                          
+
       
                                                          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.fit.html b/docs/reference/tam.fit.html
index c497fa6..e043fbf 100644
--- a/docs/reference/tam.fit.html
+++ b/docs/reference/tam.fit.html
@@ -8,21 +8,25 @@
 
 Item Infit and Outfit Statistic — tam.fit • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                                                            
         
                                                          • 
   
-    
+    
      
   
 
                                                            • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                                                            
@@ -107,23 +113,21 @@ 
                                                            Item Infit and Outfit Statistic
                                                            
     
                                                            
 
     
                                                            
-    
     
                                                            The item infit and outfit statistic are calculated for
 objects of classes tam, tam.mml and
 tam.jml,  respectively.
                                                            
-    
     
                                                            
 
-    
                                                            tam.fit(tamobj, ...)
+    
                                                            tam.fit(tamobj, ...)
 
-tam.mml.fit(tamobj, FitMatrix=NULL, Nsimul=NULL,progress=TRUE,
-   useRcpp=TRUE, seed=NA, fit.facets=TRUE)
+tam.mml.fit(tamobj, FitMatrix=NULL, Nsimul=NULL,progress=TRUE,
+   useRcpp=TRUE, seed=NA, fit.facets=TRUE)
 
-tam.jml.fit(tamobj)
+tam.jml.fit(tamobj)
+
+# S3 method for tam.fit
+summary(object, file=NULL, ...)
                                                            
 
-# S3 method for tam.fit
-summary(object, file=NULL, …)
                                                            
-    
     
                                                            Arguments
                                                            
     
@@ -171,11 +175,11 @@ 
                                                            Arg
       
                                                            
-      
+      
                                                            
     
                                                            Optional file name for summary output
                                                            		
     
                                                            
     
                                                            …...
       
                                                            Further arguments to be passed
                                                            		
     
                                                            
     
                                                            
-    
+
     
                                                            Value
                                                            
 
     
                                                            In case of tam.mml.fit a data frame as entry itemfit
@@ -191,14 +195,12 @@ 
                                                            Value
                                                            
 
                                                            Infit_pholm
                                                            Significance \(p\) value for infit statistic,
 adjusted for multiple testing according to the Holm procedure
                                                            
 
-    
     
                                                            References
                                                            
 
     
                                                            Adams, R. J., & Wu, M. L. (2007). The mixed-coefficients multinomial logit model.
 A generalized form of the Rasch model. In M. von Davier & C. H. Carstensen (Eds.).
 Multivariate and mixture distribution Rasch models: Extensions and applications
 (pp. 55-76). New York: Springer.
                                                            
-    
     
                                                            See also
                                                            
 
     
                                                            Fit statistics can be also calculated by the function msq.itemfit
@@ -208,22 +210,20 @@ 
                                                            See a
 for models fitted with JML.
                                                            
 
                                                            See tam.personfit for computing person fit statistics.
                                                            
 
                                                            Item fit and person fit based on estimated person parameters can also be
-calculated using the sirt::pcm.fit function
+calculated using the sirt::pcm.fit function
 in the sirt package (see Example 1 and Example 2).
                                                            
-    
 
     
                                                            Examples
                                                            
-    
                                                            # NOT RUN {
-#############################################################################
+    
                                                            #############################################################################
 # EXAMPLE 1: Dichotomous data data.sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # estimate Rasch model
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 # item fit
-fit1 <- TAM::tam.fit( mod1 )
-summary(fit1)
+fit1 <- TAM::tam.fit( mod1 )
+summary(fit1)
   ##   > summary(fit1)
   ##      parameter Outfit Outfit_t Outfit_p Infit Infit_t Infit_p
   ##   1         I1  0.966   -0.409    0.171 0.996  -0.087   0.233
@@ -233,31 +233,31 @@ 
                                                            Examp
 
 #--------
 # infit and oufit based on estimated WLEs
-library(sirt)
+library(sirt)
 
 # estimate WLE
-wle <- TAM::tam.wle(mod1)
+wle <- TAM::tam.wle(mod1)
 # extract item parameters
 b1 <- - mod1$AXsi[, -1 ]
 # assess item fit and person fit
-fit1a <- sirt::pcm.fit(b=b1, theta=wle$theta, data.sim.rasch )
+fit1a <- sirt::pcm.fit(b=b1, theta=wle$theta, data.sim.rasch )
 fit1a$item       # item fit statistic
 fit1a$person     # person fit statistic
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: Partial credit model data.gpcm
 #############################################################################
 
-data( data.gpcm )
+data( data.gpcm )
 dat <- data.gpcm
 
 # estimate partial credit model in ConQuest parametrization 'item+item*step'
-mod2 <- TAM::tam.mml( resp=dat, irtmodel="PCM2" )
-summary(mod2)
+mod2 <- TAM::tam.mml( resp=dat, irtmodel="PCM2" )
+summary(mod2)
 # estimate item fit
-fit2 <- TAM::tam.fit(mod2)
-summary(fit2)
+fit2 <- TAM::tam.fit(mod2)
+summary(fit2)
 
 #=> The first three rows of the data frame correspond to the fit statistics
 #     of first three items Comfort, Work and Benefit.
@@ -265,11 +265,11 @@ 
                                                            Examp
 #--------
 # infit and oufit based on estimated WLEs
 # compute WLEs
-wle <- TAM::tam.wle(mod2)
+wle <- TAM::tam.wle(mod2)
 # extract item parameters
 b1 <- - mod2$AXsi[, -1 ]
 # assess fit
-fit1a <- sirt::pcm.fit(b=b1, theta=wle$theta, dat)
+fit1a <- sirt::pcm.fit(b=b1, theta=wle$theta, dat)
 fit1a$item
 
 #############################################################################
@@ -277,112 +277,112 @@ 
                                                            Examp
 #############################################################################
 
 # generate data with local dependence and User-defined fit statistics
-set.seed(4888)
+set.seed(4888)
 I <- 40        # 40 items
 N <- 1000       # 1000 persons
 
-delta <- seq(-2,2, len=I)
-theta <- stats::rnorm(N, 0, 1)
+delta <- seq(-2,2, len=I)
+theta <- stats::rnorm(N, 0, 1)
 # simulate data
-prob <- stats::plogis(outer(theta, delta, "-"))
-rand <- matrix( stats::runif(N*I), nrow=N, ncol=I)
+prob <- stats::plogis(outer(theta, delta, "-"))
+rand <- matrix( stats::runif(N*I), nrow=N, ncol=I)
 resp <- 1*(rand < prob)
-colnames(resp) <- paste("I", 1:I, sep="")
+colnames(resp) <- paste("I", 1:I, sep="")
 
 #induce some local dependence
-for (item in c(10, 20, 30)){
- #  20<!-- % of the item responses for items 11, 21 and 31 -->
+for (item in c(10, 20, 30)){
+ #  20
  #are made equal to the previous item
-  row <- round( stats::runif(0.2*N)*N + 0.5)
+  row <- round( stats::runif(0.2*N)*N + 0.5)
   resp[row, item+1] <- resp[row, item]
 }
 
 #run TAM
-mod1 <- TAM::tam.mml(resp)
+mod1 <- TAM::tam.mml(resp)
 
 #User-defined fit design matrix
-F <- array(0, dim=c(dim(mod1$A)[1], dim(mod1$A)[2], 6))
+F <- array(0, dim=c(dim(mod1$A)[1], dim(mod1$A)[2], 6))
 F[,,1] <- mod1$A[,,10] + mod1$A[,,11]
 F[,,2] <- mod1$A[,,12] + mod1$A[,,13]
 F[,,3] <- mod1$A[,,20] + mod1$A[,,21]
 F[,,4] <- mod1$A[,,22] + mod1$A[,,23]
 F[,,5] <- mod1$A[,,30] + mod1$A[,,31]
 F[,,6] <- mod1$A[,,32] + mod1$A[,,33]
-fit <- TAM::tam.fit(mod1, FitMatrix=F)
-summary(fit)
+fit <- TAM::tam.fit(mod1, FitMatrix=F)
+summary(fit)
 
 #############################################################################
 # EXAMPLE 4: Fit statistic testing for items with differing slopes
 #############################################################################
 
 #*** simulate data
-library(sirt)
-set.seed(9875)
+library(sirt)
+set.seed(9875)
 N <- 2000
 I <- 20
-b <- sample( seq( -2, 2, length=I ) )
-a <- rep( 1, I )
+b <- sample( seq( -2, 2, length=I ) )
+a <- rep( 1, I )
 # create some misfitting items
-a[c(1,3)] <- c(.5, 1.5 )
+a[c(1,3)] <- c(.5, 1.5 )
 # simulate data
-dat <- sirt::sim.raschtype( rnorm(N), b=b, fixed.a=a )
+dat <- sirt::sim.raschtype( rnorm(N), b=b, fixed.a=a )
 #*** estimate Rasch model
-mod1 <- TAM::tam.mml(resp=dat)
+mod1 <- TAM::tam.mml(resp=dat)
 #*** assess item fit by infit and outfit statistic
-fit1 <- TAM::tam.fit( mod1 )$itemfit
-round( cbind( "b"=mod1$item$AXsi_.Cat1, fit1$itemfit[,-1] )[1:7,], 3 )
+fit1 <- TAM::tam.fit( mod1 )$itemfit
+round( cbind( "b"=mod1$item$AXsi_.Cat1, fit1$itemfit[,-1] )[1:7,], 3 )
 
 #*** compute item fit statistic in mirt package
-library(mirt)
-library(sirt)
-mod1c <- mirt::mirt( dat, model=1, itemtype="Rasch", verbose=TRUE)
-print(mod1c)                      # model summary
-sirt::mirt.wrapper.coef(mod1c)    # estimated parameters
-fit1c <- mirt::itemfit(mod1c, method="EAP")    # model fit in mirt package
+library(mirt)
+library(sirt)
+mod1c <- mirt::mirt( dat, model=1, itemtype="Rasch", verbose=TRUE)
+print(mod1c)                      # model summary
+sirt::mirt.wrapper.coef(mod1c)    # estimated parameters
+fit1c <- mirt::itemfit(mod1c, method="EAP")    # model fit in mirt package
 # compare results of TAM and mirt
-dfr <- cbind( "TAM"=fit1, "mirt"=fit1c[,-c(1:2)] )
+dfr <- cbind( "TAM"=fit1, "mirt"=fit1c[,-c(1:2)] )
 
 # S-X2 item fit statistic (see also the output from mirt)
-library(CDM)
-sx2mod1 <- CDM::itemfit.sx2( mod1 )
-summary(sx2mod1)
+library(CDM)
+sx2mod1 <- CDM::itemfit.sx2( mod1 )
+summary(sx2mod1)
 
 # compare results of CDM and mirt
-sx2comp <-  cbind( sx2mod1$itemfit.stat[, c("S-X2", "p") ],
-                    dfr[, c("mirt.S_X2", "mirt.p.S_X2") ] )
-round(sx2comp, 3 )
-# }
                                                            
+sx2comp <-  cbind( sx2mod1$itemfit.stat[, c("S-X2", "p") ],
+                    dfr[, c("mirt.S_X2", "mirt.p.S_X2") ] )
+round(sx2comp, 3 )
+}
                                                            
   
   
 
 
+
       
                                                            
       
                                                            
   
                                                            Developed by Alexander Robitzsch.
                                                            
 
                                                            
 
 
                                                            
-  
                                                            Site built with pkgdown 1.3.0.
                                                            
+  
                                                            Site built with pkgdown 1.4.1.
                                                            
 
                                                            
+
       
                                                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.jml.html b/docs/reference/tam.jml.html
index c6f69c4..8affd7a 100644
--- a/docs/reference/tam.jml.html
+++ b/docs/reference/tam.jml.html
@@ -8,21 +8,25 @@
 
 Joint Maximum Likelihood Estimation — tam.jml • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                                                              
         
                                                            • 
   
-    
+    
      
   
 
                                                              • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                                                              
@@ -107,23 +113,21 @@ 
                                                              Joint Maximum Likelihood Estimation
                                                              
     
                                                              
 
     
                                                              
-    
     
                                                              This function estimate unidimensional
 item response models with joint maximum likelihood (JML,
  see e.g. Linacre, 1994).
                                                              
-    
     
                                                              
 
-    
                                                              tam.jml(resp, group=NULL, adj=.3, disattenuate=FALSE, bias=TRUE,
-    xsi.fixed=NULL, xsi.inits=NULL, theta.fixed=NULL, A=NULL, B=NULL, Q=NULL,
-    ndim=1, pweights=NULL, verbose=TRUE, control=list(), version=2)
+    
                                                              tam.jml(resp, group=NULL, adj=.3, disattenuate=FALSE, bias=TRUE,
+    xsi.fixed=NULL, xsi.inits=NULL, theta.fixed=NULL, A=NULL, B=NULL, Q=NULL,
+    ndim=1, pweights=NULL, verbose=TRUE, control=list(), version=2)
 
-# S3 method for tam.jml
-summary(object, file=NULL, …)
+# S3 method for tam.jml
+summary(object, file=NULL, ...)
+
+# S3 method for tam.jml
+logLik(object, ...)
                                                              
 
-# S3 method for tam.jml
-logLik(object, …)
                                                              
-    
     
                                                              Arguments
                                                              
     
@@ -222,11 +226,11 @@ 
                                                              Arg
 (only for summary.tam.jml function)
                                                              
     
     
                                                              
-      
+      
                                                              
     
                                                              …...
       
                                                              Further arguments to be passed
                                                              		
     
                                                              
     
                                                              
-    
+
     
                                                              Value
                                                              
 
     
                                                              A list with following entries
                                                              
@@ -255,93 +259,88 @@ 
                                                              Value
                                                              
 
                                                              resp.ind.list
                                                              Like resp.ind, only in the format of a list
                                                              
 
                                                              xsi.fixed
                                                              Fixed \(\xi\) item parameters
                                                              
 
                                                              control
                                                              Control list
                                                              
-
                                                              …
                                                              
+
                                                              ...
                                                              
 
-    
     
                                                              References
                                                              
 
     
                                                              Linacre, J. M. (1994). Many-Facet Rasch Measurement.
 Chicago: MESA Press.
                                                              
-    
     
                                                              Note
                                                              
 
     
                                                              This joint maximum likelihood estimation procedure should be
 compatible with Winsteps and Facets software, see also
 http://www.rasch.org/software.htm.
                                                              
-    
     
                                                              See also
                                                              
 
     
                                                              For estimating the same class of models with marginal
 maximum likelihood estimation see tam.mml.
                                                              
-    
 
     
                                                              Examples
                                                              
-    
                                                              # NOT RUN {
-#############################################################################
+    
                                                              #############################################################################
 # EXAMPLE 1: Dichotomous data
 #############################################################################
-data(data.sim.rasch)
-resp <- data.sim.rasch[1:700, seq( 1, 40, len=10)  ]  # subsample
+data(data.sim.rasch)
+resp <- data.sim.rasch[1:700, seq( 1, 40, len=10)  ]  # subsample
 # estimate the Rasch model with JML (function 'tam.jml')
-mod1a <- TAM::tam.jml(resp=resp)
-summary(mod1a)
-itemfit <- TAM::tam.fit(mod1a)$fit.item
+mod1a <- TAM::tam.jml(resp=resp)
+summary(mod1a)
+itemfit <- TAM::tam.fit(mod1a)$fit.item
 
 # compare results with Rasch model estimated by MML
-mod1b <- TAM::tam.mml(resp=resp )
+mod1b <- TAM::tam.mml(resp=resp )
 
 # plot estimated parameters
-plot( mod1a$xsi, mod1b$xsi$xsi, pch=16,
-    xlab=expression( paste( xi[i], " (JML)" )),
-    ylab=expression( paste( xi[i], " (MML)" )),
+plot( mod1a$xsi, mod1b$xsi$xsi, pch=16,
+    xlab=expression( paste( xi[i], " (JML)" )),
+    ylab=expression( paste( xi[i], " (MML)" )),
     main="Item Parameter Estimate Comparison")
-lines( c(-5,5), c(-5,5), col="gray" )
+lines( c(-5,5), c(-5,5), col="gray" )
 
 # Now, the adjustment pf .05 instead of the default .3 is used.
-mod1d <- TAM::tam.jml(resp=resp, adj=.05)
+mod1d <- TAM::tam.jml(resp=resp, adj=.05)
 # compare item parameters
-round( rbind( mod1a$xsi, mod1d$xsi ), 3 )
+round( rbind( mod1a$xsi, mod1d$xsi ), 3 )
   ##          [,1]   [,2]   [,3]   [,4]   [,5]  [,6]  [,7]  [,8]  [,9] [,10]
   ##   [1,] -2.076 -1.743 -1.217 -0.733 -0.338 0.147 0.593 1.158 1.570 2.091
   ##   [2,] -2.105 -1.766 -1.233 -0.746 -0.349 0.139 0.587 1.156 1.574 2.108
 
 # person parameters for persons with a score 0, 5 and 10
-pers1 <- data.frame( "score_adj0.3"=mod1a$PersonScore, "theta_adj0.3"=mod1a$theta,
+pers1 <- data.frame( "score_adj0.3"=mod1a$PersonScore, "theta_adj0.3"=mod1a$theta,
            "score_adj0.05"=mod1d$PersonScore, "theta_adj0.05"=mod1d$theta  )
-round( pers1[ c(698, 683, 608), ],3  )
+round( pers1[ c(698, 683, 608), ],3  )
   ##       score_adj0.3 theta_adj0.3 score_adj0.05 theta_adj0.05
   ##   698          0.3       -4.404          0.05        -6.283
   ##   683          5.0       -0.070          5.00        -0.081
   ##   608          9.7        4.315          9.95         6.179
 
-# }# NOT RUN {
+if (FALSE) {
 #*** item fit and person fit statistics
-fmod1a <- TAM::tam.jml.fit(mod1a)
-head(fmod1a$fit.item)
-head(fmod1a$fit.person)
+fmod1a <- TAM::tam.jml.fit(mod1a)
+head(fmod1a$fit.item)
+head(fmod1a$fit.person)
 
 #*** Models in which some item parameters are fixed
-xsi.fixed <- cbind( c(1,3,9,10), c(-2, -1.2, 1.6, 2 ) )
-mod1e <- TAM::tam.jml( resp=resp, xsi.fixed=xsi.fixed )
-summary(mod1e)
+xsi.fixed <- cbind( c(1,3,9,10), c(-2, -1.2, 1.6, 2 ) )
+mod1e <- TAM::tam.jml( resp=resp, xsi.fixed=xsi.fixed )
+summary(mod1e)
 
 #*** Model in which also some person parameters theta are fixed
 # fix theta parameters of persons 2, 3, 4 and 33 to values -2.9, ...
-theta.fixed <- cbind( c(2,3,4,33), c( -2.9, 4, -2.9, -2.9 ) )
-mod1g <- TAM::tam.jml( resp=resp, xsi.fixed=xsi.fixed, theta.fixed=theta.fixed )
+theta.fixed <- cbind( c(2,3,4,33), c( -2.9, 4, -2.9, -2.9 ) )
+mod1g <- TAM::tam.jml( resp=resp, xsi.fixed=xsi.fixed, theta.fixed=theta.fixed )
 # look at estimated results
-ind.person <- c( 1:5, 30:33 )
-cbind( mod1g$WLE, mod1g$errorWLE )[ind.person,]
+ind.person <- c( 1:5, 30:33 )
+cbind( mod1g$WLE, mod1g$errorWLE )[ind.person,]
 
 #############################################################################
 # EXAMPLE 2: Partial credit model
 #############################################################################
 
-data(data.gpcm)
+data(data.gpcm)
 # JML estimation
-mod2 <- TAM::tam.jml(resp=data.gpcm)
+mod2 <- TAM::tam.jml(resp=data.gpcm)
 mod2$xsi     # extract item parameters
-summary(mod2)
+summary(mod2)
 tam.fit(mod2)    # item and person infit/outfit statistic
 
 #############################################################################
@@ -349,7 +348,7 @@ 
                                                              Examp
 #            data.ex10; see also Example 10 in tam.mml
 #############################################################################
 
-data(data.ex10)
+data(data.ex10)
 dat <- data.ex10
   ## > head(dat)
   ##  pid rater I0001 I0002 I0003 I0004 I0005
@@ -361,32 +360,32 @@ 
                                                              Examp
 
 facets <- dat[, "rater", drop=FALSE ] # define facet (rater)
 pid <- dat$pid      # define person identifier (a person occurs multiple times)
-resp <- dat[, -c(1:2) ]        # item response data
+resp <- dat[, -c(1:2) ]        # item response data
 formulaA <- ~ item * rater      # formula
 
 # use MML function only to restructure data and input obtained design matrices
 # and processed response data to tam.jml (-> therefore use only 2 iterations)
-mod3a <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA,
-             pid=dat$pid,  control=list(maxiter=2) )
+mod3a <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA,
+             pid=dat$pid,  control=list(maxiter=2) )
 
 # use modified response data mod3a$resp and design matrix mod3a$A
 resp1 <- mod3a$resp
 # JML
-mod3b <- TAM::tam.jml( resp=resp1, A=mod3a$A, control=list(maxiter=200) )
+mod3b <- TAM::tam.jml( resp=resp1, A=mod3a$A, control=list(maxiter=200) )
 
 #############################################################################
 # EXAMPLE 4: Multi faceted model with some anchored item and person parameters
 #############################################################################
 
-data(data.exJ03)
+data(data.exJ03)
 resp <- data.exJ03$resp
 X <- data.exJ03$X
 
 #*** (0) preprocess data with TAM::tam.mml.mfr
-mod0 <- TAM::tam.mml.mfr( resp=resp, facets=X, pid=X$rater,
+mod0 <- TAM::tam.mml.mfr( resp=resp, facets=X, pid=X$rater,
                 formulaA=~ leader + item + step,
-                control=list(maxiter=2) )
-summary(mod0)
+                control=list(maxiter=2) )
+summary(mod0)
 
 #*** (1) estimation with tam.jml (no parameter fixings)
 
@@ -394,59 +393,58 @@ 
                                                              Examp
 resp1 <- mod0$resp
 A1 <- mod0$A
 # estimate model with tam.jml
-mod1 <- TAM::tam.jml( resp=resp1, A=A1, control=list( Msteps=4, maxiter=100 ) )
-summary(mod1)
+mod1 <- TAM::tam.jml( resp=resp1, A=A1, control=list( Msteps=4, maxiter=100 ) )
+summary(mod1)
 
 #*** (2) fix some parameters (persons and items)
 
 # look at indices in mod1$xsi
 mod1$xsi
 # fix step parameters
-xsi.index1 <- cbind( 21:25, c( -2.44, 0.01, -0.15, 0.01,  1.55 ) )
+xsi.index1 <- cbind( 21:25, c( -2.44, 0.01, -0.15, 0.01,  1.55 ) )
 # fix some item parameters of items 1,2,3,6 and 13
-xsi.index2 <- cbind( c(1,2,3,6,13), c(-2,-1,-1,-1.32, -1 ) )
-xsi.index <- rbind( xsi.index1, xsi.index2 )
+xsi.index2 <- cbind( c(1,2,3,6,13), c(-2,-1,-1,-1.32, -1 ) )
+xsi.index <- rbind( xsi.index1, xsi.index2 )
 # fix some theta parameters of persons 1, 15 and 20
-theta.fixed <- cbind(  c(1,15,20), c(0.4, 1, 0 ) )
+theta.fixed <- cbind(  c(1,15,20), c(0.4, 1, 0 ) )
 # estimate model, theta.fixed only works for version=1
-mod2 <- TAM::tam.jml( resp=resp1, A=A1, xsi.fixed=xsi.fixed, theta.fixed=theta.fixed,
-            control=list( Msteps=4, maxiter=100) )
-summary(mod2)
-cbind( mod2$WLE, mod2$errorWLE )
-# }
                                                              
+mod2 <- TAM::tam.jml( resp=resp1, A=A1, xsi.fixed=xsi.fixed, theta.fixed=theta.fixed,
+            control=list( Msteps=4, maxiter=100) )
+summary(mod2)
+cbind( mod2$WLE, mod2$errorWLE )
+}
                                                              
   
   
 
 
+
       
                                                              
       
                                                              
   
                                                              Developed by Alexander Robitzsch.
                                                              
 
                                                              
 
 
                                                              
-  
                                                              Site built with pkgdown 1.3.0.
                                                              
+  
                                                              Site built with pkgdown 1.4.1.
                                                              
 
                                                              
+
       
                                                              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.latreg.html b/docs/reference/tam.latreg.html
index 2b50c41..958b665 100644
--- a/docs/reference/tam.latreg.html
+++ b/docs/reference/tam.latreg.html
@@ -8,21 +8,25 @@
 
 Latent Regression Model — tam.latreg • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -53,6 +58,7 @@
 
 
 
+
   
 
   
@@ -77,7 +83,7 @@
       
-      
       
                                                                
         
                                                              • 
   
-    
+    
      
   
 
                                                                • 
@@ -100,6 +105,7 @@
 
 
       
+
       
 
 
                                                                
@@ -111,7 +117,6 @@ 
                                                                Latent Regression Model
                                                                
     
                                                                
 
     
                                                                
-    
     
                                                                This function fits a latent regression model \(\bold{\theta}=\bold{Y}
 \bold{\beta} + \bold{\varepsilon}\).
 Only the individual likelihood evaluated at a
@@ -119,20 +124,19 @@ 
                                                                Latent Regression Model
                                                                
 tam.mml a multivariate normal distribution is posed
 on the residual distribution. Plausible values can be drawn by subsequent
 application of tam.pv (see Example 1).
                                                                
-    
     
                                                                
 
-    
                                                                tam.latreg(like, theta=NULL, Y=NULL, group=NULL, formulaY=NULL, dataY=NULL,
-   beta.fixed=FALSE, beta.inits=NULL, variance.fixed=NULL,
-   variance.inits=NULL, est.variance=TRUE, pweights=NULL, pid=NULL,
-   userfct.variance=NULL, variance.Npars=NULL, verbose=TRUE, control=list())
+    
                                                                tam.latreg(like, theta=NULL, Y=NULL, group=NULL, formulaY=NULL, dataY=NULL,
+   beta.fixed=FALSE, beta.inits=NULL, variance.fixed=NULL,
+   variance.inits=NULL, est.variance=TRUE, pweights=NULL, pid=NULL,
+   userfct.variance=NULL, variance.Npars=NULL, verbose=TRUE, control=list())
 
-# S3 method for tam.latreg
-summary(object,file=NULL,…)
+# S3 method for tam.latreg
+summary(object,file=NULL,...)
+
+# S3 method for tam.latreg
+print(x,...)
                                                                
 
-# S3 method for tam.latreg
-print(x,…)
                                                                
-    
     
                                                                Arguments
                                                                
     
@@ -160,7 +164,7 @@ 
                                                                Arg
       
                                                                
+     e. g. female*race or I(age^2).
                                                                
@@ -241,178 +245,50 @@ 
                                                                Arg
       
                                                                
-      
+      
                                                                
     
                                                                formulaY
       
                                                                An R formula for latent regression. Transformations of predictors
      in \(Y\) (included in dataY) can be easily specified,
-     e. g. female*race or I(age^2).
                                                                		
     
                                                                
     
                                                                
       dataY
                                                                Object of class tam.latreg
                                                                		
     
                                                                
     
                                                                …...
       
                                                                Further arguments to be passed
                                                                		
     
                                                                
     
                                                                
-    
+
     
                                                                Value
                                                                
 
     
                                                                Subset of values of tam.mml
                                                                
-    
     
                                                                See also
                                                                
 
     
                                                                See also tam.pv for plausible value imputation.
                                                                
-    
 
     
                                                                Examples
                                                                
-    
                                                                # NOT RUN {
-#############################################################################
-# EXAMPLE 1: Unidimensional latent regression model with fitted IRT model in
-#            sirt package
-#############################################################################
-
-library(sirt)
-data(data.pisaRead, package="sirt")
-dat <- data.pisaRead$data
-
-items <- grep("R4", colnames(dat), value=TRUE )    # select test items from data
-# define testlets
-testlets <- substring( items, 1, 4 )
-itemcluster <- match( testlets, unique(testlets) )
-# fit Rasch copula model (only few iterations)
-mod <- sirt::rasch.copula2( dat[,items], itemcluster=itemcluster, mmliter=5)
-# extract individual likelihood
-like1 <- IRT.likelihood( mod )
-# fit latent regression model in TAM
-Y <- dat[, c("migra", "hisei", "female") ]
-mod2 <- TAM::tam.latreg( like1, theta=attr(like1, "theta"), Y=Y, pid=dat$idstud )
-summary(mod2)
-# plausible value imputation
-pv2 <- TAM::tam.pv( mod2 )
-# create list of imputed datasets
-Y <- dat[, c("idstud", "idschool", "female", "hisei", "migra") ]
-pvnames <- c("PVREAD")
-datlist <- TAM::tampv2datalist( pv2, pvnames=pvnames, Y=Y, Y.pid="idstud")
-
-#--- fit some models
-library(mice)
-library(miceadds)
-# convert data list into a mice object
-mids1 <- miceadds::datalist2mids( datlist )
-# perform an ANOVA
-mod3a <- with( mids1, stats::lm(PVREAD ~ hisei*migra) )
-summary( pool( mod3a ))
-mod3b <- miceadds::mi.anova( mids1, "PVREAD ~ hisei*migra" )
-
-#############################################################################
-# EXAMPLE 2: data.pisaRead - fitted IRT model in mirt package
-#############################################################################
-
-library(sirt)
-library(mirt)
-
-data(data.pisaRead, package="sirt")
-dat <- data.pisaRead$data
-
-# define dataset with item responses
-items <- grep("R4", colnames(dat), value=TRUE )
-resp <- dat[,items]
-# define dataset with covariates
-X <- dat[, c("female","hisei","migra") ]
-
-# fit 2PL model in mirt
-mod <- mirt::mirt( resp, 1, itemtype="2PL", verbose=TRUE)
-print(mod)
-# extract coefficients
-sirt::mirt.wrapper.coef(mod)
-
-# extract likelihood
-like <- IRT.likelihood(mod)
-str(like)
-
-# fit latent regression model in TAM
-mod2 <- TAM::tam.latreg( like, Y=X, pid=dat$idstud )
-summary(mod2)
-# plausible value imputation
-pv2 <- TAM::tam.pv( mod2, samp.regr=TRUE, nplausible=5 )
-# create list of imputed datasets
-X <- dat[, c("idstud", "idschool", "female", "hisei", "migra") ]
-pvnames <- c("PVREAD")
-datlist <- TAM::tampv2datalist( pv2, pvnames=pvnames, Y=X, Y.pid="idstud")
-str(datlist)
-
-# regression using semTools package
-library(semTools)
-lavmodel <- "
-   PVREAD ~ hisei + female
-           "
-mod1a <- semTools::sem.mi( lavmodel, datlist)
-summary(mod1a, standardized=TRUE, rsquare=TRUE)
-
-#############################################################################
-# EXAMPLE 3: data.Students - fitted confirmatory factor analysis in lavaan
-#############################################################################
-
-library(CDM)
-library(sirt)
-library(lavaan)
-
-data(data.Students, package="CDM")
-dat <- data.Students
-vars <- scan(what="character", nlines=1)
-   urban female sc1 sc2 sc3 sc4 mj1 mj2 mj3 mj4
-dat <- dat[, vars]
-dat <- na.omit(dat)
-
-# fit confirmatory factor analysis in lavaan
-lavmodel <- "
-   SC=~ sc1__sc4
-   SC ~~ 1*SC
-   MJ=~ mj1__mj4
-   MJ ~~ 1*MJ
-   SC ~~ MJ
-        "
-# process lavaan syntax
-res <- TAM::lavaanify.IRT( lavmodel, dat )
-# fit lavaan CFA model
-mod1 <- lavaan::cfa( res$lavaan.syntax, dat, std.lv=TRUE)
-summary(mod1, standardized=TRUE, fit.measures=TRUE )
-# extract likelihood
-like1 <- TAM::IRTLikelihood.cfa( dat, mod1 )
-str(like1)
-# fit latent regression model in TAM
-X <- dat[, c("urban","female") ]
-mod2 <- TAM::tam.latreg( like1, Y=X  )
-summary(mod2)
-# plausible value imputation
-pv2 <- TAM::tam.pv( mod2, samp.regr=TRUE, normal.approx=TRUE )
-# create list of imputed datasets
-Y <- dat[, c("urban", "female" ) ]
-pvnames <- c("PVSC", "PVMJ")
-datlist <- TAM::tampv2datalist( pv2, pvnames=pvnames, Y=Y )
-str(datlist)
-# }
-
                                                                
+    
                                                                
   
   
 
 
+
       
                                                                
       
                                                                
   
                                                                Developed by Alexander Robitzsch.
                                                                
 
                                                                
 
 
                                                                
-  
                                                                Site built with pkgdown 1.3.0.
                                                                
+  
                                                                Site built with pkgdown 1.4.1.
                                                                
 
                                                                
+
       
                                                                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.linking.html b/docs/reference/tam.linking.html
index d45c3cd..557d603 100644
--- a/docs/reference/tam.linking.html
+++ b/docs/reference/tam.linking.html
@@ -8,21 +8,25 @@
 
 Linking of Fitted Unidimensional Item Response Models in <span class="pkg">TAM</span> — tam.linking • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM — tam.linking" />
 
+TAM — tam.linking" />
 
 
@@ -53,6 +58,7 @@
 
 
 
+
   
 
   
@@ -77,7 +83,7 @@
       
-      
       
                                                                  
         
                                                                • 
   
-    
+    
      
   
 
                                                                  • 
@@ -100,6 +105,7 @@
 
 
       
+
       
 
 
                                                                  
@@ -111,7 +117,6 @@ 
                                                                  Linking of Fitted Unidimensional Item Response Models in T
     
                                                                  
 
     
                                                                  
-    
     
                                                                  Performs linking of fitted unidimensional item response models in TAM
 according to the Stocking-Lord and the Haebara method (Kolen & Brennan, 2014;
 Gonzales & Wiberg, 2017).
@@ -119,27 +124,26 @@ 
                                                                  Linking of Fitted Unidimensional Item Response Models in T
 (method="chain") or a joint linking approach (method="joint")
 comprising all pairwise linkings.
                                                                  
 
                                                                  The linking of two studies is implemented in the tam_linking_2studies function.
                                                                  
-    
     
                                                                  
 
-    
                                                                  tam.linking(tamobj_list, type="Hae", method="joint", pow_rob_hae=1, theta=NULL,
-   wgt=NULL, wgt_sd=2, fix.slope=FALSE, elim_items=NULL, verbose=TRUE)
+    
                                                                  tam.linking(tamobj_list, type="Hae", method="joint", pow_rob_hae=1, theta=NULL,
+   wgt=NULL, wgt_sd=2, fix.slope=FALSE, elim_items=NULL, verbose=TRUE)
 
-# S3 method for tam.linking
-summary(object, file=NULL, …)
+# S3 method for tam.linking
+summary(object, file=NULL, ...)
 
-# S3 method for tam.linking
-print(x, …)
+# S3 method for tam.linking
+print(x, ...)
 
-tam_linking_2studies( B1, AXsi1, guess1, B2, AXsi2, guess2, theta, wgt, type,
-    M1=0, SD1=1, M2=0, SD2=1, fix.slope=FALSE, pow_rob_hae=1)
+tam_linking_2studies( B1, AXsi1, guess1, B2, AXsi2, guess2, theta, wgt, type,
+    M1=0, SD1=1, M2=0, SD2=1, fix.slope=FALSE, pow_rob_hae=1)
 
-# S3 method for tam_linking_2studies
-summary(object, file=NULL, …)
+# S3 method for tam_linking_2studies
+summary(object, file=NULL, ...)
+
+# S3 method for tam_linking_2studies
+print(x, ...)
                                                                  
 
-# S3 method for tam_linking_2studies
-print(x, …)
                                                                  
-    
     
                                                                  Arguments
                                                                  
     
@@ -163,7 +167,7 @@ 
                                                                  Arg
     
     
                                                                  
-      
+      
@@ -201,7 +205,7 @@ 
                                                                  Arg
       
                                                                  
-      
+      
@@ -245,7 +249,7 @@ 
                                                                  Arg
       
                                                                  
     
                                                                  
       theta
                                                                  Grid of \(\theta\) points. The default is seq(-6,6,len=101).
                                                                  Grid of \(\theta\) points. The default is seq(-6,6,len=101).
                                                                  		
     
                                                                  
     
                                                                  
       wgt
                                                                  A file name in which the summary output will be written
                                                                  		
     
                                                                  
     
                                                                  …...
       
                                                                  Further arguments to be passed
                                                                  		
     
                                                                  
     
                                                                  Standard deviation of second study
                                                                  		
     
                                                                  
     
                                                                  
-    
+
     
                                                                  Details
                                                                  
 
     
                                                                  The Haebara linking is defined by minimizing the loss function
@@ -254,7 +258,6 @@ 
                                                                  Details
 $$\sum_i \sum_k \int \left ( P_{ik} ( \theta ) - P_{ik}^\ast ( \theta ) \right )^p  $$
 with a power \(p\) (defined in pow_rob_hae) smaller than 2. He, Cui and
 Osterlind (2015) consider \(p=1\).
                                                                  
-    
     
                                                                  Value
                                                                  
 
     
                                                                  List containing entries
                                                                  
@@ -265,7 +268,6 @@ 
                                                                  Value
                                                                  
  
                                                                  trafo_items
                                                                  Transformation constants for item parameters
                                                                  
  
                                                                  trafo_persons
                                                                  Transformation constants for person parameters
                                                                  
 
-    
     
                                                                  References
                                                                  
 
     
                                                                  Battauz, M. (2015). equateIRT: An R package for IRT test equating.
@@ -279,110 +281,108 @@ 
                                                                  R
 Methods and practices. New York, Springer.
                                                                  
 
                                                                  Weeks, J. P. (2010). plink: An R package for linking mixed-format tests
 using IRT-based methods. Journal of Statistical Software, 35(12), 1-33.
                                                                  
-    
     
                                                                  See also
                                                                  
 
     
                                                                  Linking or equating of item response models can be also conducted with plink
 (Weeks, 2010), equate, equateIRT (Battauz, 2015), equateMultiple,
 kequate and irteQ packages.
                                                                  
-
                                                                  See also the sirt::linking.haberman,
-sirt::invariance.alignment and
-sirt::linking.haebara functions
+
                                                                  See also the sirt::linking.haberman,
+sirt::invariance.alignment and
+sirt::linking.haebara functions
 in the sirt package.
                                                                  
-    
 
     
                                                                  Examples
                                                                  
-    
                                                                  # NOT RUN {
+    
                                                                  if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Linking dichotomous data with the 2PL model
 #############################################################################
 
-data(data.ex16)
+data(data.ex16)
 dat <- data.ex16
-items <- colnames(dat)[-c(1,2)]
+items <- colnames(dat)[-c(1,2)]
 
 # fit grade 1
-rdat1 <- TAM::tam_remove_missings( dat[ dat$grade==1, ], items=items )
-mod1 <- TAM::tam.mml.2pl( resp=rdat1$resp[, rdat1$items], pid=rdat1$dat$idstud )
-summary(mod1)
+rdat1 <- TAM::tam_remove_missings( dat[ dat$grade==1, ], items=items )
+mod1 <- TAM::tam.mml.2pl( resp=rdat1$resp[, rdat1$items], pid=rdat1$dat$idstud )
+summary(mod1)
 
 # fit grade 2
-rdat2 <- TAM::tam_remove_missings( dat[ dat$grade==2, ], items=items )
-mod2 <- TAM::tam.mml.2pl( resp=rdat2$resp[, rdat2$items], pid=rdat2$dat$idstud )
-summary(mod2)
+rdat2 <- TAM::tam_remove_missings( dat[ dat$grade==2, ], items=items )
+mod2 <- TAM::tam.mml.2pl( resp=rdat2$resp[, rdat2$items], pid=rdat2$dat$idstud )
+summary(mod2)
 
 # fit grade 3
-rdat3 <- TAM::tam_remove_missings( dat[ dat$grade==3, ], items=items )
-mod3 <- TAM::tam.mml.2pl( resp=rdat3$resp[, rdat3$items], pid=rdat3$dat$idstud )
-summary(mod3)
+rdat3 <- TAM::tam_remove_missings( dat[ dat$grade==3, ], items=items )
+mod3 <- TAM::tam.mml.2pl( resp=rdat3$resp[, rdat3$items], pid=rdat3$dat$idstud )
+summary(mod3)
 
 # define list of fitted models
-tamobj_list <- list( mod1, mod2, mod3 )
+tamobj_list <- list( mod1, mod2, mod3 )
 
 #-- link item response models
-lmod <- TAM::tam.linking( tamobj_list)
-summary(lmod)
+lmod <- TAM::tam.linking( tamobj_list)
+summary(lmod)
 
 # estimate WLEs based on transformed item parameters
 parm_list <- lmod$parameters_list
 
 # WLE grade 1
-arglist <- list( resp=mod1$resp, B=parm_list[[1]]$B, AXsi=parm_list[[1]]$AXsi )
-wle1 <- TAM::tam.mml.wle(tamobj=arglist)
+arglist <- list( resp=mod1$resp, B=parm_list[[1]]$B, AXsi=parm_list[[1]]$AXsi )
+wle1 <- TAM::tam.mml.wle(tamobj=arglist)
 
 # WLE grade 2
-arglist <- list( resp=mod2$resp, B=parm_list[[2]]$B, AXsi=parm_list[[2]]$AXsi )
-wle2 <- TAM::tam.mml.wle(tamobj=arglist)
+arglist <- list( resp=mod2$resp, B=parm_list[[2]]$B, AXsi=parm_list[[2]]$AXsi )
+wle2 <- TAM::tam.mml.wle(tamobj=arglist)
 
 # WLE grade 3
-arglist <- list( resp=mod3$resp, B=parm_list[[3]]$B, AXsi=parm_list[[3]]$AXsi )
-wle3 <- TAM::tam.mml.wle(tamobj=arglist)
+arglist <- list( resp=mod3$resp, B=parm_list[[3]]$B, AXsi=parm_list[[3]]$AXsi )
+wle3 <- TAM::tam.mml.wle(tamobj=arglist)
 
 # compare result with chain linking
-lmod1b <- TAM::tam.linking(tamobj_list)
-summary(lmod1b)
+lmod1b <- TAM::tam.linking(tamobj_list)
+summary(lmod1b)
 
 #-- linking with some eliminated items
 
 # remove three items from first group and two items from third group
-elim_items <- list( c("A1", "E2","F1"), NULL,  c("F1","F2") )
-lmod2 <- TAM::tam.linking(tamobj_list, elim.items=elim.items)
-summary(lmod2)
+elim_items <- list( c("A1", "E2","F1"), NULL,  c("F1","F2") )
+lmod2 <- TAM::tam.linking(tamobj_list, elim.items=elim.items)
+summary(lmod2)
 
 #-- Robust Haabara linking
-lmod3 <- TAM::tam.linking(tamobj_list, type="RobHae", pow_rob_hae=1)
-summary(lmod3)
+lmod3 <- TAM::tam.linking(tamobj_list, type="RobHae", pow_rob_hae=1)
+summary(lmod3)
 
 #############################################################################
 # EXAMPLE 2: Linking polytomous data with the partial credit model
 #############################################################################
 
-data(data.ex17)
+data(data.ex17)
 dat <- data.ex17
 
-items <- colnames(dat)[-c(1,2)]
+items <- colnames(dat)[-c(1,2)]
 
 # fit grade 1
-rdat1 <- TAM::tam_remove_missings( dat[ dat$grade==1, ], items=items )
-mod1 <- TAM::tam.mml.2pl( resp=rdat1$resp[, rdat1$items], pid=rdat1$dat$idstud )
-summary(mod1)
+rdat1 <- TAM::tam_remove_missings( dat[ dat$grade==1, ], items=items )
+mod1 <- TAM::tam.mml.2pl( resp=rdat1$resp[, rdat1$items], pid=rdat1$dat$idstud )
+summary(mod1)
 
 # fit grade 2
-rdat2 <- TAM::tam_remove_missings( dat[ dat$grade==2, ], items=items )
-mod2 <- TAM::tam.mml.2pl( resp=rdat2$resp[, rdat2$items], pid=rdat2$dat$idstud )
-summary(mod2)
+rdat2 <- TAM::tam_remove_missings( dat[ dat$grade==2, ], items=items )
+mod2 <- TAM::tam.mml.2pl( resp=rdat2$resp[, rdat2$items], pid=rdat2$dat$idstud )
+summary(mod2)
 
 # fit grade 3
-rdat3 <- TAM::tam_remove_missings( dat[ dat$grade==3, ], items=items )
-mod3 <- TAM::tam.mml.2pl( resp=rdat3$resp[, rdat3$items], pid=rdat3$dat$idstud )
-summary(mod3)
+rdat3 <- TAM::tam_remove_missings( dat[ dat$grade==3, ], items=items )
+mod3 <- TAM::tam.mml.2pl( resp=rdat3$resp[, rdat3$items], pid=rdat3$dat$idstud )
+summary(mod3)
 
 # list of fitted TAM models
-tamobj_list <- list( mod1, mod2, mod3 )
+tamobj_list <- list( mod1, mod2, mod3 )
 
 #-- linking: fix slope because partial credit model is fitted
-lmod <- TAM::tam.linking( tamobj_list, fix.slope=TRUE)
-summary(lmod)
+lmod <- TAM::tam.linking( tamobj_list, fix.slope=TRUE)
+summary(lmod)
 
 # WLEs can be estimated in the same way as in Example 1.
 
@@ -390,116 +390,115 @@ 
                                                                  Examp
 # EXAMPLE 3: Linking dichotomous data with the multiple group 2PL models
 #############################################################################
 
-data(data.ex16)
+data(data.ex16)
 dat <- data.ex16
-items <- colnames(dat)[-c(1,2)]
+items <- colnames(dat)[-c(1,2)]
 
 # fit grade 1
-rdat1 <- TAM::tam_remove_missings( dat[ dat$grade==1, ], items=items )
+rdat1 <- TAM::tam_remove_missings( dat[ dat$grade==1, ], items=items )
 # create some grouping variable
-group <- ( seq( 1, nrow( rdat1$dat ) ) %% 3 ) + 1
-mod1 <- TAM::tam.mml.2pl( resp=rdat1$resp[, rdat1$items], pid=rdat1$dat$idstud, group=group)
-summary(mod1)
+group <- ( seq( 1, nrow( rdat1$dat ) ) %% 3 ) + 1
+mod1 <- TAM::tam.mml.2pl( resp=rdat1$resp[, rdat1$items], pid=rdat1$dat$idstud, group=group)
+summary(mod1)
 
 # fit grade 2
-rdat2 <- TAM::tam_remove_missings( dat[ dat$grade==2, ], items=items )
+rdat2 <- TAM::tam_remove_missings( dat[ dat$grade==2, ], items=items )
 group <- 1*(rdat2$dat$dat$idstud > 500)
-mod2 <- TAM::tam.mml.2pl( resp=rdat2$resp[, rdat2$items], pid=rdat2$dat$dat$idstud, group=group)
-summary(mod2)
+mod2 <- TAM::tam.mml.2pl( resp=rdat2$resp[, rdat2$items], pid=rdat2$dat$dat$idstud, group=group)
+summary(mod2)
 
 # fit grade 3
-rdat3 <- TAM::tam_remove_missings( dat[ dat$grade==3, ], items=items )
-mod3 <- TAM::tam.mml.2pl( resp=rdat3$resp[, rdat3$items], pid=rdat3$dat$idstud )
-summary(mod3)
+rdat3 <- TAM::tam_remove_missings( dat[ dat$grade==3, ], items=items )
+mod3 <- TAM::tam.mml.2pl( resp=rdat3$resp[, rdat3$items], pid=rdat3$dat$idstud )
+summary(mod3)
 
 # define list of fitted models
-tamobj_list <- list( mod1, mod2, mod3 )
+tamobj_list <- list( mod1, mod2, mod3 )
 
 #-- link item response models
-lmod <- TAM::tam.linking( tamobj_list)
+lmod <- TAM::tam.linking( tamobj_list)
 
 #############################################################################
 # EXAMPLE 4: Linking simulated dichotomous data with two groups
 #############################################################################
 
-library(sirt)
+library(sirt)
 
 #*** simulate data
 N <- 3000  # number of persons
 I <- 30    # number of items
-b <- seq(-2,2, length=I)
+b <- seq(-2,2, length=I)
 # data for group 1
-dat1 <- sirt::sim.raschtype( rnorm(N, mean=0, sd=1), b=b )
+dat1 <- sirt::sim.raschtype( rnorm(N, mean=0, sd=1), b=b )
 # data for group 2
-dat2 <- sirt::sim.raschtype( rnorm(N, mean=1, sd=.6), b=b )
+dat2 <- sirt::sim.raschtype( rnorm(N, mean=1, sd=.6), b=b )
 
 # fit group 1
-mod1 <- TAM::tam.mml.2pl( resp=dat1 )
-summary(mod1)
+mod1 <- TAM::tam.mml.2pl( resp=dat1 )
+summary(mod1)
 
 # fit group 2
-mod2 <- TAM::tam.mml.2pl( resp=dat2 )
-summary(mod2)
+mod2 <- TAM::tam.mml.2pl( resp=dat2 )
+summary(mod2)
 
 # define list of fitted models
-tamobj_list <- list( mod1, mod2 )
+tamobj_list <- list( mod1, mod2 )
 
 #-- link item response models
-lmod <- TAM::tam.linking( tamobj_list)
-summary(lmod)
+lmod <- TAM::tam.linking( tamobj_list)
+summary(lmod)
 
 # estimate WLEs based on transformed item parameters
 parm_list <- lmod$parameters_list
 
 # WLE grade 1
-arglist <- list( resp=mod1$resp, B=parm_list[[1]]$B, AXsi=parm_list[[1]]$AXsi )
-wle1 <- TAM::tam.mml.wle(tamobj=arglist)
+arglist <- list( resp=mod1$resp, B=parm_list[[1]]$B, AXsi=parm_list[[1]]$AXsi )
+wle1 <- TAM::tam.mml.wle(tamobj=arglist)
 
 # WLE grade 2
-arglist <- list( resp=mod2$resp, B=parm_list[[2]]$B, AXsi=parm_list[[2]]$AXsi )
-wle2 <- TAM::tam.mml.wle(tamobj=arglist)
-summary(wle1)
-summary(wle2)
+arglist <- list( resp=mod2$resp, B=parm_list[[2]]$B, AXsi=parm_list[[2]]$AXsi )
+wle2 <- TAM::tam.mml.wle(tamobj=arglist)
+summary(wle1)
+summary(wle2)
 
 # estimation with linked and fixed item parameters for group 2
 B <- parm_list[[2]]$B
-xsi.fixed <- cbind( 1:I, -parm_list[[2]]$AXsi[,2] )
-mod2f <- TAM::tam.mml( resp=dat2, B=B, xsi.fixed=xsi.fixed )
-summary(mod2f)
-# }
                                                                  
+xsi.fixed <- cbind( 1:I, -parm_list[[2]]$AXsi[,2] )
+mod2f <- TAM::tam.mml( resp=dat2, B=B, xsi.fixed=xsi.fixed )
+summary(mod2f)
+}
                                                                  
   
   
 
 
+
       
                                                                  
       
                                                                  
   
                                                                  Developed by Alexander Robitzsch.
                                                                  
 
                                                                  
 
 
                                                                  
-  
                                                                  Site built with pkgdown 1.3.0.
                                                                  
+  
                                                                  Site built with pkgdown 1.4.1.
                                                                  
 
                                                                  
+
       
                                                                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.mml.3pl.html b/docs/reference/tam.mml.3pl.html
index 267882b..51e992d 100644
--- a/docs/reference/tam.mml.3pl.html
+++ b/docs/reference/tam.mml.3pl.html
@@ -8,21 +8,25 @@
 
 3PL Structured Item Response Model in <span class="pkg">TAM</span> — tam.mml.3pl • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM — tam.mml.3pl" />
 
+TAM — tam.mml.3pl" />
 
@@ -39,6 +43,7 @@
 
 
 
+
 
 
 
@@ -49,6 +54,7 @@
 
 
 
+
   
 
   
@@ -73,7 +79,7 @@
       
-      
       
                                                                    
         
                                                                  • 
   
-    
+    
      
   
 
                                                                    • 
@@ -96,6 +101,7 @@
 
 
       
+
       
 
 
                                                                    
@@ -107,32 +113,30 @@ 
                                                                    3PL Structured Item Response Model in TAM
                                                                    
     
                                                                    
 
     
                                                                    
-    
     
                                                                    This estimates a 3PL model with design matrices for item slopes and
 item intercepts. Discrete distributions of the latent variables are
 allowed which can be log-linearly smoothed.
                                                                    
-    
     
                                                                    
 
-    
                                                                    tam.mml.3pl(resp, Y=NULL, group=NULL, formulaY=NULL, dataY=NULL, ndim=1,
-  pid=NULL, xsi.fixed=NULL, xsi.inits=NULL, xsi.prior=NULL,
-  beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL, variance.inits=NULL,
-  est.variance=TRUE, A=NULL, notA=FALSE, Q=NULL, Q.fixed=NULL, E=NULL,
-  gammaslope.des="2PL", gammaslope=NULL, gammaslope.fixed=NULL,
-  est.some.slopes=TRUE, gammaslope.constr.V=NULL, gammaslope.constr.c=NULL,
-  gammaslope.center.index=NULL,  gammaslope.center.value=NULL,
-  gammaslope.prior=NULL, userfct.gammaslope=NULL, gammaslope.constr.Npars=0,
-  est.guess=NULL, guess=rep(0, ncol(resp)),
-  guess.prior=NULL, max.guess=0.5, skillspace="normal", theta.k=NULL,
-  delta.designmatrix=NULL, delta.fixed=NULL, delta.inits=NULL, pweights=NULL,
-  item.elim=TRUE, verbose=TRUE, control=list(), Edes=NULL )
-
-# S3 method for tam.mml.3pl
-summary(object,file=NULL,…)
+    
                                                                    tam.mml.3pl(resp, Y=NULL, group=NULL, formulaY=NULL, dataY=NULL, ndim=1,
+  pid=NULL, xsi.fixed=NULL, xsi.inits=NULL, xsi.prior=NULL,
+  beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL, variance.inits=NULL,
+  est.variance=TRUE, A=NULL, notA=FALSE, Q=NULL, Q.fixed=NULL, E=NULL,
+  gammaslope.des="2PL", gammaslope=NULL, gammaslope.fixed=NULL,
+  est.some.slopes=TRUE, gammaslope.constr.V=NULL, gammaslope.constr.c=NULL,
+  gammaslope.center.index=NULL,  gammaslope.center.value=NULL,
+  gammaslope.prior=NULL, userfct.gammaslope=NULL, gammaslope.constr.Npars=0,
+  est.guess=NULL, guess=rep(0, ncol(resp)),
+  guess.prior=NULL, max.guess=0.5, skillspace="normal", theta.k=NULL,
+  delta.designmatrix=NULL, delta.fixed=NULL, delta.inits=NULL, pweights=NULL,
+  item.elim=TRUE, verbose=TRUE, control=list(), Edes=NULL )
+
+# S3 method for tam.mml.3pl
+summary(object,file=NULL,...)
+
+# S3 method for tam.mml.3pl
+print(x,...)
                                                                    
 
-# S3 method for tam.mml.3pl
-print(x,…)
                                                                    
-    
     
                                                                    Arguments
                                                                    
     
@@ -154,7 +158,7 @@ 
                                                                    Arg
       
                                                                    
+     e. g. female*race or I(age^2).
                                                                    
@@ -319,7 +323,7 @@ 
                                                                    Arg
     
                                                                    
@@ -391,11 +395,11 @@ 
                                                                    Arg
       
                                                                    
-      
+      
                                                                    
     
                                                                    formulaY
       
                                                                    An R formula for latent regression. Transformations of predictors
      in \(Y\) (included in dataY) can be easily specified,
-     e. g. female*race or I(age^2).
                                                                    		
     
                                                                    
     
                                                                    
       dataY
                                                                    
       guess.prior
       
                                                                    Item-specific prior distribution for guessing parameters \(c_i\). It is
-     assumed that \(c_i \sim N( a_i, b_i)\). The first column
+     assumed that \(c_i \sim Beta(a_i, b_i)\). The first column
      in gammaslope.prior is \(a_i\), the second is \(b_i\).
                                                                    		
     
                                                                    
     
                                                                    Object of class tam.mml.3pl
                                                                    		
     
                                                                    
     
                                                                    …...
       
                                                                    Further arguments to be passed
                                                                    		
     
                                                                    
     
                                                                    
-    
+
     
                                                                    Details
                                                                    
 
     
                                                                    The item response model for item \(i\) and category \(h\) and no guessing
@@ -423,7 +427,6 @@ 
                                                                    Details
 von Davier (2008). The design matrix \(D_{\delta}\) can be specified in
 delta.designmatrix. The theta grid \(\bold{\theta}\) of the skill space
 can be defined in theta.k.
                                                                    
-    
     
                                                                    Value
                                                                    
 
     
                                                                    The same output as in tam.mml and additional entries
                                                                    
@@ -436,7 +439,6 @@ 
                                                                    Value
                                                                    
  
                                                                    se.guess
                                                                    Standard errors \(c\) guessing parameters
                                                                    
 
 
-    
     
                                                                    References
                                                                    
 
     
                                                                    Formann, A. K. (1992). Linear logistic latent class analysis for polytomous data.
@@ -447,800 +449,52 @@ 
                                                                    R
 New York: Springer.
                                                                    
 
                                                                    Xu, X., & von Davier, M. (2008). Fitting the structured general diagnostic
 model to NAEP data. ETS Research Report ETS RR-08-27. Princeton, ETS.
                                                                    
-    
     
                                                                    Note
                                                                    
 
     
                                                                    The implementation of the model builds on pieces work of Anton Formann.
 See http://www.antonformann.at/ for more information.
                                                                    
-    
     
                                                                    See also
                                                                    
 
     
                                                                    See also tam.mml.
                                                                    
-
                                                                    See the CDM::slca function in the CDM package
+
                                                                    See the CDM::slca function in the CDM package
 for a similar method.
                                                                    
 
                                                                    logLik.tam, anova.tam
                                                                    
-    
 
     
                                                                    Examples
                                                                    
-    
                                                                    # NOT RUN {
-#############################################################################
-# EXAMPLE 1: Dichotomous data | data.sim.rasch
-#############################################################################
-
-data(data.sim.rasch)
-dat <- data.sim.rasch
-# some control arguments
-ctl.list <- list(maxiter=100) # increase the number of iterations in applications!
-
-#*** Model 1: Rasch model, normal trait distribution
-mod1 <- TAM::tam.mml.3pl(resp=dat, skillspace="normal", est.some.slopes=FALSE,
-              control=ctl.list)
-summary(mod1)
-
-#*** Model 2: Rasch model, discrete trait distribution
-#  choose theta grid
-theta.k <- seq( -3, 3, len=7 )    # discrete theta grid distribution
-# define symmetric trait distribution
-delta.designmatrix <- matrix( 0, nrow=7, ncol=4 )
-delta.designmatrix[4,1] <- 1
-delta.designmatrix[c(3,5),2] <- 1
-delta.designmatrix[c(2,6),3] <- 1
-delta.designmatrix[c(1,7),4] <- 1
-mod2 <- TAM::tam.mml.3pl(resp=dat, skillspace="discrete", est.some.slopes=FALSE,
-           theta.k=theta.k, delta.designmatrix=delta.designmatrix, control=ctl.list)
-summary(mod2)
-
-#*** Model 3: 2PL model
-mod3 <- TAM::tam.mml.3pl(resp=dat, skillspace="normal", gammaslope.des="2PL",
-       control=ctl.list, est.variance=FALSE )
-summary(mod3)
-
-#*** Model 4: 3PL model
-# estimate guessing parameters for items 3,7,9 and 12
-I <- ncol(dat)
-est.guess <- rep(0, I )
-# set parameters 9 and 12 equal -> same integers
-est.guess[ c(3,7,9,12) ] <- c( 1, 3, 2, 2 )
-# starting values guessing parameter
-guess <- .2*(est.guess > 0)
-# estimate model
-mod4 <- TAM::tam.mml.3pl(resp=dat, skillspace="normal", gammaslope.des="2PL",
-        control=ctl.list, est.guess=est.guess, guess=guess, est.variance=FALSE)
-summary(mod4)
-
-#--- specification in tamaan
-tammodel <- "
-LAVAAN MODEL:
-  F1=~ I1__I40
-  F1 ~~ 1*F1
-  I3 + I7 ?=g1
-  I9 + I12 ?=g912 * g1
-    "
-mod4a <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=20))
-summary(mod4a)
-
-#*** Model 5: 3PL model, add some prior Beta distribution
-guess.prior <- matrix( 0, nrow=I, ncol=2 )
-guess.prior[ est.guess  > 0, 1] <- 5
-guess.prior[ est.guess  > 0, 2] <- 17
-mod5 <- TAM::tam.mml.3pl(resp=dat, skillspace="normal", gammaslope.des="2PL",
-        control=ctl.list, est.guess=est.guess, guess=guess, guess.prior=guess.prior)
-summary(mod5)
-
-#--- specification in tamaan
-tammodel <- "
-LAVAAN MODEL:
-  F1=~ I1__I40
-  F1 ~~ 1*F1
-  I3 + I7 ?=g1
-  I9 + I12 ?=g912 * g1
-MODEL PRIOR:
-  g912 ~ Beta(5,17)
-  I3_guess ~ Beta(5,17)
-  I7_guess ~ Beta(5,17)
-    "
-mod5a <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=20))
-
-#*** Model 6: 2PL model with design matrix for item slopes
-I <- 40         # number of items
-D <- 1       # dimensions
-maxK <- 2    # maximum number of categories
-Ngam <- 13   # number of different slope parameters
-E <- array( 0, dim=c(I,maxK,D,Ngam) )
-# joint slope parameters for items 1 to 10, 11 to 20, 21 to 30
-E[ 1:10, 2, 1, 2 ] <- 1
-E[ 11:20, 2, 1, 1 ] <- 1
-E[ 21:30, 2, 1, 3 ] <- 1
-for (ii in 31:40){   E[ii,2,1,ii - 27 ] <- 1 }
-# estimate model
-mod6 <- TAM::tam.mml.3pl(resp=dat, control=ctl.list,   E=E, est.variance=FALSE  )
-summary(mod6)
-
-#*** Model 6b: Truncated normal prior distribution for slope parameters
-gammaslope.prior <- matrix( 0, nrow=Ngam, ncol=4 )
-gammaslope.prior[,1] <- 2  # mean
-gammaslope.prior[,2] <- 10  # standard deviation
-gammaslope.prior[,3] <- -Inf # lower bound
-gammaslope.prior[ 4:13,3] <- 1.2
-gammaslope.prior[,4] <- Inf  # upper bound
-# estimate model
-mod6b <- TAM::tam.mml.3pl(resp=dat, E=E, est.variance=FALSE,
-                gammaslope.prior=gammaslope.prior, control=ctl.list )
-summary(mod6b)
-
-#*** Model 7: 2PL model with design matrix of slopes and slope constraints
-Ngam <- dim(E)[4]   # number of gamma parameters
-# define two constraint equations
-gammaslope.constr.V <- matrix( 0, nrow=Ngam, ncol=2 )
-gammaslope.constr.c <- rep(0,2)
-# set sum of first two xlambda entries to 1.8
-gammaslope.constr.V[1:2,1] <- 1
-gammaslope.constr.c[1] <- 1.8
-# set sum of entries 4, 5 and 6 to 3
-gammaslope.constr.V[4:6,2] <- 1
-gammaslope.constr.c[2] <- 3
-mod7 <- TAM::tam.mml.3pl(resp=dat, control=ctl.list,  E=E,  est.variance=FALSE,
-   gammaslope.constr.V=gammaslope.constr.V, gammaslope.constr.c=gammaslope.constr.c)
-summary(mod7)
-
-#**** Model 8: Located latent class Rasch model with estimated three skill points
-
-# three classes of theta's are estimated
-TP <- 3
-theta.k <- diag(TP)
-# because item difficulties are unrestricted, we define the sum of the estimated
-# theta points equal to zero
-Ngam <- TP  # estimate three gamma loading parameters which are discrete theta points
-E <- array( 0, dim=c(I,2,TP,Ngam) )
-E[,2,1,1] <- E[,2,2,2] <- E[,2,3,3] <- 1
-gammaslope.constr.V <- matrix( 1, nrow=3, ncol=1 )
-gammaslope.constr.c <- c(0)
-# initial gamma values
-gammaslope <- c( -2, 0, 2 )
-# estimate model
-mod8 <- TAM::tam.mml.3pl(resp=dat, control=ctl.list,  E=E,  skillspace="discrete",
-     theta.k=theta.k, gammaslope=gammaslope, gammaslope.constr.V=gammaslope.constr.V,
-     gammaslope.constr.c=gammaslope.constr.c )
-summary(mod8)
-
-#*** Model 9: Multidimensional multiple group model
-N <- nrow(dat)
-I <- ncol(dat)
-group <- c( rep(1,N/4), rep(2,N/4), rep(3,N/2) )
-Q <- matrix(0,nrow=I,ncol=2)
-Q[ 1:(I/2), 1] <- Q[ seq(I/2+1,I), 2] <- 1
-# estimate model
-mod9 <- TAM::tam.mml.3pl(resp=dat, skillspace="normal", est.some.slopes=FALSE,
-               group=group, Q=Q)
-summary(mod9)
-
-#############################################################################
-# EXAMPLE 2: Polytomous data
-#############################################################################
-
-data( data.mg, package="CDM")
-dat <- data.mg[1:1000, paste0("I",1:11)]
-
-#*******************************************************
-#*** Model 1: 1-dimensional 1PL estimation, normal skill distribution
-mod1 <- TAM::tam.mml.3pl(resp=dat, skillspace="normal",
-           gammaslope.des="2PL", est.some.slopes=FALSE,  est.variance=TRUE  )
-summary(mod1)
-
-#*******************************************************
-#*** Model 2: 1-dimensional 2PL estimation, discrete skill distribution
-# define skill space
-theta.k <- matrix( seq(-5,5,len=21) )
-# allow skew skill distribution
-delta.designmatrix <- cbind( 1, theta.k, theta.k^2, theta.k^3 )
-# fix 13th xsi item parameter to zero
-xsi.fixed <- cbind( 13, 0 )
-# fix 10th slope paremeter to one
-gammaslope.fixed <- cbind( 10, 1 )
-# estimate model
-mod2 <- TAM::tam.mml.3pl(resp=dat, skillspace="discrete", theta.k=theta.k,
-          delta.designmatrix=delta.designmatrix, gammaslope.des="2PL", xsi.fixed=xsi.fixed,
-          gammaslope.fixed=gammaslope.fixed)
-summary(mod2)
-
-#*******************************************************
-#*** Model 3: 2-dimensional 2PL estimation, normal skill distribution
-
-# define loading matrix
-Q <- matrix(0,11,2)
-Q[1:6,1] <- 1   # items 1 to 6 load on dimension 1
-Q[7:11,2] <- 1  # items 7 to 11 load on dimension 2
-# estimate model
-mod3 <- TAM::tam.mml.3pl(resp=dat, gammaslope.des="2PL", Q=Q )
-summary(mod3)
-
-#############################################################################
-# EXAMPLE 3: Dichotomous data with guessing
-#############################################################################
-
-#*** simulate data
-set.seed(9765)
-N <- 4000   # number of persons
-I <- 20     # number of items
-b <- seq( -1.5, 1.5, len=I )
-guess <- rep(0, I )
-guess.items <- c(6,11,16)
-guess[ guess.items ] <- .33
-library(sirt)
-dat <- sirt::sim.raschtype( stats::rnorm(N), b=b, fixed.c=guess )
-
-#*******************************************************
-#*** Model 1: Difficulty + guessing model, i.e. fix slopes to 1
-est.guess <- rep(0,I)
-est.guess[ guess.items ] <- seq(1, length(guess.items) )
-# define prior distribution
-guess.prior <- matrix( cbind( 5, 17 ), I, 2, byrow=TRUE )
-guess.prior[ ! est.guess, ] <- 0
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat, guess=guess, est.guess=est.guess,
-           guess.prior=guess.prior, control=ctl.list,est.variance=TRUE,
-           est.some.slopes=FALSE )
-summary(mod1)
-
-#*******************************************************
-#*** Model 2: estimate a joint guessing parameter
-est.guess <- rep(0,I)
-est.guess[ guess.items ] <- 1
-# estimate model
-mod2 <- TAM::tam.mml.3pl(resp=dat, guess=guess, est.guess=est.guess,
-            guess.prior=guess.prior, control=ctl.list,est.variance=TRUE,
-            est.some.slopes=FALSE )
-summary(mod2)
-
-#############################################################################
-# EXAMPLE 4: Latent class model with two classes
-#      See slca Simulated Example 2 in the CDM package
-#############################################################################
-
-#*******************************************************
-#*** simulate data
-set.seed(9876)
-I <- 7  # number of items
-# simulate response probabilities
-a1 <- round( stats::runif(I,0, .4 ),4)
-a2 <- round( stats::runif(I, .6, 1 ),4)
-N <- 1000   # sample size
-# simulate data in two classes of proportions .3 and .7
-N1 <- round(.3*N)
-dat1 <- 1 * ( matrix(a1,N1,I,byrow=TRUE) > matrix( stats::runif( N1 * I), N1, I ) )
-N2 <- round(.7*N)
-dat2 <- 1 * ( matrix(a2,N2,I,byrow=TRUE) > matrix( stats::runif( N2 * I), N2, I ) )
-dat <- rbind( dat1, dat2 )
-colnames(dat) <- paste0("I", 1:I)
-
-#*******************************************************
-# estimation using tam.mml.3pl function
-
-# define design matrices
-TP <- 2     # two classes
-theta.k <- diag(TP)     # there are theta dimensions -> two classes
-# The idea is that latent classes refer to two different "dimensions".
-# Items load on latent class indicators 1 and 2, see below.
-E <- array(0, dim=c(I,2,2,2*I) )
-items <- colnames(dat)
-dimnames(E)[[4]] <- c(paste0( colnames(dat), "Class", 1),
-          paste0( colnames(dat), "Class", 2) )
-# items, categories, classes, parameters
-# probabilities for correct solution
-for (ii in 1:I){
-    E[ ii, 2, 1, ii ] <- 1    # probabilities class 1
-    E[ ii, 2, 2, ii+I ] <- 1  # probabilities class 2
-                    }
-
-# estimation command
-mod1 <- TAM::tam.mml.3pl(resp=dat, E=E, control=list(maxit=20), skillspace="discrete",
-          theta.k=theta.k, notA=TRUE)
-summary(mod1)
-# compare simulated and estimated data
-cbind( mod1$rprobs[,2,1], a2 )  # Simulated class 2
-cbind( mod1$rprobs[,2,2], a1 )  # Simulated class 1
-
-#*******************************************************
-#** specification with tamaan
-tammodel <- "
-ANALYSIS:
-  TYPE=LCA;
-  NCLASSES(2);   # 2 classes
-  NSTARTS(5,20); # 5 random starts with 20 iterations
-LAVAAN MODEL:
-  F=~ I1__I7
-    "
-mod1b <- TAM::tamaan( tammodel, resp=dat )
-summary(mod1b)
-# compare with mod1
-logLik(mod1)
-logLik(mod1b)
-
-#############################################################################
-# EXAMPLE 5: Located latent class model, Rasch model
-#      See slca Simulated Example 4 in the CDM package
-#############################################################################
-
-#*** simulate data
-set.seed(487)
-I <- 15  # I items
-b1 <- seq( -2, 2, len=I)      # item difficulties
-N <- 2000    # number of persons
-# simulate 4 theta classes
-theta0 <- c( -2.5, -1, 0.3, 1.3 )  # skill classes
-probs0 <- c( .1, .4, .2, .3 )      # skill class probabilities
-TP <- length(theta0)
-theta <- theta0[ rep(1:TP, round(probs0*N)  ) ]
-library(sirt)
-dat <- sirt::sim.raschtype( theta, b1 )
-colnames(dat) <- paste0("I",1:I)
-
-#*******************************************************
-#*** Model 1: Located latent class model with 4 classes
-maxK <- 2
-Ngam <- TP
-E <- array( 0, dim=c(I, maxK, TP,  Ngam ) )
-dimnames(E)[[1]] <- colnames(dat)
-dimnames(E)[[2]] <- paste0("Cat", 1:(maxK) )
-dimnames(E)[[3]] <- paste0("Class", 1:TP)
-dimnames(E)[[4]] <- paste0("theta", 1:TP)
-# theta design
-for (tt in 1:TP){   E[1:I, 2, tt,  tt] <- 1       }
-theta.k <- diag(TP)
-# set eighth item difficulty to zero
-xsi.fixed <- cbind( 8, 0 )
-# initial gamma parameter
-gammaslope <- seq( -1.5, 1.5, len=TP)
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat, E=E, xsi.fixed=xsi.fixed,
-           control=list(maxiter=100), skillspace="discrete",
-           theta.k=theta.k, gammaslope=gammaslope)
-summary(mod1)
-# compare estimated and simulated theta class locations
-cbind( mod1$gammaslope, theta0 )
-# compare estimated and simulated latent class proportions
-cbind( mod1$pi.k, probs0 )
-
-#----- specification using tamaan
-tammodel <- "
-ANALYSIS:
-  TYPE=LOCLCA;
-  NCLASSES(4)
-LAVAAN MODEL:
-  F=~ I1__I15
-  I8 | 0*t1
-    "
-mod1b <- TAM::tamaan( tammodel, resp=dat )
-summary(mod1b)
-
-#############################################################################
-# EXAMPLE 6: DINA model with two skills
-#      See slca Simulated Example 5 in the CDM package
-#############################################################################
-
-#*** simulate data
-set.seed(487)
-N <- 3000   # number of persons
-# define Q-matrix
-I <- 9  # 9 items
-NS <- 2 # 2 skills
-TP <- 4 # number of skill classes
-Q <- scan(nlines=3, text=
-  "1 0   1 0   1 0
-   0 1   0 1   0 1
-   1 1   1 1   1 1",
-   )
-Q <- matrix(Q, I, ncol=NS, byrow=TRUE)
-# define skill distribution
-alpha0 <- matrix( c(0,0,1,0,0,1,1,1), nrow=4,ncol=2,byrow=TRUE)
-prob0 <- c( .2, .4, .1, .3 )
-alpha <- alpha0[ rep( 1:TP, prob0*N),]
-# define guessing and slipping parameters
-guess <- round( stats::runif(I, 0, .4 ), 2 )
-slip <- round( stats::runif(I, 0, .3 ), 2 )
-# simulate data according to the DINA model
-dat <- CDM::sim.din( q.matrix=Q, alpha=alpha, slip=slip, guess=guess )$dat
-
-#*** Model 1: Estimate DINA model
-# define E matrix which contains the anti-slipping parameters
-maxK <- 2
-Ngam <- I
-E <- array( 0, dim=c(I, maxK, TP,  Ngam ) )
-dimnames(E)[[1]] <- colnames(dat)
-dimnames(E)[[2]] <- paste0("Cat", 1:(maxK) )
-dimnames(E)[[3]] <- c("S00","S10","S01","S11")
-dimnames(E)[[4]] <- paste0( "antislip", 1:I )
-# define anti-slipping parameters in E
-for (ii in 1:I){
-        # define latent responses
-        latresp <- 1*( alpha0 %*% Q[ii,]==sum(Q[ii,]) )[,1]
-        # model slipping parameters
-        E[ii, 2, latresp==1, ii ] <- 1
-                 }
-# skill space definition
-theta.k <- diag(TP)
-gammaslope <- rep( qlogis( .8 ), I )
-
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat, E=E, control=list(maxiter=100), skillspace="discrete",
-          theta.k=theta.k, gammaslope=gammaslope)
-summary(mod1)
-# compare estimated and simulated latent class proportions
-cbind( mod1$pi.k, probs0 )
-# compare estimated and simulated guessing parameters
-cbind( mod1$rprobs[,2,1], guess )
-# compare estimated and simulated slipping parameters
-cbind( 1 - mod1$rprobs[,2,4], slip )
-
-#############################################################################
-# EXAMPLE 7: Mixed Rasch model with two classes
-#      See slca Simulated Example 3 in the CDM package
-#############################################################################
-
-#*** simulate data
-set.seed(987)
-library(sirt)
-# simulate two latent classes of Rasch populations
-I <- 15  # 6 items
-b1 <- seq( -1.5, 1.5, len=I)      # difficulties latent class 1
-b2 <- b1    # difficulties latent class 2
-b2[ c(4,7, 9, 11, 12, 13) ] <- c(1, -.5, -.5, .33, .33, -.66 )
-b2 <- b2 - mean(b2)
-N <- 3000    # number of persons
-wgt <- .25       # class probability for class 1
-# class 1
-dat1 <- sirt::sim.raschtype( stats::rnorm( wgt*N ), - b1 )
-# class 2
-dat2 <- sirt::sim.raschtype( stats::rnorm( (1-wgt)*N, mean=1, sd=1.7), - b2 )
-dat <- rbind( dat1, dat2 )
-
-# The idea is that each grid point class x theta is defined as new
-# dimension. If we approximate the trait distribution by 7 theta points
-# and are interested in estimating 2 latent classes, then we need
-# 7*2=14 dimensions.
-
-#*** Model 1: Rasch model
-# theta grid
-theta.k1 <- seq( -5, 5, len=7 )
-TT <- length(theta.k1)
-#-- define theta design matrix
-theta.k <- diag(TT)
-#-- delta designmatrix
-delta.designmatrix <- matrix( 0, TT, ncol=3 )
-delta.designmatrix[, 1] <- 1
-delta.designmatrix[, 2:3] <- cbind( theta.k1, theta.k1^2 )
-
-#-- define loading matrix E
-E <- array( 0, dim=c(I,2,TT,I + 1) )  # last parameter is constant 1
-for (ii in 1:I){
-    E[ ii, 2, 1:TT, ii ] <- -1   # '-b' in '1*theta - b'
-    E[ ii, 2, 1:TT, I+1] <- theta.k1  # '1*theta' in '1*theta - b'
-                }
-# initial gammaslope parameters
-par1 <- stats::qlogis( colMeans( dat ) )
-gammaslope <- c( par1, 1 )
-# sum constraint of zero on item difficulties
-gammaslope.constr.V <- matrix( 0, I+1, 1 )
-gammaslope.constr.V[ 1:I, 1] <- 1  # Class 1
-gammaslope.constr.c <- c(0)
-# fixed gammaslope parameter
-gammaslope.fixed <- cbind( I+1, 1 )
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat1, E=E, skillspace="discrete",
-           theta.k=theta.k, delta.designmatrix=delta.designmatrix,
-           gammaslope=gammaslope, gammaslope.constr.V=gammaslope.constr.V,
-           gammaslope.constr.c=gammaslope.constr.c, gammaslope.fixed=gammaslope.fixed,
-           notA=TRUE, est.variance=FALSE)
-summary(mod1)
-
-#*** Model 2: Mixed Rasch model with two latent classes
-# theta grid
-theta.k1 <- seq( -4, 4, len=7 )
-TT <- length(theta.k1)
-#-- define theta design matrix
-theta.k <- diag(2*TT)   # 2*7=14 classes
-#-- delta designmatrix
-delta.designmatrix <- matrix( 0, 2*TT, ncol=6 )
-# Class 1
-delta.designmatrix[1:TT, 1] <- 1
-delta.designmatrix[1:TT, 2:3] <- cbind( theta.k1, theta.k1^2 )
-# Class 2
-delta.designmatrix[TT+1:TT, 4] <- 1
-delta.designmatrix[TT+1:TT, 5:6] <- cbind( theta.k1, theta.k1^2 )
-
-#-- define loading matrix E
-E <- array( 0, dim=c(I,2,2*TT,2*I + 1) )  # last parameter is constant 1
-dimnames(E)[[1]] <- colnames(dat)
-dimnames(E)[[2]] <- c("Cat0","Cat1")
-dimnames(E)[[3]] <- c( paste0("Class1_theta", 1:TT), paste0("Class2_theta", 1:TT) )
-dimnames(E)[[4]] <- c( paste0("b_Class1_", colnames(dat)),
-       paste0("b_Class2_", colnames(dat)), "One")
-for (ii in 1:I){
-  # Class 1 item parameters
-    E[ ii, 2, 1:TT, ii ] <- -1   # '-b' in '1*theta - b'
-    E[ ii, 2, 1:TT, 2*I+1] <- theta.k1  # '1*theta' in '1*theta - b'
-  # Class 2 item parameters
-    E[ ii, 2, TT + 1:TT, I + ii ] <- -1
-    E[ ii, 2, TT + 1:TT, 2*I+1] <- theta.k1
-                }
-# initial gammaslope parameters
-par1 <- qlogis( colMeans( dat ) )
-gammaslope <- c( par1, par1 + stats::runif(I, -2,2 ), 1 )
-# sum constraint of zero on item difficulties within a class
-gammaslope.center.index <- c( rep( 1, I ), rep(2,I), 0 )
-gammaslope.center.value <- c(0,0)
-
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat, E=E, skillspace="discrete",
-            theta.k=theta.k, delta.designmatrix=delta.designmatrix,
-            gammaslope=gammaslope, gammaslope.center.index=gammaslope.center.index,
-            gammaslope.center.value=gammaslope.center.value, gammaslope.fixed=gammaslope.fixed,
-            notA=TRUE)
-summary(mod1)
-# latent class proportions
-stats::aggregate( mod1$pi.k, list( rep(1:2, each=TT)), sum )
-# compare simulated and estimated item parameters
-cbind( b1, b2, - mod1$gammaslope[1:I], - mod1$gammaslope[I + 1:I ] )
-
-#--- specification in tamaan
-tammodel <- "
-ANALYSIS:
-  TYPE=MIXTURE;
-  NCLASSES(2)
-  NSTARTS(5,30)
-LAVAAN MODEL:
-  F=~ I0001__I0015
-ITEM TYPE:
-  ALL(Rasch);
-    "
-mod1b <- TAM::tamaan( tammodel, resp=dat )
-summary(mod1b)
-
-#############################################################################
-# EXAMPLE 8: 2PL mixture distribution model
-#############################################################################
-
-#*** simulate data
-set.seed(9187)
-library(sirt)
-# simulate two latent classes of Rasch populations
-I <- 20
-b1 <- seq( -1.5, 1.5, len=I)      # difficulties latent class 1
-b2 <- b1    # difficulties latent class 2
-b2[ c(4,7, 9, 11, 12, 13, 16, 18) ] <- c(1, -.5, -.5, .33, .33, -.66, -1, .3)
-# b2 <- scale( b2, scale=FALSE)
-b2 <- b2 - mean(b2)
-N <- 4000       # number of persons
-wgt <- .75       # class probability for class 1
-# item slopes
-a1 <- rep( 1, I )  # first class
-a2 <- rep( c(.5,1.5), I/2 )
-
-# class 1
-dat1 <- sirt::sim.raschtype( stats::rnorm( wgt*N ), - b1, fixed.a=a1)
-# class 2
-dat2 <- sirt::sim.raschtype( stats::rnorm( (1-wgt)*N, mean=1, sd=1.4), - b2, fixed.a=a2)
-dat <- rbind( dat1, dat2 )
-
-#*** Model 1: Mixed 2PL model with two latent classes
-
-theta.k1 <- seq( -4, 4, len=7 )
-TT <- length(theta.k1)
-#-- define theta design matrix
-theta.k <- diag(2*TT)   # 2*7=14 classes
-#-- delta designmatrix
-delta.designmatrix <- matrix( 0, 2*TT, ncol=6 )
-# Class 1
-delta.designmatrix[1:TT, 1] <- 1
-delta.designmatrix[1:TT, 2:3] <- cbind( theta.k1, theta.k1^2 )
-# Class 2
-delta.designmatrix[TT+1:TT, 4] <- 1
-delta.designmatrix[TT+1:TT, 5:6] <- cbind( theta.k1, theta.k1^2 )
-
-#-- define loading matrix E
-E <- array( 0, dim=c(I,2,2*TT,4*I ) )
-dimnames(E)[[1]] <- colnames(dat)
-dimnames(E)[[2]] <- c("Cat0","Cat1")
-dimnames(E)[[3]] <- c( paste0("Class1_theta", 1:TT), paste0("Class2_theta", 1:TT) )
-dimnames(E)[[4]] <- c( paste0("b_Class1_", colnames(dat)),
-                       paste0("a_Class1_", colnames(dat)),
-                       paste0("b_Class2_", colnames(dat)),
-                       paste0("a_Class2_", colnames(dat)) )
-
-for (ii in 1:I){
-  # Class 1 item parameters
-    E[ ii, 2, 1:TT, ii ] <- -1           # '-b' in 'a*theta - b'
-    E[ ii, 2, 1:TT, I + ii] <- theta.k1  # 'a*theta' in 'a*theta - b'
-  # Class 2 item parameters
-    E[ ii, 2, TT + 1:TT, 2*I + ii ] <- -1
-    E[ ii, 2, TT + 1:TT, 3*I + ii ] <- theta.k1
-}
-
-# initial gammaslope parameters
-par1 <- scale( - stats::qlogis( colMeans( dat ) ), scale=FALSE )
-gammaslope <- c( par1, rep(1,I),  scale( par1 + runif(I, - 1.4, 1.4 ),
-        scale=FALSE), stats::runif( I,.6,1.4) )
-
-# constraint matrix
-gammaslope.constr.V <- matrix( 0, 4*I, 4 )
-# sum of item intercepts equals zero
-gammaslope.constr.V[ 1:I, 1] <- 1        # Class 1 (b)
-gammaslope.constr.V[ 2*I + 1:I, 2] <- 1  # Class 2 (b)
-# sum of item slopes equals number of items -> mean slope of 1
-gammaslope.constr.V[ I + 1:I, 3] <- 1    # Class 1 (a)
-gammaslope.constr.V[ 3*I + 1:I, 4] <- 1  # Class 2 (a)
-gammaslope.constr.c <- c(0,0,I,I)
-
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat, E=E, control=list(maxiter=80), skillspace="discrete",
-      theta.k=theta.k, delta.designmatrix=delta.designmatrix,
-      gammaslope=gammaslope, gammaslope.constr.V=gammaslope.constr.V,
-      gammaslope.constr.c=gammaslope.constr.c,  gammaslope.fixed=gammaslope.fixed,
-      notA=TRUE)
-
-# estimated item parameters
-mod1$gammaslope
-# summary
-summary(mod1)
-# latent class proportions
-round( stats::aggregate( mod1$pi.k, list( rep(1:2, each=TT)), sum ), 3 )
-# compare simulated and estimated item intercepts
-int <- cbind( b1*a1, b2 * a2, - mod1$gammaslope[1:I], - mod1$gammaslope[2*I + 1:I ] )
-round( int, 3 )
-# simulated and estimated item slopes
-slo <- cbind( a1, a2,  mod1$gammaslope[I+1:I], mod1$gammaslope[3*I + 1:I ] )
-round(slo,3)
-
-#--- specification in tamaan
-tammodel <- "
-ANALYSIS:
-  TYPE=MIXTURE;
-  NCLASSES(2)
-  NSTARTS(10,25)
-LAVAAN MODEL:
-  F=~ I0001__I0020
-    "
-mod1t <- TAM::tamaan( tammodel, resp=dat )
-summary(mod1t)
-
-#############################################################################
-# EXAMPLE 9: Toy example: Exact representation of an item by a factor
-#############################################################################
-
-data(data.gpcm)
-dat <- data.gpcm[,1,drop=FALSE ]   # choose first item
-# some descriptives
-( t1 <- table(dat) )
-
-# The idea is that we define an IRT model with one latent variable
-# which extactly corresponds to the manifest item.
-
-I <- 1    # 1 item
-K <- 4    # 4 categories
-TP <- 4   # 4 discrete theta points
-
-# define skill space
-theta.k <- diag(TP)
-# define loading matrix E
-E <- array( -99, dim=c(I,K,TP,1 ) )
-for (vv in 1:K){
-    E[ 1, vv, vv, 1 ] <- 9
-                }
-# estimate model
-mod1 <- TAM::tam.mml.3pl(resp=dat, E=E, skillspace="discrete",
-         theta.k=theta.k,  notA=TRUE)
-summary(mod1)
-# -> the latent distribution corresponds to the manifest distribution, because ...
-round( mod1$pi.k, 3 )
-round( t1 / sum(t1), 3 )
-
-#############################################################################
-# EXAMPLE 10: Some fixed item loadings
-#############################################################################
-
-data(data.Students,package="CDM")
-dat <- data.Students
-# select variables
-vars <- scan( nlines=1, what="character")
-    act1 act2 act3 act4 act5 sc1 sc2 sc3 sc4
-dat <- data.Students[, vars  ]
-
-# define loading matrix: two-dimensional model
-Q <- matrix( 0, nrow=9, ncol=2 )
-Q[1:5,1] <- 1
-Q[6:9,2] <- 1
-# define some fixed item loadings
-Q.fixed <- NA*Q
-Q.fixed[ c(1,4), 1] <- .5
-Q.fixed[ 6:7, 2 ] <- 1
-
-# estimate model
-mod3 <- TAM::tam.mml.3pl( resp=dat, gammaslope.des="2PL", Q=Q, Q.fixed=Q.fixed,
-            control=list( maxiter=10, nodes=seq(-4,4,len=10) )  )
-summary(mod3)
-
-#############################################################################
-# EXAMPLE 11: Mixed response formats - Multiple choice and partial credit items
-#############################################################################
-
-data(data.timssAusTwn.scored)
-dat <- data.timssAusTwn.scored
-
-# select columns with item responses
-dat <- dat[, grep("M0", colnames(dat) ) ]
-I <- ncol(dat)   # number of items
-
-# The idea is to start with partial credit modelling
-# and then to include the guessing parameters
-
-#*** Model 0: Partial Credit Model
-mod0 <- TAM::tam.mml(dat)
-summary(mod0)
-
-#*** Model 1 and Model 2: include guessing parameters
-
-# multiple choice items
-guess_items <- which( apply( dat, 2, max, na.rm=TRUE )==1 )
-# define guessing parameters
-guess0 <- rep(0,I)
-guess0[ guess_items ] <- .25  # set guessing probability to .25
-
-# define which guessing parameters should be estimated
-est.guess1 <- rep(0,I)  # all parameters are fixed
-est.guess2 <- 1 * ( guess0==.25 )  # joint guessing parameter
-
-# use design matrix from partial credit model
-A0 <- mod0$A
-
-#--- Model 1: fixed guessing parameters of .25 and item slopes of 1
-mod1 <- TAM::tam.mml.3pl( dat, guess=guess0, est.guess=est.guess1,
-            A=A0, est.some.slopes=FALSE, control=list(maxiter=50) )
-summary(mod1)
-
-#--- Model 2: estimate joint guessing parameters and item slopes of 1
-mod2 <- TAM::tam.mml.3pl( dat, guess=guess0, est.guess=est.guess2,
-            A=A0, est.some.slopes=FALSE, control=list(maxiter=50) )
-summary(mod2)
-
-# model comparison
-IRT.compareModels(mod0,mod1,mod2)
-# }
-
                                                                    
+    
                                                                    
   
   
 
 
+
       
                                                                    
       
                                                                    
   
                                                                    Developed by Alexander Robitzsch.
                                                                    
 
                                                                    
 
 
                                                                    
-  
                                                                    Site built with pkgdown 1.3.0.
                                                                    
+  
                                                                    Site built with pkgdown 1.4.1.
                                                                    
 
                                                                    
+
       
                                                                    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.mml.html b/docs/reference/tam.mml.html
index 52e0bab..a28efb7 100644
--- a/docs/reference/tam.mml.html
+++ b/docs/reference/tam.mml.html
@@ -8,21 +8,25 @@
 
 Test Analysis Modules: Marginal Maximum Likelihood Estimation — tam.mml • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -54,6 +59,7 @@
 
 
 
+
   
 
   
@@ -78,7 +84,7 @@
       
-      
       
                                                                      
         
                                                                    • 
   
-    
+    
      
   
 
                                                                      • 
@@ -101,6 +106,7 @@
 
 
       
+
       
 
 
                                                                      
@@ -112,7 +118,6 @@ 
                                                                      Test Analysis Modules: Marginal Maximum Likelihood Estimation
                                                                      
     
                                                                      
 
     
                                                                      
-    
     
                                                                      Modules for psychometric test analysis demonstrated
   with the help of artificial example data.
   The package includes MML and JML estimation of
@@ -121,47 +126,46 @@ 
                                                                      Test Analysis Modules: Marginal Maximum Likelihood Estimation
                                                                      
   models, fit statistic computation, standard error estimation, as
   well as plausible value imputation and weighted likelihood
   estimation of ability.
                                                                      
-    
     
                                                                      
 
-    
                                                                      tam(resp, irtmodel="1PL", formulaA=NULL, ...)
-
-tam.mml(resp, Y=NULL, group=NULL, irtmodel="1PL", formulaY=NULL,
-    dataY=NULL, ndim=1,  pid=NULL, xsi.fixed=NULL, xsi.inits=NULL,
-    beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL,
-    variance.inits=NULL, est.variance=TRUE, constraint="cases", A=NULL,
-    B=NULL, B.fixed=NULL,  Q=NULL,  est.slopegroups=NULL, E=NULL,
-    pweights=NULL, userfct.variance=NULL,
-    variance.Npars=NULL, item.elim=TRUE, verbose=TRUE, control=list() )
-
-tam.mml.2pl(resp, Y=NULL, group=NULL, irtmodel="2PL", formulaY=NULL,
-    dataY=NULL, ndim=1, pid=NULL, xsi.fixed=NULL, xsi.inits=NULL,
-    beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL,
-    variance.inits=NULL, est.variance=FALSE, A=NULL, B=NULL,
-    B.fixed=NULL, Q=NULL, est.slopegroups=NULL, E=NULL, gamma.init=NULL,
-    pweights=NULL, userfct.variance=NULL, variance.Npars=NULL,
-    item.elim=TRUE, verbose=TRUE, control=list() )
-
-tam.mml.mfr(resp, Y=NULL, group=NULL, irtmodel="1PL", formulaY=NULL,
-    dataY=NULL, ndim=1, pid=NULL, xsi.fixed=NULL, xsi.setnull=NULL,
-    xsi.inits=NULL, beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL,
-    variance.inits=NULL, est.variance=TRUE, formulaA=~item+item:step,
-    constraint="cases", A=NULL, B=NULL,  B.fixed=NULL, Q=NULL,
-    facets=NULL, est.slopegroups=NULL, E=NULL,
-    pweights=NULL, verbose=TRUE, control=list(), delete.red.items=TRUE )
-
-# S3 method for tam
-summary(object, file=NULL, …)
-
-# S3 method for tam.mml
-summary(object, file=NULL, …)
-
-# S3 method for tam
-print(x, …)
-
-# S3 method for tam.mml
-print(x, …)
                                                                      
-    
+    
                                                                      tam(resp, irtmodel="1PL", formulaA=NULL, ...)
+
+tam.mml(resp, Y=NULL, group=NULL, irtmodel="1PL", formulaY=NULL,
+    dataY=NULL, ndim=1,  pid=NULL, xsi.fixed=NULL, xsi.inits=NULL,
+    beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL,
+    variance.inits=NULL, est.variance=TRUE, constraint="cases", A=NULL,
+    B=NULL, B.fixed=NULL,  Q=NULL,  est.slopegroups=NULL, E=NULL,
+    pweights=NULL, userfct.variance=NULL,
+    variance.Npars=NULL, item.elim=TRUE, verbose=TRUE, control=list() )
+
+tam.mml.2pl(resp, Y=NULL, group=NULL, irtmodel="2PL", formulaY=NULL,
+    dataY=NULL, ndim=1, pid=NULL, xsi.fixed=NULL, xsi.inits=NULL,
+    beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL,
+    variance.inits=NULL, est.variance=FALSE, A=NULL, B=NULL,
+    B.fixed=NULL, Q=NULL, est.slopegroups=NULL, E=NULL, gamma.init=NULL,
+    pweights=NULL, userfct.variance=NULL, variance.Npars=NULL,
+    item.elim=TRUE, verbose=TRUE, control=list() )
+
+tam.mml.mfr(resp, Y=NULL, group=NULL, irtmodel="1PL", formulaY=NULL,
+    dataY=NULL, ndim=1, pid=NULL, xsi.fixed=NULL, xsi.setnull=NULL,
+    xsi.inits=NULL, beta.fixed=NULL, beta.inits=NULL, variance.fixed=NULL,
+    variance.inits=NULL, est.variance=TRUE, formulaA=~item+item:step,
+    constraint="cases", A=NULL, B=NULL,  B.fixed=NULL, Q=NULL,
+    facets=NULL, est.slopegroups=NULL, E=NULL,
+    pweights=NULL, verbose=TRUE, control=list(), delete.red.items=TRUE )
+
+# S3 method for tam
+summary(object, file=NULL, ...)
+
+# S3 method for tam.mml
+summary(object, file=NULL, ...)
+
+# S3 method for tam
+print(x, ...)
+
+# S3 method for tam.mml
+print(x, ...)
                                                                      
+
     
                                                                      Arguments
                                                                      
     
@@ -205,7 +209,7 @@ 
                                                                      Arg
       
                                                                      
+     e. g. female*race or I(age^2).
                                                                      
@@ -440,7 +444,7 @@ 
                                                                      Arg
 (only for summary.tam functions)
                                                                      
     
                                                                      
-      
+      
@@ -448,7 +452,7 @@ 
                                                                      Arg
       
                                                                      
     
                                                                      formulaY
       
                                                                      An R formula for latent regression. Transformations of predictors
      in \(Y\) (included in dataY) can be easily specified,
-     e. g. female*race or I(age^2).
                                                                      		
     
                                                                      
     
                                                                      
       dataY
                                                                      
     
                                                                      …...
       
                                                                      Further arguments to be passed
                                                                      		
     
                                                                      
     
                                                                      Object of class tam or tam.mml
                                                                      		
     
                                                                      
     
                                                                      
-    
+
     
                                                                      Details
                                                                      
 
     
                                                                      The multidimensional item response model in TAM is
@@ -499,7 +503,6 @@ 
                                                                      Details
 (because control$QMC=TRUE is set by default). If
 control$QMC=FALSE is specified, then stochastic (Monte Carlo) integration
 is employed with control$snodes stochastic nodes.
                                                                      
-    
     
                                                                      Value
                                                                      
 
     
                                                                      A list with following entries:
                                                                      
@@ -577,9 +580,8 @@ 
                                                                      Value
                                                                      
 
                                                                      deviance.history
                                                                      Deviance history in iterations
                                                                      
 
                                                                      control
                                                                      List of control arguments
                                                                      
 
                                                                      latreg_stand
                                                                      List containing standardized regression coefficients
                                                                      
-
                                                                      …
                                                                      Further values
                                                                      
+
                                                                      ...
                                                                      Further values
                                                                      
 
-    
     
                                                                      References
                                                                      
 
     
                                                                      Adams, R. J., Wilson, M., & Wu, M. (1997).
@@ -605,7 +607,6 @@ 
                                                                      R
 
                                                                      Wu, M. L., Adams, R. J., Wilson, M. R. & Haldane, S. (2007).
 ACER ConQuest Version 2.0. Mulgrave.
 https://shop.acer.edu.au/acer-shop/group/CON3.
                                                                      
-    
     
                                                                      Note
                                                                      
 
     
                                                                      For more than three dimensions, quasi-Monte Carlo or stochastic integration
@@ -623,7 +624,6 @@ 
                                                                      Note
                                                                      
 Third, change in estimated parameters can be stabilized by fac.oldxsi for
 which a value of 0 (the default) and a value of 1 can be chosen. We recommend
 value between .5 and .8 if your model does not converge.
                                                                      
-    
     
                                                                      See also
                                                                      
 
     
                                                                      See data.cqc01 for more examples which is are similar to the ones
@@ -632,36 +632,34 @@ 
                                                                      See a
 
                                                                      Standard errors are estimated by a rather crude (but quick) approximation.
 Use tam.se for improved standard errors.
                                                                      
 
                                                                      For model comparisons see anova.tam.
                                                                      
-
                                                                      See sirt::tam2mirt for converting
+
                                                                      See sirt::tam2mirt for converting
 tam objects into objects of class
-mirt::mirt in the mirt package.
                                                                      
-    
+mirt::mirt in the mirt package.
                                                                      
 
     
                                                                      Examples
                                                                      
-    
                                                                      # NOT RUN {
-#############################################################################
+    
                                                                      #############################################################################
 # EXAMPLE 1: dichotomous data
 # data.sim.rasch: 2000 persons, 40 items
 #############################################################################
-data(data.sim.rasch)
+data(data.sim.rasch)
 
 #************************************************************
 # Model 1: Rasch model (MML estimation)
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 # extract item parameters
 mod1$item    # item difficulties
 
-# }# NOT RUN {
+if (FALSE) {
 # WLE estimation
-wle1 <- TAM::tam.wle( mod1 )
+wle1 <- TAM::tam.wle( mod1 )
 # item fit
-fit1 <- TAM::tam.fit(mod1)
+fit1 <- TAM::tam.fit(mod1)
 # plausible value imputation
-pv1 <- TAM::tam.pv(mod1, normal.approx=TRUE, ntheta=300)
+pv1 <- TAM::tam.pv(mod1, normal.approx=TRUE, ntheta=300)
 # standard errors
-se1 <- TAM::tam.se( mod1 )
+se1 <- TAM::tam.se( mod1 )
 # summary
-summary(mod1)
+summary(mod1)
 
 #-- specification with tamaan
 tammodel <- "
@@ -671,27 +669,27 @@ 
                                                                      Examp
  ITEM TYPE:
    ALL(Rasch)
    "
-mod1t <- TAM::tamaan( tammodel, data.sim.rasch)
-summary(mod1t)
+mod1t <- TAM::tamaan( tammodel, data.sim.rasch)
+summary(mod1t)
 
 #************************************************************
 # Model 1a: Rasch model with fixed item difficulties from 'mod1'
 xsi0 <- mod1$xsi$xsi
-xsi.fixed <- cbind( 1:(length(xsi0)), xsi0 )
+xsi.fixed <- cbind( 1:(length(xsi0)), xsi0 )
         # define vector with fixed item difficulties
-mod1a <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed )
-summary(mod1a)
+mod1a <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed )
+summary(mod1a)
 
 # Usage of the output value mod1$xsi.fixed.estimated has the right format
 # as the input of xsi.fixed
-mod1aa <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=mod1$xsi.fixed.estimated )
-summary(mod1b)
+mod1aa <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=mod1$xsi.fixed.estimated )
+summary(mod1b)
 
 #************************************************************
 # Model 1b: Rasch model with initial xsi parameters for items 2 (item difficulty b=-1.8),
 # item 4 (b=-1.6) and item 40 (b=2)
-xsi.inits <- cbind( c(2,4,40), c(-1.8,-1.6,2))
-mod1b <- TAM::tam.mml( resp=data.sim.rasch, xsi.inits=xsi.inits )
+xsi.inits <- cbind( c(2,4,40), c(-1.8,-1.6,2))
+mod1b <- TAM::tam.mml( resp=data.sim.rasch, xsi.inits=xsi.inits )
 
 #-- tamaan specification
 tammodel <- "
@@ -705,58 +703,59 @@ 
                                                                      Examp
  ITEM TYPE:
    ALL(Rasch)
    "
-mod1bt <- TAM::tamaan( tammodel, data.sim.rasch)
-summary(mod1bt)
+mod1bt <- TAM::tamaan( tammodel, data.sim.rasch)
+summary(mod1bt)
 
 #************************************************************
 # Model 1c: 1PL estimation with sum constraint on item difficulties
 dat <- data.sim.rasch
 # modify A design matrix to include the sum constraint
-des <- TAM::designMatrices(resp=dat)
-A1 <- des$A[,, - ncol(dat) ]
-A1[ ncol(dat),2, ] <- 1
+des <- TAM::designMatrices(resp=dat)
+A1 <- des$A[,, - ncol(dat) ]
+A1[ ncol(dat),2, ] <- 1
 A1[,2,]
 # estimate model
-mod1c <- TAM::tam.mml( resp=dat, A=A1, beta.fixed=FALSE,
-           control=list(fac.oldxsi=.1) )
-summary(mod1c)
+mod1c <- TAM::tam.mml( resp=dat, A=A1, beta.fixed=FALSE,
+           control=list(fac.oldxsi=.1) )
+summary(mod1c)
 
 #************************************************************
 # Model 1d: estimate constraint='items' using tam.mml.mfr
 formulaA=~ 0 + item
-mod1d <- TAM::tam.mml.mfr( resp=dat, formulaA=formulaA,
-                     control=list(fac.oldxsi=.1), constraint="items")
-summary(mod1d)
+mod1d <- TAM::tam.mml.mfr( resp=dat, formulaA=formulaA,
+                     control=list(fac.oldxsi=.1), constraint="items")
+summary(mod1d)
 
 #************************************************************
 # Model 1e: This sum constraint can also be obtained by using the argument
 # constraint="items" in tam.mml
-mod1e <- TAM::tam.mml( resp=data.sim.rasch, constraint="items" )
-summary(mod1e)
+mod1e <- TAM::tam.mml( resp=data.sim.rasch, constraint="items" )
+summary(mod1e)
 
 #************************************************************
 # Model 1d2: estimate constraint='items' using tam.mml.mfr
 # long format response data
-resp.long <- c(dat)
+resp.long <- c(dat)
 
 # pid and item facet specifications are necessary
 #     Note, that we recommend the facet labels to be sortable in the same order that the
 #     results are desired.
 #     compare to: facets <- data.frame( "item"=rep(colnames(dat), each=nrow(dat)) )
-pid <- rep(1:nrow(dat), ncol(dat))
-itemnames <- paste0("I", sprintf(paste('%0', max(nchar(1:ncol(dat))), 'i', sep='' ),
-                    c(1:ncol(dat)) ) )
-facets   <- data.frame( "item_"=rep(itemnames, each=nrow(dat)) )
+pid <- rep(1:nrow(dat), ncol(dat))
+itemnames <- paste0("I", sprintf(paste('%0', max(nchar(1:ncol(dat))), 'i', sep='' ),
+                    c(1:ncol(dat)) ) )
+facets   <- data.frame( "item_"=rep(itemnames, each=nrow(dat)) )
 formulaA=~ 0 + item_
 
-mod1d2 <- TAM::tam.mml.mfr( resp=resp.long, formulaA=formulaA, control=list(fac.oldxsi=.1),
+mod1d2 <- TAM::tam.mml.mfr( resp=resp.long, formulaA=formulaA, control=list(fac.oldxsi=.1),
                        constraint="items", facets=facets, pid=pid)
-stopifnot( all(mod1d$xsi.facets$xsi==mod1d2$xsi.facets$xsi) )
-# }# NOT RUN {
+stopifnot( all(mod1d$xsi.facets$xsi==mod1d2$xsi.facets$xsi) )
+}
+
 
 #************************************************************
 # Model 2: 2PL model
-mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch,irtmodel="2PL")
+mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch,irtmodel="2PL")
 
 # extract item parameters
 mod2$xsi    # item difficulties
@@ -770,19 +769,19 @@ 
                                                                      Examp
    # item type of 2PL is the default for dichotomous data
    "
 # estimate model
-mod2t <- TAM::tamaan( tammodel, data.sim.rasch)
-summary(mod2t)
+mod2t <- TAM::tamaan( tammodel, data.sim.rasch)
+summary(mod2t)
 
-# }# NOT RUN {
+if (FALSE) {
 #************************************************************
 # Model 2a: 2PL with fixed item difficulties and slopes from 'mod2'
 xsi0 <- mod2$xsi$xsi
-xsi.fixed <- cbind( 1:(length(xsi0)), xsi0 )
+xsi.fixed <- cbind( 1:(length(xsi0)), xsi0 )
         # define vector with fixed item difficulties
-mod2a <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed,
+mod2a <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed,
                  B=mod2$B # fix slopes
             )
-summary(mod2a)
+summary(mod2a)
 mod2a$B     # inspect used slope matrix
 
 #************************************************************
@@ -790,13 +789,13 @@ 
                                                                      Examp
 # estimate item parameters in different slope groups
 # items 1-10, 21-30 group 1
 # items 11-20 group 2 and items 31-40 group 3
-est.slope <- rep(1,40)
+est.slope <- rep(1,40)
 est.slope[ 11:20 ] <- 2
 est.slope[ 31:40 ] <- 3
-mod3 <- TAM::tam.mml.2pl( resp=data.sim.rasch, irtmodel="2PL.groups",
+mod3 <- TAM::tam.mml.2pl( resp=data.sim.rasch, irtmodel="2PL.groups",
                est.slopegroups=est.slope )
 mod3$B
-summary(mod3)
+summary(mod3)
 
 #--- tamaan specification (A)
 tammodel <- "
@@ -805,8 +804,8 @@ 
                                                                      Examp
    F ~~ 1*F
    "
 # estimate model
-mod3tA <- TAM::tamaan( tammodel, data.sim.rasch)
-summary(mod3tA)
+mod3tA <- TAM::tamaan( tammodel, data.sim.rasch)
+summary(mod3tA)
 
 #--- tamaan specification (alternative B)
 tammodel <- "
@@ -819,8 +818,8 @@ 
                                                                      Examp
    a21__a30==lam1
    a31__a40==lam3
    "
-mod3tB <- TAM::tamaan( tammodel, data.sim.rasch)
-summary(mod3tB)
+mod3tB <- TAM::tamaan( tammodel, data.sim.rasch)
+summary(mod3tB)
 
 #--- tamaan specification (alternative C using DO operator)
 tammodel <- "
@@ -840,70 +839,70 @@ 
                                                                      Examp
    F ~~ 1*F
    "
 # estimate model
-mod3tC <- TAM::tamaan( tammodel, data.sim.rasch)
-summary(mod3tC)
+mod3tC <- TAM::tamaan( tammodel, data.sim.rasch)
+summary(mod3tC)
 
 #############################################################################
 # EXAMPLE 2: Unidimensional calibration with latent regressors
 #############################################################################
 
 # (1) simulate data
-set.seed(6778)     # set simulation seed
+set.seed(6778)     # set simulation seed
 N <- 2000          # number of persons
 # latent regressors Y
-Y <- cbind( stats::rnorm( N, sd=1.5), stats::rnorm(N, sd=.3 ) )
+Y <- cbind( stats::rnorm( N, sd=1.5), stats::rnorm(N, sd=.3 ) )
 # simulate theta
-theta <- stats::rnorm( N ) + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
+theta <- stats::rnorm( N ) + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
 # number of items
 I <- 40
-p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
+p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
 # simulate response matrix
-resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
-colnames(resp) <- paste("I", 1:I, sep="")
+resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+colnames(resp) <- paste("I", 1:I, sep="")
 
 # (2) estimate model
-mod2_1 <- TAM::tam.mml(resp=resp, Y=Y)
-summary(mod2_1)
+mod2_1 <- TAM::tam.mml(resp=resp, Y=Y)
+summary(mod2_1)
 
 # (3) setting initial values for beta coefficients
 #   beta_2=.20, beta_3=.35 for dimension 1
-beta.inits <- cbind( c(2,3), 1, c(.2, .35 ) )
-mod2_2 <- TAM::tam.mml(resp=resp, Y=Y, beta.inits=beta.inits)
+beta.inits <- cbind( c(2,3), 1, c(.2, .35 ) )
+mod2_2 <- TAM::tam.mml(resp=resp, Y=Y, beta.inits=beta.inits)
 
 # (4) fix intercept to zero and third coefficient to .3
-beta.fixed <- cbind( c(1,3), 1, c(0, .3 ) )
-mod2_3 <- TAM::tam.mml(resp=resp, Y=Y, beta.fixed=beta.fixed )
+beta.fixed <- cbind( c(1,3), 1, c(0, .3 ) )
+mod2_3 <- TAM::tam.mml(resp=resp, Y=Y, beta.fixed=beta.fixed )
 
 # (5) same model but with R regression formula for Y
-dataY <- data.frame(Y)
-colnames(dataY) <- c("Y1","Y2")
-mod2_4 <- TAM::tam.mml(resp=resp, dataY=dataY, formulaY=~ Y1+Y2 )
-summary(mod2_4)
+dataY <- data.frame(Y)
+colnames(dataY) <- c("Y1","Y2")
+mod2_4 <- TAM::tam.mml(resp=resp, dataY=dataY, formulaY=~ Y1+Y2 )
+summary(mod2_4)
 
 # (6) model with interaction of regressors
-mod2_5 <- TAM::tam.mml(resp=resp, dataY=dataY, formulaY=~ Y1*Y2 )
-summary(mod2_5)
+mod2_5 <- TAM::tam.mml(resp=resp, dataY=dataY, formulaY=~ Y1*Y2 )
+summary(mod2_5)
 
 # (7) no constraint on regressors (removing constraint from intercept)
-mod2_6 <- TAM::tam.mml(resp=resp, Y=Y, beta.fixed=FALSE )
+mod2_6 <- TAM::tam.mml(resp=resp, Y=Y, beta.fixed=FALSE )
 
 #############################################################################
 # EXAMPLE 3: Multiple group estimation
 #############################################################################
 
 # (1) simulate data
-set.seed(6778)
+set.seed(6778)
 N <- 3000
-theta <- c( stats::rnorm(N/2,mean=0,sd=1.5), stats::rnorm(N/2,mean=.5,sd=1)  )
+theta <- c( stats::rnorm(N/2,mean=0,sd=1.5), stats::rnorm(N/2,mean=.5,sd=1)  )
 I <- 20
-p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
-resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
-colnames(resp) <- paste("I", 1:I, sep="")
-group <- rep(1:2, each=N/2 )
+p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
+resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+colnames(resp) <- paste("I", 1:I, sep="")
+group <- rep(1:2, each=N/2 )
 
 # (2) estimate model
-mod3_1 <- TAM::tam.mml( resp,  group=group )
-summary(mod3_1)
+mod3_1 <- TAM::tam.mml( resp,  group=group )
+summary(mod3_1)
 
 #############################################################################
 # EXAMPLE 4: Multidimensional estimation
@@ -914,30 +913,30 @@ 
                                                                      Examp
 #############################################################################
 
 # (1) simulate some data
-set.seed(6778)
-library(mvtnorm)
+set.seed(6778)
+library(mvtnorm)
 N <- 1000
-Y <- cbind( stats::rnorm( N ), stats::rnorm(N) )
-theta <- mvtnorm::rmvnorm( N,mean=c(0,0), sigma=matrix( c(1,.5,.5,1), 2, 2 ))
+Y <- cbind( stats::rnorm( N ), stats::rnorm(N) )
+theta <- mvtnorm::rmvnorm( N,mean=c(0,0), sigma=matrix( c(1,.5,.5,1), 2, 2 ))
 theta[,1] <- theta[,1] + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
 theta[,2] <- theta[,2] + .8 * Y[,1] + .5 * Y[,2]  # latent regression model
 I <- 20
-p1 <- stats::plogis( outer( theta[,1], seq( -2, 2, len=I ), "-" ) )
-resp1 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
-p1 <- stats::plogis( outer( theta[,2], seq( -2, 2, len=I ), "-" ) )
-resp2 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
-resp <- cbind(resp1,resp2)
-colnames(resp) <- paste("I", 1:(2*I), sep="")
+p1 <- stats::plogis( outer( theta[,1], seq( -2, 2, len=I ), "-" ) )
+resp1 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+p1 <- stats::plogis( outer( theta[,2], seq( -2, 2, len=I ), "-" ) )
+resp2 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+resp <- cbind(resp1,resp2)
+colnames(resp) <- paste("I", 1:(2*I), sep="")
 
 # (2) define loading Matrix
-Q <- array( 0, dim=c( 2*I, 2 ))
-Q[cbind(1:(2*I), c( rep(1,I), rep(2,I) ))] <- 1
+Q <- array( 0, dim=c( 2*I, 2 ))
+Q[cbind(1:(2*I), c( rep(1,I), rep(2,I) ))] <- 1
 
 # (3) estimate models
 
 #************************************************************
 # Model 4.1: Rasch model: without regressors
-mod4_1 <- TAM::tam.mml( resp=resp, Q=Q )
+mod4_1 <- TAM::tam.mml( resp=resp, Q=Q )
 
 #--- tamaan specification
 tammodel <- "
@@ -949,18 +948,18 @@ 
                                                                      Examp
     F2 ~~ F2
     F1 ~~ F2
     "
-mod4_1t <- TAM::tamaan( tammodel, resp, control=list(maxiter=100))
-summary(mod4_1t)
+mod4_1t <- TAM::tamaan( tammodel, resp, control=list(maxiter=100))
+summary(mod4_1t)
 
 #************************************************************
 # Model 4.1b: estimate model with sum constraint of items for each dimension
-mod4_1b <- TAM::tam.mml( resp=resp, Q=Q,constraint="items")
+mod4_1b <- TAM::tam.mml( resp=resp, Q=Q,constraint="items")
 
 #************************************************************
 # Model 4.2: Rasch model: set covariance between dimensions to zero
-variance_fixed <- cbind( 1, 2, 0 )
-mod4_2 <- TAM::tam.mml( resp=resp, Q=Q, variance.fixed=variance_fixed )
-summary(mod4_2)
+variance_fixed <- cbind( 1, 2, 0 )
+mod4_2 <- TAM::tam.mml( resp=resp, Q=Q, variance.fixed=variance_fixed )
+summary(mod4_2)
 
 #--- tamaan specification
 tammodel <- "
@@ -973,12 +972,12 @@ 
                                                                      Examp
   ITEM TYPE:
     ALL(Rasch)
     "
-mod4_2t <- TAM::tamaan( tammodel, resp)
-summary(mod4_2t)
+mod4_2t <- TAM::tamaan( tammodel, resp)
+summary(mod4_2t)
 
 #************************************************************
 # Model 4.3: 2PL model
-mod4_3 <- TAM::tam.mml.2pl( resp=resp, Q=Q, irtmodel="2PL" )
+mod4_3 <- TAM::tam.mml.2pl( resp=resp, Q=Q, irtmodel="2PL" )
 
 #--- tamaan specification
 tammodel <- "
@@ -989,21 +988,21 @@ 
                                                                      Examp
     F2 ~~ F2
     F1 ~~ F2
     "
-mod4_3t <- TAM::tamaan( tammodel, resp )
-summary(mod4_3t)
+mod4_3t <- TAM::tamaan( tammodel, resp )
+summary(mod4_3t)
 
 #************************************************************
 # Model 4.4: Rasch model with 2000 quasi monte carlo nodes
 # -> nodes are useful for more than 3 or 4 dimensions
-mod4_4 <- TAM::tam.mml( resp=resp, Q=Q, control=list(snodes=2000) )
+mod4_4 <- TAM::tam.mml( resp=resp, Q=Q, control=list(snodes=2000) )
 
 #************************************************************
 # Model 4.5: Rasch model with 2000 stochastic nodes
-mod4_5 <- TAM::tam.mml( resp=resp, Q=Q,control=list(snodes=2000,QMC=FALSE))
+mod4_5 <- TAM::tam.mml( resp=resp, Q=Q,control=list(snodes=2000,QMC=FALSE))
 
 #************************************************************
 # Model 4.6: estimate two dimensional Rasch model with regressors
-mod4_6 <- TAM::tam.mml( resp=resp, Y=Y, Q=Q )
+mod4_6 <- TAM::tam.mml( resp=resp, Y=Y, Q=Q )
 
 #--- tamaan specification
 tammodel <- "
@@ -1016,38 +1015,38 @@ 
                                                                      Examp
   ITEM TYPE:
     ALL(Rasch)
     "
-mod4_6t <- TAM::tamaan( tammodel, resp, Y=Y )
-summary(mod4_6t)
+mod4_6t <- TAM::tamaan( tammodel, resp, Y=Y )
+summary(mod4_6t)
 
 #############################################################################
 # EXAMPLE 5: 2-dimensional estimation with within item dimensionality
 #############################################################################
-library(mvtnorm)
+library(mvtnorm)
 # (1) simulate data
-set.seed(4762)
+set.seed(4762)
 N <- 2000 # 2000 persons
-Y <- stats::rnorm( N )
-theta <- mvtnorm::rmvnorm( N,mean=c(0,0), sigma=matrix( c(1,.5,.5,1), 2, 2 ))
+Y <- stats::rnorm( N )
+theta <- mvtnorm::rmvnorm( N,mean=c(0,0), sigma=matrix( c(1,.5,.5,1), 2, 2 ))
 I <- 10
 # 10 items load on the first dimension
-p1 <- stats::plogis( outer( theta[,1], seq( -2, 2, len=I ), "-" ) )
-resp1 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+p1 <- stats::plogis( outer( theta[,1], seq( -2, 2, len=I ), "-" ) )
+resp1 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
 # 10 items load on the second dimension
-p1 <- stats::plogis( outer( theta[,2], seq( -2, 2, len=I ), "-" ) )
-resp2 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+p1 <- stats::plogis( outer( theta[,2], seq( -2, 2, len=I ), "-" ) )
+resp2 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
 # 20 items load on both dimensions
-p1 <- stats::plogis( outer( 0.5*theta[,1] + 1.5*theta[,2], seq(-2,2,len=2*I ), "-" ))
-resp3 <- 1 * ( p1 > matrix( stats::runif( N*2*I ), nrow=N, ncol=2*I ) )
+p1 <- stats::plogis( outer( 0.5*theta[,1] + 1.5*theta[,2], seq(-2,2,len=2*I ), "-" ))
+resp3 <- 1 * ( p1 > matrix( stats::runif( N*2*I ), nrow=N, ncol=2*I ) )
 #Combine the two sets of items into one response matrix
-resp <- cbind(resp1, resp2, resp3 )
-colnames(resp) <- paste("I", 1:(4*I), sep="")
+resp <- cbind(resp1, resp2, resp3 )
+colnames(resp) <- paste("I", 1:(4*I), sep="")
 
 # (2) define loading matrix
-Q <- cbind(c(rep(1,10),rep(0,10),rep(1,20)), c(rep(0,10),rep(1,10),rep(1,20)))
+Q <- cbind(c(rep(1,10),rep(0,10),rep(1,20)), c(rep(0,10),rep(1,10),rep(1,20)))
 
 # (3) model: within item dimensionality and 2PL estimation
-mod5 <- TAM::tam.mml.2pl(resp, Q=Q, irtmodel="2PL" )
-summary(mod5)
+mod5 <- TAM::tam.mml.2pl(resp, Q=Q, irtmodel="2PL" )
+summary(mod5)
 
 # item difficulties
 mod5$item
@@ -1063,31 +1062,32 @@ 
                                                                      Examp
     F1 ~~ F2
     F2 ~~ 1*F2
     "
-mod5t <- TAM::tamaan( tammodel, resp,  control=list(maxiter=10))
-summary(mod5t)
+mod5t <- TAM::tamaan( tammodel, resp,  control=list(maxiter=10))
+summary(mod5t)
 
 #############################################################################
 # EXAMPLE 6: ordered data - Generalized partial credit model
 #############################################################################
-data(data.gpcm, package="TAM")
+data(data.gpcm, package="TAM")
 
 #************************************************************
 # Ex6.1: Nominal response model (irtmodel="2PL")
-mod6_1 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL", control=list(maxiter=200) )
+mod6_1 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL", control=list(maxiter=200) )
 mod6_1$item # item intercepts
 mod6_1$B    # for every category a separate slope parameter is estimated
 
 # reestimate the model with fixed item parameters
-mod6_1a <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL",
-       xsi.fixed=mod6_1$xsi.fixed.estimated,  B.fixed=mod6_1$B.fixed.estimated )
+mod6_1a <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL",
+       xsi.fixed=mod6_1$xsi.fixed.estimated,  B.fixed=mod6_1$B.fixed.estimated,
+       est.variance=TRUE )
 
 # estimate the model with initial item parameters from mod6_1
-mod6_1b <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL",
+mod6_1b <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL",
        xsi.inits=mod6_1$xsi.fixed.estimated,  B=mod6_1$B )
 
 #************************************************************
 # Ex6.2: Generalized partial credit model
-mod6_2 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="GPCM", control=list(maxiter=200))
+mod6_2 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="GPCM", control=list(maxiter=200))
 mod6_2$B[,2,]    # joint slope parameter for all categories
 
 #************************************************************
@@ -1095,55 +1095,55 @@ 
                                                                      Examp
 # B: nitems x maxK x ndim
 #   ( number of items x maximum number of categories x number of dimensions)
 # set two constraints
-B.fixed <- matrix( 0, 2, 4 )
+B.fixed <- matrix( 0, 2, 4 )
 # set second item, score of 2 (category 3), at first dimension to 2.3
-B.fixed[1,] <- c(2,3,1,2.3)
+B.fixed[1,] <- c(2,3,1,2.3)
 # set third item, score of 1 (category 2), at first dimension to 1.4
-B.fixed[2,] <- c(3,2,1,1.4)
+B.fixed[2,] <- c(3,2,1,1.4)
 
 # estimate item parameter with variance fixed (by default)
-mod6_3 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL", B.fixed=B.fixed,
-                 control=list( maxiter=200) )
+mod6_3 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL", B.fixed=B.fixed,
+                 control=list( maxiter=200) )
 mod6_3$B
 
 #************************************************************
 # Ex 6.4: estimate the same model, but estimate variance
-mod6_4 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL", B.fixed=B.fixed,
-               est.variance=TRUE, control=list( maxiter=350) )
+mod6_4 <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL", B.fixed=B.fixed,
+               est.variance=TRUE, control=list( maxiter=350) )
 mod6_4$B
 
 #************************************************************
 # Ex 6.5: partial credit model
-mod6_5 <- TAM::tam.mml( resp=data.gpcm,control=list( maxiter=200) )
+mod6_5 <- TAM::tam.mml( resp=data.gpcm,control=list( maxiter=200) )
 mod6_5$B
 
 #************************************************************
 # Ex 6.6: partial credit model: Conquest parametrization 'item+item*step'
-mod6_6 <- TAM::tam.mml( resp=data.gpcm, irtmodel="PCM2" )
-summary(mod6_6)
+mod6_6 <- TAM::tam.mml( resp=data.gpcm, irtmodel="PCM2" )
+summary(mod6_6)
 
 # estimate mod6_6 applying the sum constraint of item difficulties
 # modify design matrix of xsi paramters
-A1 <- TAM::.A.PCM2(resp=data.gpcm )
+A1 <- TAM::.A.PCM2(resp=data.gpcm )
 A1[3,2:4,"Comfort"] <- 1:3
 A1[3,2:4,"Work"] <- 1:3
 A1 <- A1[,, -3] # remove Benefit xsi item parameter
 # estimate model
-mod6_6b <- TAM::tam.mml( resp=data.gpcm, A=A1, beta.fixed=FALSE )
-summary(mod6_6b)
+mod6_6b <- TAM::tam.mml( resp=data.gpcm, A=A1, beta.fixed=FALSE )
+summary(mod6_6b)
 
 # estimate model with argument constraint="items"
-mod6_6c <- TAM::tam.mml( resp=data.gpcm, irtmodel="PCM2", constraint="items")
+mod6_6c <- TAM::tam.mml( resp=data.gpcm, irtmodel="PCM2", constraint="items")
 
 # estimate mod6_6 using tam.mml.mfr
-mod6_6d <- TAM::tam.mml.mfr( resp=data.gpcm, formulaA=~ 0 + item + item:step,
-    control=list(fac.oldxsi=.1), constraint="items" )
-summary(mod6_6d)
+mod6_6d <- TAM::tam.mml.mfr( resp=data.gpcm, formulaA=~ 0 + item + item:step,
+    control=list(fac.oldxsi=.1), constraint="items" )
+summary(mod6_6d)
 
 #************************************************************
 # Ex 6.7: Rating scale model: Conquest parametrization 'item+step'
-mod6_7 <- TAM::tam.mml( resp=data.gpcm, irtmodel="RSM" )
-summary(mod6_7)
+mod6_7 <- TAM::tam.mml( resp=data.gpcm, irtmodel="RSM" )
+summary(mod6_7)
 
 #************************************************************
 # Ex 6.8: sum constraint on item difficulties
@@ -1152,23 +1152,23 @@ 
                                                                      Examp
 #         compare to Example 16
 #
 
-data(data.timssAusTwn.scored)
+data(data.timssAusTwn.scored)
 dat <- data.timssAusTwn.scored[,1:11]
 
 ## > tail(sort(names(dat)),1) # constrained item
 ## [1] "M032761"
 
 # modify design matrix of xsi paramters
-A1 <- TAM::.A.PCM2( resp=dat )
+A1 <- TAM::.A.PCM2( resp=dat )
 # constrained item loads on every other main item parameter
 # with opposing margin it had been loaded on its own main item parameter
-A1["M032761",,setdiff(colnames(dat), "M032761")] <- -A1["M032761",,"M032761"]
+A1["M032761",,setdiff(colnames(dat), "M032761")] <- -A1["M032761",,"M032761"]
 # remove main item parameter for constrained item
-A1 <- A1[,, setdiff(dimnames(A1)[[3]],"M032761")]
+A1 <- A1[,, setdiff(dimnames(A1)[[3]],"M032761")]
 
 # estimate model
-mod6_8a <- TAM::tam.mml( resp=dat, A=A1, beta.fixed=FALSE )
-summary(mod6_8a)
+mod6_8a <- TAM::tam.mml( resp=dat, A=A1, beta.fixed=FALSE )
+summary(mod6_8a)
 # extract fixed item parameter for item M032761
 ## - sum(mod6_8a$xsi[setdiff(colnames(dat), "M032761"),"xsi"])
 
@@ -1176,67 +1176,67 @@ 
                                                                      Examp
 ## fixed a bug in 'tam.mml.mfr' for differing number of categories
 ## per item -> now a xsi vector with parameter fixings to values
 ## of 99 is used
-mod6_8b <- TAM::tam.mml.mfr( resp=dat, formulaA=~ 0 + item + item:step,
-                        control=list(fac.oldxsi=.1), constraint="items" )
-summary(mod6_8b)
+mod6_8b <- TAM::tam.mml.mfr( resp=dat, formulaA=~ 0 + item + item:step,
+                        control=list(fac.oldxsi=.1), constraint="items" )
+summary(mod6_8b)
 
 #************************************************************
 # Ex 6.9: sum constraint on item difficulties for irtmodel="PCM"
 
-data(data.timssAusTwn.scored)
+data(data.timssAusTwn.scored)
 dat <- data.timssAusTwn.scored[,2:11]
 dat[ dat==9 ] <- NA
 
 # obtain the design matrix for the PCM parametrization and
 # the number of categories for each item
-maxKi <- apply(dat, 2, max, na.rm=TRUE)
-des <- TAM::designMatrices(resp=dat)
+maxKi <- apply(dat, 2, max, na.rm=TRUE)
+des <- TAM::designMatrices(resp=dat)
 A1 <- des$A
 
 # define the constrained item category and remove the respective parameter
-(par <- unlist( strsplit(dimnames(A1)[[3]][dim(A1)[3]], split="_") ))
-A1 <- A1[,,-dim(A1)[3]]
+(par <- unlist( strsplit(dimnames(A1)[[3]][dim(A1)[3]], split="_") ))
+A1 <- A1[,,-dim(A1)[3]]
 
 # the item category loads on every other item category parameter with
 # opposing margin, balancing the number of categories for each item
-item.id <- which(colnames(dat)==par[1])
+item.id <- which(colnames(dat)==par[1])
 cat.id <- maxKi[par[1]]+1
-loading <- 1/rep(maxKi, maxKi)
-loading <- loading [-which(names(loading)==par[1])[1]]
+loading <- 1/rep(maxKi, maxKi)
+loading <- loading [-which(names(loading)==par[1])[1]]
 A1[item.id, cat.id, ] <- loading
 A1[item.id,,]
 
 # estimate model
-mod6_9 <- TAM::tam.mml( resp=dat, A=A1, beta.fixed=FALSE )
-summary(mod6_9)
+mod6_9 <- TAM::tam.mml( resp=dat, A=A1, beta.fixed=FALSE )
+summary(mod6_9)
 
 ## extract fixed item category parameter
 # calculate mean for each item
-ind.item.cat.pars <- sapply(colnames(dat), grep, rownames(mod6_8$xsi))
-item.means <- lapply(ind.item.cat.pars, function(ii) mean(mod6_8$xsi$xsi[ii]))
+ind.item.cat.pars <- sapply(colnames(dat), grep, rownames(mod6_8$xsi))
+item.means <- lapply(ind.item.cat.pars, function(ii) mean(mod6_8$xsi$xsi[ii]))
 
 # these sum up to the negative of the fixed parameter
-fix.par <- -sum( unlist(item.means), na.rm=TRUE)
+fix.par <- -sum( unlist(item.means), na.rm=TRUE)
 
 #************************************************************
 # Ex 6.10: Generalized partial credit model with equality constraints
 #          on item discriminations
 
-data(data.gpcm)
+data(data.gpcm)
 dat <- data.gpcm
 
 # Ex 6.10a: set all slopes of three items equal to each other
-E <- matrix( 1, nrow=3, ncol=1 )
-mod6_10a <- TAM::tam.mml.2pl( dat, irtmodel="GPCM.design", E=E  )
-summary(mod6_10a)
+E <- matrix( 1, nrow=3, ncol=1 )
+mod6_10a <- TAM::tam.mml.2pl( dat, irtmodel="GPCM.design", E=E  )
+summary(mod6_10a)
 mod6_10a$B[,,]
 
 # Ex 6.10b: equal slope for first and third item
-E <- matrix( 0, nrow=3, ncol=2 )
-E[c(1,3),1] <- 1
+E <- matrix( 0, nrow=3, ncol=2 )
+E[c(1,3),1] <- 1
 E[ 2, 2 ] <- 1
-mod6_10b <- TAM::tam.mml.2pl( dat, irtmodel="GPCM.design", E=E  )
-summary(mod6_10b)
+mod6_10b <- TAM::tam.mml.2pl( dat, irtmodel="GPCM.design", E=E  )
+summary(mod6_10b)
 mod6_10b$B[,,]
 
 #############################################################################
@@ -1244,38 +1244,38 @@ 
                                                                      Examp
 #############################################################################
 
 # (1) simulate data from a model with a (item slope) design matrix E
-set.seed(789)
+set.seed(789)
 I <- 42
-b <- seq( -2, 2, len=I)
+b <- seq( -2, 2, len=I)
 # create design matrix for loadings
-E <- matrix( 0, I, 5 )
-E[ seq(1,I,3), 1 ] <- 1
-E[ seq(2,I,3), 2 ] <- 1
-E[ seq(3,I,3), 3 ] <- 1
-ind <- seq( 1, I, 2 ) ; E[ ind, 4 ] <- rep( c( .3, -.2 ), I )[ 1:length(ind) ]
-ind <- seq( 2, I, 4 ) ; E[ ind, 5 ] <- rep( .15, I )[ 1:length(ind) ]
+E <- matrix( 0, I, 5 )
+E[ seq(1,I,3), 1 ] <- 1
+E[ seq(2,I,3), 2 ] <- 1
+E[ seq(3,I,3), 3 ] <- 1
+ind <- seq( 1, I, 2 ) ; E[ ind, 4 ] <- rep( c( .3, -.2 ), I )[ 1:length(ind) ]
+ind <- seq( 2, I, 4 ) ; E[ ind, 5 ] <- rep( .15, I )[ 1:length(ind) ]
 E
 
 # true basis slope parameters
-lambda <- c( 1, 1.2, 0.8, 1, 1.1 )
+lambda <- c( 1, 1.2, 0.8, 1, 1.1 )
 # calculate item slopes
 a <- E %*% lambda
 
 # simulate
 N <- 4000
-theta <- stats::rnorm( N )
-aM <- outer( rep(1,N), a[,1] )
-bM <- outer( rep(1,N), b )
-pM <- stats::plogis( aM * ( matrix( theta, nrow=N, ncol=I  ) - bM ) )
-dat <- 1 * ( pM > stats::runif( N*I ) )
-colnames(dat) <- paste("I", 1:I, sep="")
+theta <- stats::rnorm( N )
+aM <- outer( rep(1,N), a[,1] )
+bM <- outer( rep(1,N), b )
+pM <- stats::plogis( aM * ( matrix( theta, nrow=N, ncol=I  ) - bM ) )
+dat <- 1 * ( pM > stats::runif( N*I ) )
+colnames(dat) <- paste("I", 1:I, sep="")
 
 # estimate model
-mod7 <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM.design", E=E )
+mod7 <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM.design", E=E )
 mod7$B
 
 # recalculate estimated basis parameters
-stats::lm( mod7$B[,2,1] ~ 0+ as.matrix(E ) )
+stats::lm( mod7$B[,2,1] ~ 0+ as.matrix(E ) )
   ##   Call:
   ##   lm(formula=mod7$B[, 2, 1] ~ 0 + as.matrix(E))
   ##   Coefficients:
@@ -1287,30 +1287,30 @@ 
                                                                      Examp
 #  A first example of a Multifaceted Rasch Model                            #
 #  Facet is only female; 10 items are studied                               #
 #############################################################################
-data(data.ex08)
+data(data.ex08)
 
 formulaA <- ~ item+female+item*female
 # this formula is in R equivalent to 'item*female'
 resp <- data.ex08[["resp"]]
-facets <- as.data.frame( data.ex08[["facets"]] )
+facets <- as.data.frame( data.ex08[["facets"]] )
 
 #***
 # Model 8a: investigate gender DIF on all items
-mod8a <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA )
-summary(mod8a)
+mod8a <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA )
+summary(mod8a)
 
 #***
 # Model 8a 2: specification with long format response data
-resp.long <- c( data.ex08[["resp"]] )
-pid <- rep( 1:nrow(data.ex08[["resp"]]), ncol(data.ex08[["resp"]]) )
+resp.long <- c( data.ex08[["resp"]] )
+pid <- rep( 1:nrow(data.ex08[["resp"]]), ncol(data.ex08[["resp"]]) )
 
-itemnames <- rep(colnames(data.ex08[["resp"]]), each=nrow(data.ex08[["resp"]]))
-facets.long <- cbind( data.frame( "item"=itemnames ),
+itemnames <- rep(colnames(data.ex08[["resp"]]), each=nrow(data.ex08[["resp"]]))
+facets.long <- cbind( data.frame( "item"=itemnames ),
                  data.ex08[["facets"]][pid,,drop=F] )
 
-mod8a_2 <- TAM::tam.mml.mfr( resp=resp.long, facets=facets.long,
+mod8a_2 <- TAM::tam.mml.mfr( resp=resp.long, facets=facets.long,
                       formulaA=formulaA, pid=pid)
-stopifnot( all(mod8a$xsi.facets$xsi==mod8a_2$xsi.facets$xsi) )
+stopifnot( all(mod8a$xsi.facets$xsi==mod8a_2$xsi.facets$xsi) )
 
 #***
 # Model 8b: Differential bundle functioning (DBF)
@@ -1318,55 +1318,55 @@ 
                                                                      Examp
 
 # modify pre-specified design matrix to define 'appropriate' DBF effects
 formulaA <- ~ item*female
-des <- TAM::designMatrices.mfr( resp=resp, facets=facets, formulaA=formulaA)
+des <- TAM::designMatrices.mfr( resp=resp, facets=facets, formulaA=formulaA)
 A1 <- des$A$A.3d
 # item group A: items 1-5
 # item group B: items 6-8
 # item group C: items 9-10
 A1 <- A1[,,1:13]
-dimnames(A1)[[3]][ c(12,13) ] <- c("A:female1", "B:female1")
+dimnames(A1)[[3]][ c(12,13) ] <- c("A:female1", "B:female1")
 # item group A
 A1[,2,12] <- 0
-A1[c(1,5,7,9,11),2,12] <- -1
-A1[c(1,5,7,9,11)+1,2,12] <- 1
+A1[c(1,5,7,9,11),2,12] <- -1
+A1[c(1,5,7,9,11)+1,2,12] <- 1
 # item group B
 A1[,2,13] <- 0
-A1[c(13,15,17),2,13] <- -1
-A1[c(13,15,17)+1,2,13] <- 1
+A1[c(13,15,17),2,13] <- -1
+A1[c(13,15,17)+1,2,13] <- 1
 # item group C (define effect(A)+effect(B)+effect(C)=0)
-A1[c(19,3),2,c(12,13)] <- 1
-A1[c(19,3)+1,2,c(12,13)] <- -1
+A1[c(19,3),2,c(12,13)] <- 1
+A1[c(19,3)+1,2,c(12,13)] <- -1
 #   A1[,2,]   # look at modified design matrix
 # estimate model
-mod8b <- TAM::tam.mml( resp=des$gresp$gresp.noStep, A=A1 )
-summary(mod8b)
+mod8b <- TAM::tam.mml( resp=des$gresp$gresp.noStep, A=A1 )
+summary(mod8b)
 
 #############################################################################
 # EXAMPLE 9: Multifaceted Rasch Model
 #############################################################################
 
-data(data.sim.mfr)
-data(data.sim.facets)
+data(data.sim.mfr)
+data(data.sim.facets)
 
 # two way interaction item and rater
 formulaA <- ~item+item:step + item*rater
-mod9a <- TAM::tam.mml.mfr( resp=data.sim.mfr, facets=data.sim.facets, formulaA=formulaA)
+mod9a <- TAM::tam.mml.mfr( resp=data.sim.mfr, facets=data.sim.facets, formulaA=formulaA)
 mod9a$xsi.facets
-summary(mod9a)
+summary(mod9a)
 
 # three way interaction item, female and rater
 formulaA <- ~item+item:step + female*rater + female*item*step
-mod9b <- TAM::tam.mml.mfr( resp=data.sim.mfr, facets=data.sim.facets, formulaA=formulaA)
-summary(mod9b)
+mod9b <- TAM::tam.mml.mfr( resp=data.sim.mfr, facets=data.sim.facets, formulaA=formulaA)
+summary(mod9b)
 
 #############################################################################
 # EXAMPLE 10: Model with raters.
 #   Persons are arranged in multiple rows which is indicated
 #   by multiple person identifiers.
 #############################################################################
-data(data.ex10)
+data(data.ex10)
 dat <- data.ex10
-head(dat)
+head(dat)
   ##     pid rater I0001 I0002 I0003 I0004 I0005
   ## 1     1     1     0     1     1     0     0
   ## 451   1     2     1     1     1     1     0
@@ -1376,32 +1376,32 @@ 
                                                                      Examp
 
 facets <- dat[, "rater", drop=FALSE ] # define facet (rater)
 pid <- dat$pid      # define person identifier (a person occurs multiple times)
-resp <- dat[, -c(1:2) ]        # item response data
+resp <- dat[, -c(1:2) ]        # item response data
 formulaA <- ~ item * rater      # formula
 
-mod10 <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid )
-summary(mod10)
+mod10 <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid )
+summary(mod10)
 
 # estimate person parameter with WLE
-wmod10 <- TAM::tam.wle( mod10 )
+wmod10 <- TAM::tam.wle( mod10 )
 
 #--- Example 10a
 # compare model containing only item
 formulaA <- ~ item + rater      # pseudo formula for item
 xsi.setnull <- "rater"          # set all rater effects to zero
-mod10a <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA,
-            xsi.setnull=xsi.setnull, pid=dat$pid, beta.fixed=cbind(1,1,0))
-summary(mod10a)
+mod10a <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA,
+            xsi.setnull=xsi.setnull, pid=dat$pid, beta.fixed=cbind(1,1,0))
+summary(mod10a)
 
 # A shorter way for specifying this example is
 formulaA <- ~ item + 0*rater        # set all rater effects to zero
-mod10a1 <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid )
-summary(mod10a1)
+mod10a1 <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid )
+summary(mod10a1)
 
 # tam.mml.mfr also appropriately extends the facets data frame with pseudo facets
 # if necessary
 formulaA <- ~ item     # omitting the rater term
-mod10a2 <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid )
+mod10a2 <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA, pid=dat$pid )
   ##   Item Parameters Xsi
   ##              xsi se.xsi
   ##   I0001   -1.931  0.111
@@ -1414,25 +1414,25 @@ 
                                                                      Examp
 
 #***
 # Model 10_2: specification with long format response data
-resp.long <- c(unlist( dat[, -c(1:2) ] ))
+resp.long <- c(unlist( dat[, -c(1:2) ] ))
 
-pid <- rep( dat$pid, ncol(dat[, -c(1:2) ]) )
-itemnames <- rep(colnames(dat[, -c(1:2) ]), each=nrow(dat[, -c(1:2) ]))
+pid <- rep( dat$pid, ncol(dat[, -c(1:2) ]) )
+itemnames <- rep(colnames(dat[, -c(1:2) ]), each=nrow(dat[, -c(1:2) ]))
 
 # quick note: the 'trick' to use pid as the row index of the facet  (cf., used in Ex 8a_2)
 # is not working here, since pid already occures multiple times in the original response data
-facets <- cbind( data.frame("item"=itemnames),
-                 dat[ rep(1:nrow(dat), ncol(dat[,-c(1:2)])), "rater",drop=F]
+facets <- cbind( data.frame("item"=itemnames),
+                 dat[ rep(1:nrow(dat), ncol(dat[,-c(1:2)])), "rater",drop=F]
 )
 
-mod10_2 <- TAM::tam.mml.mfr( resp=resp.long, facets=facets, formulaA=formulaA, pid=pid)
+mod10_2 <- TAM::tam.mml.mfr( resp=resp.long, facets=facets, formulaA=formulaA, pid=pid)
 
-stopifnot( all(mod10$xsi.facets$xsi==mod10_2$xsi.facets$xsi) )
+stopifnot( all(mod10$xsi.facets$xsi==mod10_2$xsi.facets$xsi) )
 
 #############################################################################
 # EXAMPLE 11: Dichotomous data with missing and omitted responses
 #############################################################################
-data(data.ex11) ; dat <- data.ex11
+data(data.ex11) ; dat <- data.ex11
 
 #***
 # Model 11a: Calibration (item parameter estimating) in which omitted
@@ -1440,10 +1440,10 @@ 
                                                                      Examp
 dat1 <- dat[,-1]
 dat1[ dat1==9 ] <- NA
 # estimate Rasch model
-mod11a <- TAM::tam.mml( resp=dat1 )
-summary(mod11a)
+mod11a <- TAM::tam.mml( resp=dat1 )
+summary(mod11a)
 # compute person parameters
-wmod11a <- TAM::tam.wle( mod11a )
+wmod11a <- TAM::tam.wle( mod11a )
 
 #***
 # Model 11b: Scaling persons (WLE estimation) setting omitted
@@ -1452,36 +1452,36 @@ 
                                                                      Examp
 
 # set matrix with fixed item difficulties as the input
 xsi1 <- mod11a$xsi    # xsi output from Model 11a
-xsi.fixed <- cbind( seq(1,nrow(xsi1) ), xsi1$xsi )
+xsi.fixed <- cbind( seq(1,nrow(xsi1) ), xsi1$xsi )
 # recode 9 to 0
 dat2 <- dat[,-1]
 dat2[ dat2==9 ] <- 0
 # run Rasch model with fixed item difficulties
-mod11b <- TAM::tam.mml( resp=dat2, xsi.fixed=xsi.fixed )
-summary(mod11b)
+mod11b <- TAM::tam.mml( resp=dat2, xsi.fixed=xsi.fixed )
+summary(mod11b)
 # WLE estimation
-wmod11b <- TAM::tam.wle( mod11b )
+wmod11b <- TAM::tam.wle( mod11b )
 
 #############################################################################
 # EXAMPLE 12: Avoiding nonconvergence using the argument increment.factor
 #############################################################################
-data(data.ex12)
+data(data.ex12)
 dat <- data.ex12
 
 # non-convergence without increment.factor
-mod1 <- TAM::tam.mml.2pl( resp=data.ex12, control=list( maxiter=1000) )
+mod1 <- TAM::tam.mml.2pl( resp=data.ex12, control=list( maxiter=1000) )
 
 # avoiding non-convergence with increment.factor=1.02
-mod2 <- TAM::tam.mml.2pl( resp=data.ex12,
-            control=list( maxiter=1000, increment.factor=1.02) )
-summary(mod1)
-summary(mod2)
+mod2 <- TAM::tam.mml.2pl( resp=data.ex12,
+            control=list( maxiter=1000, increment.factor=1.02) )
+summary(mod1)
+summary(mod2)
 
 #############################################################################
 # EXAMPLE 13: Longitudinal data 'data.long' from sirt package
 #############################################################################
-library(sirt)
-data(data.long, package="sirt")
+library(sirt)
+data(data.long, package="sirt")
 dat <- data.long
   ##   > colnames(dat)
   ##    [1] "idstud" "I1T1"   "I2T1"   "I3T1"   "I4T1"   "I5T1"   "I6T1"
@@ -1494,23 +1494,23 @@ 
                                                                      Examp
 # Model 13a: 2-dimensional Rasch model assuming invariant item difficulties
 
 # define matrix loadings
-Q <- matrix(0,12,2)
-colnames(Q) <- c("T1","T2")
+Q <- matrix(0,12,2)
+colnames(Q) <- c("T1","T2")
 Q[1:6,1] <- 1       # items at T1
 Q[7:12,2] <- 1      # items at T2
 
 # assume equal item difficulty of I3T1 and I3T2, I4T1 and I4T2, ...
 # create draft design matrix and modify it
-A <- TAM::designMatrices(resp=data.long[,-1])$A
-dimnames(A)[[1]] <- colnames(data.long)[-1]
+A <- TAM::designMatrices(resp=data.long[,-1])$A
+dimnames(A)[[1]] <- colnames(data.long)[-1]
   ##   > str(A)
   ##    num [1:12, 1:2, 1:12] 0 0 0 0 0 0 0 0 0 0 ...
   ##    - attr(*, "dimnames")=List of 3
   ##     ..$ : chr [1:12] "Item01" "Item02" "Item03" "Item04" ...
   ##     ..$ : chr [1:2] "Category0" "Category1"
   ##     ..$ : chr [1:12] "I1T1" "I2T1" "I3T1" "I4T1" ...
-A1 <- A[,, c(1:6, 11:12 ) ]
-dimnames(A1)[[3]] <- substring( dimnames(A1)[[3]],1,2)
+A1 <- A[,, c(1:6, 11:12 ) ]
+dimnames(A1)[[3]] <- substring( dimnames(A1)[[3]],1,2)
 A1[7,2,3] <- -1     # difficulty(I3T1)=difficulty(I3T2)
 A1[8,2,4] <- -1     # I4T1=I4T2
 A1[9,2,5] <- A1[10,2,6] <- -1
@@ -1531,9 +1531,9 @@ 
                                                                      Examp
 
 # estimate model
 # set intercept of second dimension (T2) to zero
-beta.fixed <- cbind( 1, 2, 0 )
-mod13a <- TAM::tam.mml( resp=data.long[,-1], Q=Q, A=A1, beta.fixed=beta.fixed)
-summary(mod13a)
+beta.fixed <- cbind( 1, 2, 0 )
+mod13a <- TAM::tam.mml( resp=data.long[,-1], Q=Q, A=A1, beta.fixed=beta.fixed)
+summary(mod13a)
 
 #--- tamaan specification
 tammodel <- "
@@ -1554,17 +1554,17 @@ 
                                                                      Examp
   ##         I%T1 + I%T2 | b%*t1
   ##       DOEND
 # estimate model
-mod13at <- TAM::tamaan( tammodel, resp=data.long,  beta.fixed=beta.fixed )
-summary(mod13at)
+mod13at <- TAM::tamaan( tammodel, resp=data.long,  beta.fixed=beta.fixed )
+summary(mod13at)
 
 #***
 # Model 13b: invariant item difficulties with zero mean item difficulty
 #           of anchor items
 
-A <- TAM::designMatrices(resp=data.long[,-1])$A
-dimnames(A)[[1]] <- colnames(data.long)[-1]
-A1 <- A[,, c(1:5, 11:12 ) ]
-dimnames(A1)[[3]] <- substring( dimnames(A1)[[3]],1,2)
+A <- TAM::designMatrices(resp=data.long[,-1])$A
+dimnames(A)[[1]] <- colnames(data.long)[-1]
+A1 <- A[,, c(1:5, 11:12 ) ]
+dimnames(A1)[[3]] <- substring( dimnames(A1)[[3]],1,2)
 A1[7,2,3] <- -1     # difficulty(I3T1)=difficulty(I3T2)
 A1[8,2,4] <- -1     # I4T1=I4T2
 A1[9,2,5] <- -1
@@ -1585,19 +1585,19 @@ 
                                                                      Examp
   ## I7T2  0  0  0  0  0 -1  0
   ## I8T2  0  0  0  0  0  0 -1
 
-mod13b <- TAM::tam.mml( resp=data.long[,-1], Q=Q, A=A1, beta.fixed=FALSE)
-summary(mod13b)
+mod13b <- TAM::tam.mml( resp=data.long[,-1], Q=Q, A=A1, beta.fixed=FALSE)
+summary(mod13b)
 
 #***
 # Model 13c: longitudinal polytomous data
 #
 
 # modifiy Items I1T1, I4T1, I4T2 in order to be trichotomous (codes: 0,1,2)
-set.seed(42)
+set.seed(42)
 dat <- data.long
-dat[(1:50),2] <- sample(c(0,1,2), 50, replace=TRUE)
-dat[(1:50),5] <- sample(c(0,1,2), 50, replace=TRUE)
-dat[(1:50),9] <- sample(c(0,1,2), 50, replace=TRUE)
+dat[(1:50),2] <- sample(c(0,1,2), 50, replace=TRUE)
+dat[(1:50),5] <- sample(c(0,1,2), 50, replace=TRUE)
+dat[(1:50),9] <- sample(c(0,1,2), 50, replace=TRUE)
   ##   > colnames(dat)
   ##    [1] "idstud" "I1T1"   "I2T1"   "I3T1"   "I4T1"   "I5T1"   "I6T1"
   ##    [8] "I3T2"   "I4T2"   "I5T2"   "I6T2"   "I7T2"   "I8T2"
@@ -1606,15 +1606,15 @@ 
                                                                      Examp
 ## items 3 to 6 are anchor items
 
 # (1) define matrix loadings
-Q <- matrix(0,12,2)
-colnames(Q) <- c("T1","T2")
+Q <- matrix(0,12,2)
+colnames(Q) <- c("T1","T2")
 Q[1:6,1] <- 1       # items at T1
 Q[7:12,2] <- 1      # items at T2
 
 # (2) assume equal item difficulty of anchor items
 #     create draft design matrix and modify it
-A <- TAM::designMatrices(resp=dat[,-1])$A
-dimnames(A)[[1]] <- colnames(dat)[-1]
+A <- TAM::designMatrices(resp=dat[,-1])$A
+dimnames(A)[[1]] <- colnames(dat)[-1]
   ## > str(A)
   ## num [1:12, 1:3, 1:15] 0 0 0 0 0 0 0 0 0 0 ...
   ## - attr(*, "dimnames")=List of 3
@@ -1626,8 +1626,8 @@ 
                                                                      Examp
 # Items 1 to 3 administered at T1, Items 3 to 6 are anchor items
 # Item 7 to 8 administered at T2
 # Item I1T1, I4T1, I4T2 are trichotomous (codes: 0,1,2)
-A1 <- A[,, c(1:8, 14:15) ]
-dimnames(A1)[[3]] <- gsub("T1|T2", "",  dimnames(A1)[[3]])
+A1 <- A[,, c(1:8, 14:15) ]
+dimnames(A1)[[3]] <- gsub("T1|T2", "",  dimnames(A1)[[3]])
 
 # Modifications are shortened compared to Model 13 a, but are still valid
 A1[7,,] <- A1[3,,]  # item 7, i.e. I3T2, loads on same parameters as
@@ -1643,36 +1643,36 @@ 
                                                                      Examp
 
 # (3) estimate model
 #     set intercept of second dimension (T2) to zero
-beta.fixed <- cbind( 1, 2, 0 )
-mod13c <- TAM::tam.mml( resp=dat[,-1], Q=Q, A=A1, beta.fixed=beta.fixed,
+beta.fixed <- cbind( 1, 2, 0 )
+mod13c <- TAM::tam.mml( resp=dat[,-1], Q=Q, A=A1, beta.fixed=beta.fixed,
                    irtmodel="PCM")
-summary(mod13c)
-wle.mod13c <- TAM::tam.wle(mod13c) # WLEs of dimension T1 and T2
+summary(mod13c)
+wle.mod13c <- TAM::tam.wle(mod13c) # WLEs of dimension T1 and T2
 
 #############################################################################
 # EXAMPLE 14: Facet model with latent regression
 #############################################################################
-data( data.ex14 )
+data( data.ex14 )
 dat <- data.ex14
 
 #***
 # Model 14a: facet model
-resp <- dat[, paste0("crit",1:7,sep="") ]    # item data
-facets <- data.frame( "rater"=dat$rater )     # define facets
+resp <- dat[, paste0("crit",1:7,sep="") ]    # item data
+facets <- data.frame( "rater"=dat$rater )     # define facets
 formulaA <- ~item+item*step + rater
-mod14a <- TAM::tam.mml.mfr( resp, facets=facets, formulaA=formulaA, pid=dat$pid )
-summary(mod14a)
+mod14a <- TAM::tam.mml.mfr( resp, facets=facets, formulaA=formulaA, pid=dat$pid )
+summary(mod14a)
 
 #***
 # Model 14b: facet model with latent regression
 #   Note that dataY must correspond to rows in resp and facets which means
 #   that there must be the same rows in Y for a person with multiple rows
 #   in resp
-dataY <- dat[, c("X1","X2") ]        # latent regressors
+dataY <- dat[, c("X1","X2") ]        # latent regressors
 formulaY <- ~ X1+X2            # latent regression formula
-mod14b <- TAM::tam.mml.mfr( resp, facets=facets, formulaA=formulaA,
+mod14b <- TAM::tam.mml.mfr( resp, facets=facets, formulaA=formulaA,
             dataY=dataY, formulaY=formulaY, pid=dat$pid)
-summary(mod14b)
+summary(mod14b)
 
 #***
 # Model 14c: Multi-facet model with item slope estimation
@@ -1681,29 +1681,29 @@ 
                                                                      Examp
 
 resp1 <- mod14a$resp      # extract response data with generalized items
 A <- mod14a$A             # extract design matrix for item intercepts
-colnames(resp1)
+colnames(resp1)
 
 # define design matrix for slopes
-E <- matrix( 0, nrow=ncol(resp1), ncol=7 )
-colnames(E) <- paste0("crit",1:7)
-rownames(E) <- colnames(resp1)
-E[ cbind( 1:(7*7), rep(1:7,each=7) ) ] <- 1
+E <- matrix( 0, nrow=ncol(resp1), ncol=7 )
+colnames(E) <- paste0("crit",1:7)
+rownames(E) <- colnames(resp1)
+E[ cbind( 1:(7*7), rep(1:7,each=7) ) ] <- 1
 
-mod14c <- TAM::tam.mml.2pl( resp=resp1, A=A, irtmodel="GPCM.design", E=E,
-        control=list(maxiter=100) )
-summary(mod14c)
+mod14c <- TAM::tam.mml.2pl( resp=resp1, A=A, irtmodel="GPCM.design", E=E,
+        control=list(maxiter=100) )
+summary(mod14c)
 
 #############################################################################
 # EXAMPLE 15: Coping with nonconvergent models
 #############################################################################
 
-data(data.ex15)
+data(data.ex15)
 data <- data.ex15
 # facet model 'group*item' is of interest
 
 #***
 # Model 15a:
-mod15a <- TAM::tam.mml.mfr(resp=data[,-c(1:2)],facets=data[,"group",drop=FALSE],
+mod15a <- TAM::tam.mml.mfr(resp=data[,-c(1:2)],facets=data[,"group",drop=FALSE],
     formulaA=~ item + group*item, pid=data$pid )
 # See output:
   ##
@@ -1720,31 +1720,31 @@ 
                                                                      Examp
 #***
 # Model 15b: Follow the suggestions of changing the default of fac.oldxsi and
 #            increment.factor
-mod15b <- TAM::tam.mml.mfr(resp=data[,-c(1:2)],facets=data[,"group",drop=FALSE],
+mod15b <- TAM::tam.mml.mfr(resp=data[,-c(1:2)],facets=data[,"group",drop=FALSE],
             formulaA=~ group*item, pid=data$pid,
-            control=list( increment.factor=1.03, fac.oldxsi=.4 ) )
+            control=list( increment.factor=1.03, fac.oldxsi=.4 ) )
 
 #***
 # Model 15c: Alternatively, just choose more iterations in M-step by "Msteps=10"
-mod15c <- TAM::tam.mml.mfr(resp=data[,-c(1:2)],facets=data[,"group",drop=FALSE],
+mod15c <- TAM::tam.mml.mfr(resp=data[,-c(1:2)],facets=data[,"group",drop=FALSE],
     formulaA=~ item + group*item, pid=data$pid,
-    control=list(maxiter=250, Msteps=10))
+    control=list(maxiter=250, Msteps=10))
 
 #############################################################################
 # EXAMPLE 16: Differential item function for polytomous items and
 #             differing number of response options per item
 #############################################################################
 
-data(data.timssAusTwn.scored)
+data(data.timssAusTwn.scored)
 dat <- data.timssAusTwn.scored
 # extract item response data
-resp <- dat[, sort(grep("M", colnames(data.timssAusTwn.scored), value=TRUE)) ]
+resp <- dat[, sort(grep("M", colnames(data.timssAusTwn.scored), value=TRUE)) ]
 # some descriptives
-psych::describe(resp)
+psych::describe(resp)
 # define facets: 'cnt' is group identifier
-facets <- data.frame( "cnt"=dat$IDCNTRY)
+facets <- data.frame( "cnt"=dat$IDCNTRY)
 # create design matrices
-des2 <- TAM::designMatrices.mfr2( resp=resp, facets=facets,
+des2 <- TAM::designMatrices.mfr2( resp=resp, facets=facets,
                    formulaA=~item*step + item*cnt)
 # restructured data set: pseudoitem=item x country
 resp2 <- des2$gresp$gresp.noStep
@@ -1756,27 +1756,27 @@ 
                                                                      Examp
 # extract loading matrix B
 B <- des2$B$B.3d
 # estimate model
-mod1 <- TAM::tam.mml( resp=resp2, A=A, B=B, control=list(maxiter=100) )
-summary(mod1)
+mod1 <- TAM::tam.mml( resp=resp2, A=A, B=B, control=list(maxiter=100) )
+summary(mod1)
 # The sum of all DIF parameters is set to zero. The DIF parameter for the last
 # item is therefore obtained
 xsi1 <- mod1$xsi
-difxsi <- xsi1[ intersect( grep("cnt",rownames(xsi1)),
-              grep("M03",rownames(xsi1))), ]   - colSums(difxsi)
+difxsi <- xsi1[ intersect( grep("cnt",rownames(xsi1)),
+              grep("M03",rownames(xsi1))), ]   - colSums(difxsi)
     # this is the DIF effect of the remaining item
 
 #############################################################################
 # EXAMPLE 17: Several multidimensional and subdimension models
 #############################################################################
 
-library(mirt)
+library(mirt)
 #*** (1) simulate data in mirt package
-set.seed(9897)
+set.seed(9897)
 # simulate data according to the four-dimensional Rasch model
 # variances
-variances <- c( 1.45, 1.74, .86, 1.48  )
+variances <- c( 1.45, 1.74, .86, 1.48  )
 # correlations
-corrs <- matrix( 1, 4, 4 )
+corrs <- matrix( 1, 4, 4 )
 dd1 <- 1 ; dd2 <- 2 ; corrs[dd1,dd2] <- corrs[dd2,dd1] <- .88
 dd1 <- 1 ; dd2 <- 3 ; corrs[dd1,dd2] <- corrs[dd2,dd1] <- .85
 dd1 <- 1 ; dd2 <- 4 ; corrs[dd1,dd2] <- corrs[dd2,dd1] <- .87
@@ -1784,16 +1784,16 @@ 
                                                                      Examp
 dd1 <- 2 ; dd2 <- 4 ; corrs[dd1,dd2] <- corrs[dd2,dd1] <- .90
 dd1 <- 3 ; dd2 <- 4 ; corrs[dd1,dd2] <- corrs[dd2,dd1] <- .90
 # covariance matrix
-covar <- outer( sqrt( variances), sqrt(variances) )*corrs
+covar <- outer( sqrt( variances), sqrt(variances) )*corrs
 # item thresholds and item discriminations
-d <- matrix( stats::runif(40, -2, 2 ), ncol=1 )
-a <- matrix(NA, nrow=40,ncol=4)
+d <- matrix( stats::runif(40, -2, 2 ), ncol=1 )
+a <- matrix(NA, nrow=40,ncol=4)
 a[1:10,1] <- a[11:20,2] <- a[21:30,3] <- a[31:40,4] <- 1
 # simulate data
-dat <- mirt::simdata(a=a, d=d, N=1000, itemtype="dich", sigma=covar )
+dat <- mirt::simdata(a=a, d=d, N=1000, itemtype="dich", sigma=covar )
 # define Q-matrix for testlet and subdimension models estimated below
-Q <- matrix( 0, nrow=40, ncol=5 )
-colnames(Q) <- c("g", paste0( "subd", 1:4) )
+Q <- matrix( 0, nrow=40, ncol=5 )
+colnames(Q) <- c("g", paste0( "subd", 1:4) )
 Q[,1] <- 1
 Q[1:10,2] <- Q[11:20,3] <- Q[21:30,4] <- Q[31:40,5] <- 1
 
@@ -1808,18 +1808,18 @@ 
                                                                      Examp
 # define a user function for restricting the variance according to the
 # Rasch testlet model
 variance.fct1 <- function( variance ){
-            ndim <- ncol(variance)
-            variance.new <- matrix( 0, ndim, ndim )
-            diag(variance.new) <- diag(variance)
+            ndim <- ncol(variance)
+            variance.new <- matrix( 0, ndim, ndim )
+            diag(variance.new) <- diag(variance)
             variance <- variance.new
-            return(variance)
+            return(variance)
                     }
 variance.Npars <- 5    # number of estimated parameters in variance matrix
 # estimation using tam.mml
-mod1 <- TAM::tam.mml( dat, Q=Q, userfct.variance=variance.fct1,
-             variance.Npars=variance.Npars, control=list(maxiter=maxit, QMC=TRUE,
+mod1 <- TAM::tam.mml( dat, Q=Q, userfct.variance=variance.fct1,
+             variance.Npars=variance.Npars, control=list(maxiter=maxit, QMC=TRUE,
                           snodes=snodes))
-summary(mod1)
+summary(mod1)
 
 #*****************
 # Model 2: Testlet model with correlated testlet effects
@@ -1833,25 +1833,25 @@ 
                                                                      Examp
 
 # set Cov(g,u_t)=0 and sum of all testlet covariances equals to zero
 variance.fct2 <- function( variance ){
-            ndim <- ncol(variance)
-            variance.new <- matrix( 0, ndim, ndim )
-            diag(variance.new) <- diag(variance)
+            ndim <- ncol(variance)
+            variance.new <- matrix( 0, ndim, ndim )
+            diag(variance.new) <- diag(variance)
             variance.new[1,2:ndim] <- variance.new[2:ndim,1] <- 0
             # calculate average covariance between testlets
             v1 <- variance[ -1, -1] - variance.new[-1,-1]
-            M1 <- sum(v1) / ( ( ndim-1)^2 - ( ndim - 1))
+            M1 <- sum(v1) / ( ( ndim-1)^2 - ( ndim - 1))
             v1 <- v1 - M1
             variance.new[ -1, -1 ] <- v1
-            diag(variance.new) <- diag(variance)
+            diag(variance.new) <- diag(variance)
             variance <- variance.new
-            return(variance)
+            return(variance)
                     }
 variance.Npars <- 1 + 4 + (4*3)/2 - 1
 # estimate model in TAM
-mod2 <- TAM::tam.mml( dat, Q=Q, userfct.variance=variance.fct2,
+mod2 <- TAM::tam.mml( dat, Q=Q, userfct.variance=variance.fct2,
                 variance.Npars=variance.Npars,
-                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
-summary(mod2)
+                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
+summary(mod2)
 
 #*****************
 # Model 3: Testlet model with correlated testlet effects (different identification)
@@ -1861,28 +1861,28 @@ 
                                                                      Examp
 # \sum _t,t' Cov(u_t, u_t') + \sum_t Var(u_t)=0, i.e.
 # the sum of all testlet covariances and variances is equal to zero.
 variance.fct3 <- function( variance ){
-            ndim <- ncol(variance)
-            variance.new <- matrix( 0, ndim, ndim )
-            diag(variance.new) <- diag(variance)
+            ndim <- ncol(variance)
+            variance.new <- matrix( 0, ndim, ndim )
+            diag(variance.new) <- diag(variance)
             variance.new[1,2:ndim] <- variance.new[2:ndim,1] <- 0
             # calculate average covariance and variance between testlets
             v1 <- variance[ -1, -1]
-            M1 <- mean(v1)
+            M1 <- mean(v1)
             v1 <- v1 - M1
             variance.new[ -1, -1 ] <- v1
             # ensure positive definiteness of covariance matrix
             eps <- 10^(-2)
-            diag(variance.new) <- diag( variance.new) + eps
-            variance.new <- psych::cor.smooth( variance.new )  # smoothing in psych
+            diag(variance.new) <- diag( variance.new) + eps
+            variance.new <- psych::cor.smooth( variance.new )  # smoothing in psych
             variance <- variance.new
-            return(variance)
+            return(variance)
                     }
 variance.Npars <- 1 + 4 + (4*3)/2 - 1
 # estimate model in TAM
-mod3 <- TAM::tam.mml( dat, Q=Q, userfct.variance=variance.fct3,
+mod3 <- TAM::tam.mml( dat, Q=Q, userfct.variance=variance.fct3,
                 variance.Npars=variance.Npars,
-                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
-summary(mod3)
+                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
+summary(mod3)
 
 #*****************
 # Model 4: Rasch subdimension model
@@ -1895,11 +1895,11 @@ 
                                                                      Examp
 Q2[31:40,2:4] <- -1
 
 # set Cov(g,u1)=Cov(g,u2)=Cov(g,u3)=0
-variance.fixed <- rbind( c(1,2,0), c(1,3,0), c(1,4,0) )
+variance.fixed <- rbind( c(1,2,0), c(1,3,0), c(1,4,0) )
 # estimate model in TAM
-mod4 <- TAM::tam.mml( dat, Q=Q2,variance.fixed=variance.fixed,
-                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
-summary(mod4)
+mod4 <- TAM::tam.mml( dat, Q=Q2,variance.fixed=variance.fixed,
+                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
+summary(mod4)
 
 #*****************
 # Model 5: Higher-order model
@@ -1909,29 +1909,29 @@ 
                                                                      Examp
 # F_t=a_t g + eps_g
 Q3 <- Q[,-1]
 # define fitting function using the lavaan package and ULS estimation
-N0 <- nrow(dat)         # sample size of dataset
-library(lavaan)        # requires lavaan package for fitting covariance
+N0 <- nrow(dat)         # sample size of dataset
+library(lavaan)        # requires lavaan package for fitting covariance
 variance.fct5 <- function( variance ){
-    ndim <- ncol(variance)
-    rownames(variance) <- colnames(variance) <- paste0("F",1:ndim)
-    lavmodel <- paste0(
-        "FHO=~", paste0( paste0( "F", 1:ndim ), collapse="+" ) )
-    lavres <- lavaan::cfa( model=lavmodel, sample.cov=variance, estimator="ULS",
+    ndim <- ncol(variance)
+    rownames(variance) <- colnames(variance) <- paste0("F",1:ndim)
+    lavmodel <- paste0(
+        "FHO=~", paste0( paste0( "F", 1:ndim ), collapse="+" ) )
+    lavres <- lavaan::cfa( model=lavmodel, sample.cov=variance, estimator="ULS",
                        std.lv=TRUE, sample.nobs=N0)
-    variance.new <- fitted(lavres)$cov
+    variance.new <- fitted(lavres)$cov
     variance <- variance.new
     # print coefficients
-    cat( paste0( "\n **** Higher order loadings: ",
-            paste0( paste0( round( coef(lavres)[ 1:ndim ], 3 )), collapse=" ")
+    cat( paste0( "\n **** Higher order loadings: ",
+            paste0( paste0( round( coef(lavres)[ 1:ndim ], 3 )), collapse=" ")
                         ), "\n")
-    return(variance)
+    return(variance)
                     }
 variance.Npars <- 4+4
 # estimate model in TAM
-mod5 <- TAM::tam.mml( dat, Q=Q3, userfct.variance=variance.fct5,
+mod5 <- TAM::tam.mml( dat, Q=Q3, userfct.variance=variance.fct5,
                 variance.Npars=variance.Npars,
-                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
-summary(mod5)
+                control=list(maxiter=maxit, QMC=TRUE, snodes=snodes) )
+summary(mod5)
 
 #*****************
 # Model 6: Generalized Rasch subdimension model (Brandt, 2012)
@@ -1940,70 +1940,70 @@ 
                                                                      Examp
 Q2 <- Q[,-5]
 Q2[31:40,2:4] <- -1
 # fixed covariances
-variance.fixed2 <- rbind( c(1,2,0), c(1,3,0), c(1,4,0)  )
+variance.fixed2 <- rbind( c(1,2,0), c(1,3,0), c(1,4,0)  )
 # design matrix for item loading parameters
 #      items x category x dimension x xsi parameter
-E <- array( 0, dim=c( 40, 2, 4, 4 ) )
-E[ 1:10, 2, c(1,2), 1 ] <- 1
-E[ 11:20, 2, c(1,3), 2 ] <- 1
-E[ 21:30, 2, c(1,4), 3 ] <- 1
+E <- array( 0, dim=c( 40, 2, 4, 4 ) )
+E[ 1:10, 2, c(1,2), 1 ] <- 1
+E[ 11:20, 2, c(1,3), 2 ] <- 1
+E[ 21:30, 2, c(1,4), 3 ] <- 1
 E[ 31:40, 2, 1, 4 ] <- 1
 E[ 31:40, 2, 2:4, 4 ] <- -1
 
 # constraint on slope parameters, see Brandt (2012)
 gammaconstr <- function( gammaslope ){
-        K <- length( gammaslope)
-        g1 <- sum( gammaslope^2  )
-        gammaslope.new <- sqrt(K) / sqrt(g1) * gammaslope
-        return(gammaslope.new)
+        K <- length( gammaslope)
+        g1 <- sum( gammaslope^2  )
+        gammaslope.new <- sqrt(K) / sqrt(g1) * gammaslope
+        return(gammaslope.new)
                     }
 # estimate model
-mod6 <- TAM::tam.mml.3pl( dat, E=E, Q=Q2, variance.fixed=variance.fixed2,
+mod6 <- TAM::tam.mml.3pl( dat, E=E, Q=Q2, variance.fixed=variance.fixed2,
            skillspace="normal", userfct.gammaslope=gammaconstr, gammaslope.constr.Npars=1,
-           control=list(maxiter=maxit, QMC=TRUE, snodes=snodes ) )
-summary(mod6)
+           control=list(maxiter=maxit, QMC=TRUE, snodes=snodes ) )
+summary(mod6)
 
 #############################################################################
 # EXAMPLE 18: Partial credit model with dimension-specific sum constraints
 #             on item difficulties
 #############################################################################
 
-data(data.Students, package="CDM")
-dat <- data.Students[, c( paste0("sc",1:4), paste0("mj",1:4) ) ]
+data(data.Students, package="CDM")
+dat <- data.Students[, c( paste0("sc",1:4), paste0("mj",1:4) ) ]
 # specify dimensions in Q-matrix
-Q <- matrix( 0,  nrow=8, ncol=2 )
+Q <- matrix( 0,  nrow=8, ncol=2 )
 Q[1:4,1] <- Q[5:8,2] <- 1
 # partial credit model with some constraint on item parameters
-mod1 <- TAM::tam.mml( dat, Q=Q, irtmodel="PCM2", constraint="items")
-summary(mod1)
+mod1 <- TAM::tam.mml( dat, Q=Q, irtmodel="PCM2", constraint="items")
+summary(mod1)
 
 #############################################################################
 # EXAMPLE 19: Partial credit scoring: 0.5 points for partial credit items
 #             and 1 point for dichotomous items
 #############################################################################
 
-data(data.timssAusTwn.scored)
+data(data.timssAusTwn.scored)
 dat <- data.timssAusTwn.scored
 # extrcat item response data
-dat <- dat[, grep("M03", colnames(dat) ) ]
+dat <- dat[, grep("M03", colnames(dat) ) ]
 
 # select items with do have maximum score of 2 (polytomous items)
-ind <- which( apply( dat,  2, max, na.rm=TRUE )==2 )
-I <- ncol(dat)
+ind <- which( apply( dat,  2, max, na.rm=TRUE )==2 )
+I <- ncol(dat)
 # define Q-matrix with scoring variant
-Q <- matrix( 1, nrow=I, ncol=1 )
+Q <- matrix( 1, nrow=I, ncol=1 )
 Q[ ind, 1 ] <- .5    # score of 0.5 for polyomous items
 
 # estimate model
-mod1 <- TAM::tam.mml( dat, Q=Q, irtmodel="PCM2", control=list(nodes=seq(-10,10,len=21)))
-summary(mod1)
+mod1 <- TAM::tam.mml( dat, Q=Q, irtmodel="PCM2", control=list(nodes=seq(-10,10,len=21)))
+summary(mod1)
 
 #############################################################################
 # EXAMPLE 20: Specification of loading matrix in multidimensional model
 #############################################################################
 
-data(data.gpcm)
-psych::describe(data.gpcm)
+data(data.gpcm)
+psych::describe(data.gpcm)
 resp <- data.gpcm
 
 # define three dimensions and different loadings of item categories
@@ -2012,13 +2012,13 @@ 
                                                                      Examp
 D <- 3  # 3 dimensions
 # define loading matrix B
 # 4 categories for each item (0,1,2,3)
-B <- array( 0, dim=c(I,4,D) )
+B <- array( 0, dim=c(I,4,D) )
 for (ii in 1:I){
     B[ ii, 1:4, 1 ] <- 0:3
     B[ ii, 1,2 ] <- 1
     B[ ii, 4,3 ] <- 1
             }
-dimnames(B)[[1]] <- colnames(resp)
+dimnames(B)[[1]] <- colnames(resp)
 B[1,,]
   ##   > B[1,,]
   ##        [,1] [,2] [,3]
@@ -2027,32 +2027,32 @@ 
                                                                      Examp
   ##   [3,]    2    0    0
   ##   [4,]    3    0    1
 #-- test run
-mod1 <- TAM::tam.mml( resp, B=B, control=list( snodes=1000, maxiter=5)  )
-summary(mod1)
+mod1 <- TAM::tam.mml( resp, B=B, control=list( snodes=1000, maxiter=5)  )
+summary(mod1)
 
 # Same model with TAM::tam.mml.3pl instead
 
-dim4 <- sum(apply(B, c(1, 3), function(x) any(!(x==0))))
-E1 <- array(0, dim=c(dim(B), dim4))
+dim4 <- sum(apply(B, c(1, 3), function(x) any(!(x==0))))
+E1 <- array(0, dim=c(dim(B), dim4))
 
 kkk <- 0
-for (iii in seq_len(dim(E1)[1])) {
-    for (jjj in seq_len(dim(E1)[3])) {
-        if (any(B[iii,, jjj] !=0)) {
+for (iii in seq_len(dim(E1)[1])) {
+    for (jjj in seq_len(dim(E1)[3])) {
+        if (any(B[iii,, jjj] !=0)) {
             kkk <- kkk + 1
             E1[iii,, jjj, kkk] <- B[iii,, jjj]
         }
     }
 }
-if (kkk !=dim4) stop("Something went wrong in the loop, because 'kkk !=dim4'.")
+if (kkk !=dim4) stop("Something went wrong in the loop, because 'kkk !=dim4'.")
 
-mod2 <- TAM::tam.mml.3pl(resp, E=E1, est.some.slopes=FALSE, control=list(maxiter=50))
-summary(mod2)
+mod2 <- TAM::tam.mml.3pl(resp, E=E1, est.some.slopes=FALSE, control=list(maxiter=50))
+summary(mod2)
 
-cor(mod1$person$EAP.Dim3, mod2$person$EAP.Dim3)
-cor(mod1$person$EAP.Dim2, mod2$person$EAP.Dim2)
-cor(mod1$person$EAP.Dim1, mod2$person$EAP.Dim1)
-cor(mod1$xsi$xsi, mod2$xsi$xsi)
+cor(mod1$person$EAP.Dim3, mod2$person$EAP.Dim3)
+cor(mod1$person$EAP.Dim2, mod2$person$EAP.Dim2)
+cor(mod1$person$EAP.Dim1, mod2$person$EAP.Dim1)
+cor(mod1$xsi$xsi, mod2$xsi$xsi)
 
 #############################################################################
 # EXAMPLE 21: Acceleration of EM algorithm | dichotomous data
@@ -2060,34 +2060,34 @@ 
                                                                      Examp
 
 N <- 1000      # number of persons
 I <- 100       # number of items
-set.seed(987)
+set.seed(987)
 # simulate data according to the Rasch model
-dat <- sirt::sim.raschtype( stats::rnorm(N), b=seq(-2,2,len=I)  )
+dat <- sirt::sim.raschtype( stats::rnorm(N), b=seq(-2,2,len=I)  )
 # estimate models
-mod1n <- TAM::tam.mml( resp=dat, control=list( acceleration="none") )  # no acceler.
-mod1y <- TAM::tam.mml( resp=dat, control=list( acceleration="Yu") )  # Yu acceler.
-mod1r <- TAM::tam.mml( resp=dat, control=list( acceleration="Ramsay") )  # Ramsay acceler.
+mod1n <- TAM::tam.mml( resp=dat, control=list( acceleration="none") )  # no acceler.
+mod1y <- TAM::tam.mml( resp=dat, control=list( acceleration="Yu") )  # Yu acceler.
+mod1r <- TAM::tam.mml( resp=dat, control=list( acceleration="Ramsay") )  # Ramsay acceler.
 # compare number of iterations
 mod1n$iter ; mod1y$iter ; mod1r$iter
 # log-likelihood values
-logLik(mod1n); logLik(mod1y) ; logLik(mod1r)
+logLik(mod1n); logLik(mod1y) ; logLik(mod1r)
 
 #############################################################################
 # EXAMPLE 22: Acceleration of EM algorithm | polytomous data
 #############################################################################
 
-data(data.gpcm)
+data(data.gpcm)
 dat <- data.gpcm
 
 # no acceleration
-mod1n <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM",
-                control=list( conv=1E-4, acceleration="none") )
+mod1n <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM",
+                control=list( conv=1E-4, acceleration="none") )
 # Yu acceleration
-mod1y <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM",
-                control=list( conv=1E-4, acceleration="Yu") )
+mod1y <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM",
+                control=list( conv=1E-4, acceleration="Yu") )
 # Ramsay acceleration
-mod1r <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM",
-                control=list( conv=1E-4, acceleration="Ramsay") )
+mod1r <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM",
+                control=list( conv=1E-4, acceleration="Ramsay") )
 # number of iterations
 mod1n$iter ; mod1y$iter ; mod1r$iter
 
@@ -2096,8 +2096,8 @@ 
                                                                      Examp
 #             dimensions are defined by categories
 #############################################################################
 
-data(data.Students, package="CDM")
-dat <- data.Students[, grep( "act", colnames(data.Students) ) ]
+data(data.Students, package="CDM")
+dat <- data.Students[, grep( "act", colnames(data.Students) ) ]
 
 # define multidimensional model in which categories of item define dimensions
 
@@ -2106,55 +2106,55 @@ 
                                                                      Examp
 # * Category 2 -> loading of one on Dimension 2
 
 # extract default design matrices
-res <- TAM::designMatrices( resp=dat )
+res <- TAM::designMatrices( resp=dat )
 A <- res$A
 B0 <- 0*res$B
 # create design matrix B
-B <- array( 0, dim=c( dim(B0)[c(1,2) ], 2  ) )
-dimnames(B)[[1]] <- dimnames(B0)[[1]]
-dimnames(B)[[2]] <- dimnames(B0)[[2]]
-dimnames(B)[[3]] <- c( "Dim0", "Dim2" )
+B <- array( 0, dim=c( dim(B0)[c(1,2) ], 2  ) )
+dimnames(B)[[1]] <- dimnames(B0)[[1]]
+dimnames(B)[[2]] <- dimnames(B0)[[2]]
+dimnames(B)[[3]] <- c( "Dim0", "Dim2" )
 B[,1,1]  <- 1
 B[,3,2]  <- 1
 
 # estimate multdimensional Rasch model
-mod1 <- TAM::tam.mml( resp=dat, A=A, B=B, control=list( maxiter=100) )
-summary(mod1)
+mod1 <- TAM::tam.mml( resp=dat, A=A, B=B, control=list( maxiter=100) )
+summary(mod1)
 
 # alternative definition of B
 # * Category 1: negative loading on Dimension 1 and Dimension 2
-B <- array( 0, dim=c( dim(B0)[c(1,2) ], 2  ) )
-dimnames(B)[[1]] <- dimnames(B0)[[1]]
-dimnames(B)[[2]] <- dimnames(B0)[[2]]
-dimnames(B)[[3]] <- c( "Dim0", "Dim2" )
+B <- array( 0, dim=c( dim(B0)[c(1,2) ], 2  ) )
+dimnames(B)[[1]] <- dimnames(B0)[[1]]
+dimnames(B)[[2]] <- dimnames(B0)[[2]]
+dimnames(B)[[3]] <- c( "Dim0", "Dim2" )
 B[,1, 1]  <- 1
 B[,3, 2]  <- 1
-B[,2, c(1,2)]  <- -1
+B[,2, c(1,2)]  <- -1
 
 # estimate model
-mod2 <- TAM::tam.mml( resp=dat, A=A, B=B, control=list( maxiter=100) )
-summary(mod2)
+mod2 <- TAM::tam.mml( resp=dat, A=A, B=B, control=list( maxiter=100) )
+summary(mod2)
 
 #############################################################################
 # EXAMPLE 24: Sum constraint on item-category parameters in partial credit model
 #############################################################################
 
-data(data.gpcm,package="TAM")
+data(data.gpcm,package="TAM")
 dat <- data.gpcm
 
 # check number of categories
-c1 <- TAM::tam.ctt3(dat)
+c1 <- TAM::tam.ctt3(dat)
 
 #--- fit with PCM
-mod1 <- TAM::tam.mml( dat )
-summary(mod1, file="mod1")
+mod1 <- TAM::tam.mml( dat )
+summary(mod1, file="mod1")
 
 #--- fit with constraint on sum of categories
 #** redefine design matrix
 A1 <- 0*mod1$A
-A1 <- A1[,, - dim(A1)[[3]]]
-str(A1)
-NP <- dim(A1)[[3]]
+A1 <- A1[,, - dim(A1)[[3]]]
+str(A1)
+NP <- dim(A1)[[3]]
 # define item category parameters
 A1[1,2,1] <- A1[1,3,2] <- A1[1,4,3] <- -1
 A1[2,2,4] <- A1[2,3,5] <- A1[2,4,6] <- -1
@@ -2166,8 +2166,8 @@ 
                                                                      Examp
 A1[3,,]
 
 #** estimate model
-mod2 <- TAM::tam.mml( dat, A=A1, beta.fixed=FALSE)
-summary(mod2, file="mod2")
+mod2 <- TAM::tam.mml( dat, A=A1, beta.fixed=FALSE)
+summary(mod2, file="mod2")
 
 #--- compare model fit
 IRT.compareModels(mod1, mod2 )  # -> equivalent model fit
@@ -2176,26 +2176,26 @@ 
                                                                      Examp
 # EXAMPLE 25: Different GPCM parametrizations in IRT packages
 #############################################################################
 
-library(TAM)
-library(mirt)
-library(ltm)
+library(TAM)
+library(mirt)
+library(ltm)
 
-data(data.gpcm, package="TAM")
+data(data.gpcm, package="TAM")
 dat <- data.gpcm
 
 #*** TAM
-mod1 <- TAM::tam.mml.2pl(dat, irtmodel="GPCM")
+mod1 <- TAM::tam.mml.2pl(dat, irtmodel="GPCM")
 #*** mirt
-mod2 <- mirt::mirt(dat, 1, itemtype="gpcm", verbose=TRUE)
+mod2 <- mirt::mirt(dat, 1, itemtype="gpcm", verbose=TRUE)
 #*** ltm
-mod3 <- ltm::gpcm( dat, control=list(verbose=TRUE) )
-mod3b <- ltm::gpcm( dat, control=list(verbose=TRUE), IRT.param=FALSE)
+mod3 <- ltm::gpcm( dat, control=list(verbose=TRUE) )
+mod3b <- ltm::gpcm( dat, control=list(verbose=TRUE), IRT.param=FALSE)
 
 #-- comparison log likelihood
-logLik(mod1)
-logLik(mod2)
-logLik(mod3)
-logLik(mod3b)
+logLik(mod1)
+logLik(mod2)
+logLik(mod3)
+logLik(mod3b)
 
 #*** intercept parametrization (like in TAM)
 
@@ -2203,83 +2203,83 @@ 
                                                                      Examp
 mod1$B[,2,1]   # slope
 mod1$AXsi      # intercepts
 # mirt
-coef(mod2)
+coef(mod2)
 # ltm
-coef(mod3b, IRT.param=FALSE)[, c(4,1:3)]
+coef(mod3b, IRT.param=FALSE)[, c(4,1:3)]
 
 #*** IRT parametrization
 
 # TAM
 mod1$AXsi / mod1$B[,2,1]
 # mirt
-coef(mod2, IRTpars=TRUE)
+coef(mod2, IRTpars=TRUE)
 # ltm
-coef(mod3)[, c(4,1:3)]
+coef(mod3)[, c(4,1:3)]
 
 #############################################################################
 # EXAMPLE 26: Differential item functioning in multdimensional models
 #############################################################################
 
-data(data.ex08, package="TAM")
+data(data.ex08, package="TAM")
 formulaA <- ~ item+female+item*female
 resp <- data.ex08[["resp"]]
-facets <- as.data.frame(data.ex08[["facets"]])
+facets <- as.data.frame(data.ex08[["facets"]])
 
 #***  Model 8a: investigate gender DIF in undimensional model
-mod8a <- TAM::tam.mml.mfr(resp=resp, facets=facets, formulaA=formulaA)
-summary(mod8a)
+mod8a <- TAM::tam.mml.mfr(resp=resp, facets=facets, formulaA=formulaA)
+summary(mod8a)
 
 #*** multidimensional 2PL Model
 I <- 10
-Q <- array(0, dim=c(I, 3))
-Q[cbind(1:I, c(rep(1, 3), rep(2, 3), rep(3, 4)))] <- 1
-rownames(Q) <- colnames(resp)
-mod3dim2pl <- TAM::tam.mml.2pl(resp=resp, Q=Q, irtmodel="2PL",
-                          control=list(snodes=2000))
+Q <- array(0, dim=c(I, 3))
+Q[cbind(1:I, c(rep(1, 3), rep(2, 3), rep(3, 4)))] <- 1
+rownames(Q) <- colnames(resp)
+mod3dim2pl <- TAM::tam.mml.2pl(resp=resp, Q=Q, irtmodel="2PL",
+                          control=list(snodes=2000))
 
 #*** Combine both approaches
-thisRows <- gsub("-.*", "", colnames(mod8a$resp)) #select item names
+thisRows <- gsub("-.*", "", colnames(mod8a$resp)) #select item names
 
 #*** uniform DIF (note irtmodel="2PL.groups" & est.slopegroups)
-mod3dim2pl_udiff <- TAM::tam.mml.2pl(resp=mod8a$resp, A=mod8a$A, Q=Q[thisRows, ],
+mod3dim2pl_udiff <- TAM::tam.mml.2pl(resp=mod8a$resp, A=mod8a$A, Q=Q[thisRows, ],
                                irtmodel="2PL.groups",
-                               est.slopegroups=as.numeric(as.factor(thisRows)),
-                               control=list(snodes=2000))
+                               est.slopegroups=as.numeric(as.factor(thisRows)),
+                               control=list(snodes=2000))
 
 #*** non-uniform DIF (?); different slope parameters for each item administered to each group
-mod3dim2pl_nudiff <- TAM::tam.mml.2pl(resp=mod8a$resp, A=mod8a$A, Q=Q[thisRows, ],
-                                irtmodel="2PL", control=list(snodes=2000))
+mod3dim2pl_nudiff <- TAM::tam.mml.2pl(resp=mod8a$resp, A=mod8a$A, Q=Q[thisRows, ],
+                                irtmodel="2PL", control=list(snodes=2000))
 
 #*** check results
-print(mod8a$xsi)
-print(mod3dim2pl_udiff$xsi)
-summary(mod3dim2pl_nudiff)
+print(mod8a$xsi)
+print(mod3dim2pl_udiff$xsi)
+summary(mod3dim2pl_nudiff)
 
 #*** within item dimensionality (one item loads on two dimensions)
 Q2 <- Q
 Q2[4,1] <- 1
 
 # uniform DIF (note irtmodel="2PL.groups" & est.slopegroups)
-mod3dim2pl_udiff2 <- TAM::tam.mml.2pl(resp=mod8a$resp, A=mod8a$A, Q=Q2[thisRows, ],
+mod3dim2pl_udiff2 <- TAM::tam.mml.2pl(resp=mod8a$resp, A=mod8a$A, Q=Q2[thisRows, ],
                                 irtmodel="2PL.groups",
-                                est.slopegroups=as.numeric(as.factor(thisRows)),
-                                control=list(snodes=2000))
-print(mod8a$xsi)
-print(mod3dim2pl_udiff2$xsi)
-print(mod3dim2pl_udiff2$xsi)
+                                est.slopegroups=as.numeric(as.factor(thisRows)),
+                                control=list(snodes=2000))
+print(mod8a$xsi)
+print(mod3dim2pl_udiff2$xsi)
+print(mod3dim2pl_udiff2$xsi)
 
 #############################################################################
 # EXAMPLE 27: IRT parameterization for generalized partial credit model (GPCM) in TAM
 #############################################################################
 
 #--- read item parameters
-pars <- as.numeric(miceadds::scan.vec(
+pars <- as.numeric(miceadds::scan.vec(
 "0.19029 1.25309 0.51737 -1.77046 0.94803
   0.19407 1.22680 0.34986 -1.57666 1.29726
   -0.00888 1.07093 0.31662 -1.38755 1.14809
   -0.33810 1.08205 0.48490 -1.56696 0.79547
   -0.18866 0.99587 0.37880 -1.37468 0.81114" ))
-pars <- matrix( pars, nrow=5, byrow=TRUE)
+pars <- matrix( pars, nrow=5, byrow=TRUE)
 beta <- pars[,1]
 alpha <- pars[,5]
 tau <- pars[,2:4]
@@ -2287,84 +2287,82 @@ 
                                                                      Examp
 #--- data simulation function for GPCM
 sim_gpcm_irt_param <- function(alpha, beta, tau, N, mu=0, sigma=1)
 {
-    theta <- stats::rnorm(N, mean=mu, sd=sigma)
-    I <- length(beta)
-    K <- ncol(tau)
-    dat <- matrix(0, nrow=N, ncol=I)
-    colnames(dat) <- paste0("I",1:I)
+    theta <- stats::rnorm(N, mean=mu, sd=sigma)
+    I <- length(beta)
+    K <- ncol(tau)
+    dat <- matrix(0, nrow=N, ncol=I)
+    colnames(dat) <- paste0("I",1:I)
     for (ii in 1:I){
-        probs <- matrix(0, nrow=N, ncol=K+1)
+        probs <- matrix(0, nrow=N, ncol=K+1)
         for (kk in 1:K){
             probs[,kk+1] <- probs[,kk] + alpha[ii]*( theta - beta[ii] - tau[ii,kk] )
         }
-        probs <- exp(probs)
-        probs <- probs/rowSums(probs)
-        rn <- stats::runif(N)
-        cumprobs <- t(apply(probs,1,cumsum))
+        probs <- exp(probs)
+        probs <- probs/rowSums(probs)
+        rn <- stats::runif(N)
+        cumprobs <- t(apply(probs,1,cumsum))
         for (kk in 1:K){
             dat[,ii] <- dat[,ii] + ( rn > cumprobs[,kk] )
         }
     }
-    return(dat)
+    return(dat)
 }
 
 #-- simulate data
 N <- 20000     # number of persons
-set.seed(98)
+set.seed(98)
 dat1 <- sim_gpcm_irt_param(alpha=alpha, beta=beta, tau=tau, N=N, mu=0, sigma=1)
-head(dat1)
+head(dat1)
 
 #* generate design matrix for IRT parameterization
-A1 <- TAM::.A.PCM2( resp=dat1)
+A1 <- TAM::.A.PCM2( resp=dat1)
 
 #- estimate GPCM model
-mod1 <- TAM::tam.mml.2pl( resp=dat1, irtmodel="GPCM", A=A1)
-summary(mod1)
+mod1 <- TAM::tam.mml.2pl( resp=dat1, irtmodel="GPCM", A=A1)
+summary(mod1)
 
 # compare true and estimated slope estimates (alpha)
-cbind( alpha, mod1$B[,2,] )
+cbind( alpha, mod1$B[,2,] )
 
 # compare true and estimated item difficulties (beta)
-cbind( beta, mod1$xsi$xsi[1:5] / mod1$B[,2,1] )
+cbind( beta, mod1$xsi$xsi[1:5] / mod1$B[,2,1] )
 
 # compare true and estimated tau parameters
-cbind( tau[,-3], matrix( mod1$xsi$xsi[-c(1:5)], nrow=5, byrow=TRUE ) / mod1$B[,2,1] )
-# }
                                                                      
+cbind( tau[,-3], matrix( mod1$xsi$xsi[-c(1:5)], nrow=5, byrow=TRUE ) / mod1$B[,2,1] )
+}
                                                                      
   
   
 
 
+
       
                                                                      
       
                                                                      
   
                                                                      Developed by Alexander Robitzsch.
                                                                      
 
                                                                      
 
 
                                                                      
-  
                                                                      Site built with pkgdown 1.3.0.
                                                                      
+  
                                                                      Site built with pkgdown 1.4.1.
                                                                      
 
                                                                      
+
       
                                                                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.modelfit.html b/docs/reference/tam.modelfit.html
index c466766..8fa4076 100644
--- a/docs/reference/tam.modelfit.html
+++ b/docs/reference/tam.modelfit.html
@@ -8,21 +8,25 @@
 
 Model Fit Statistics in <span class="pkg">TAM</span> — tam.modelfit • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM — tam.modelfit" />
 
+TAM — tam.modelfit" />
 
 
@@ -59,6 +64,7 @@
 
 
 
+
   
 
   
@@ -83,7 +89,7 @@
       
-      
       
                                                                        
         
                                                                      • 
   
-    
+    
      
   
 
                                                                        • 
@@ -106,6 +111,7 @@
 
 
       
+
       
 
 
                                                                        
@@ -117,7 +123,6 @@ 
                                                                        Model Fit Statistics in TAM
                                                                        
     
                                                                        
 
     
                                                                        
-    
     
                                                                        The function tam.modelfit computes several model fit statistics.
 It includes the Q3 statistic (Yen, 1984) and an
 adjusted variant of it (see Details). Effect sizes of model fit
@@ -125,43 +130,42 @@ 
                                                                        Model Fit Statistics in TAM
                                                                        
 \(SRMR\)) are also available.
                                                                        
 
                                                                        The function IRT.modelfit is a wrapper to tam.modelfit,
 but allows convenient model comparisons by using the
-CDM::IRT.compareModels function.
                                                                        
+CDM::IRT.compareModels function.
                                                                        
 
                                                                        The tam.modelfit function can also be used for fitted
 models outside the TAM package by applying
 tam.modelfit.IRT or tam.modelfit.args.
                                                                        
 
                                                                        The function tam.Q3 computes the \(Q_3\) statistic based on
 weighted likelihood estimates (see tam.wle).
                                                                        
-    
     
                                                                        
 
-    
                                                                        tam.modelfit(tamobj, progress=TRUE)
+    
                                                                        tam.modelfit(tamobj, progress=TRUE)
 
-# S3 method for tam.modelfit
-summary(object,...)
+# S3 method for tam.modelfit
+summary(object,...)
 
-# S3 method for tam.mml
-IRT.modelfit(object, …)
-# S3 method for tam.mml.3pl
-IRT.modelfit(object, …)
-# S3 method for tamaan
-IRT.modelfit(object, …)
+# S3 method for tam.mml
+IRT.modelfit(object, ...)
+# S3 method for tam.mml.3pl
+IRT.modelfit(object, ...)
+# S3 method for tamaan
+IRT.modelfit(object, ...)
 
-# S3 method for IRT.modelfit.tam.mml
-summary(object, …)
-# S3 method for IRT.modelfit.tam.mml.3pl
-summary(object, …)
-# S3 method for IRT.modelfit.tamaan
-summary(object, …)
+# S3 method for IRT.modelfit.tam.mml
+summary(object, ...)
+# S3 method for IRT.modelfit.tam.mml.3pl
+summary(object, ...)
+# S3 method for IRT.modelfit.tamaan
+summary(object, ...)
 
-tam.modelfit.IRT( object, progress=TRUE )
+tam.modelfit.IRT( object, progress=TRUE )
 
-tam.modelfit.args( resp, probs, theta, post, progress=TRUE )
+tam.modelfit.args( resp, probs, theta, post, progress=TRUE )
 
-tam.Q3(tamobj, ... )
+tam.Q3(tamobj, ... )
+
+# S3 method for tam.Q3
+summary(object,...)
                                                                        
 
-# S3 method for tam.Q3
-summary(object,...)
                                                                        
-    
     
                                                                        Arguments
                                                                        
     
@@ -197,11 +201,11 @@ 
                                                                        Arg
       
                                                                        
-      
+      
                                                                        
     
                                                                        Individual posterior distribution
                                                                        		
     
                                                                        
     
                                                                        …...
       
                                                                        Further arguments to be passed
                                                                        		
     
                                                                        
     
                                                                        
-    
+
     
                                                                        Details
                                                                        
 
     
                                                                        For each item \(i\) and each person \(n\), residuals
@@ -239,7 +243,6 @@ 
                                                                        Details
 $$ MADRESIDCOV=\frac{1}{ J(J-1)/2 } \sum_{i < j}
     | c_{ij} - \hat{c}_{ij} |  $$
 This statistic is just multiplied by 100 in the output of this function.
                                                                        
-    
     
                                                                        Value
                                                                        
 
     
                                                                        A list with following entries
                                                                        
@@ -259,7 +262,6 @@ 
                                                                        Value
                                                                        
 
                                                                        Q3_summary
                                                                        Summary of \(Q_3\) statistics
                                                                        
 
                                                                        N_itempair
                                                                        Sample size for each item pair
                                                                        
 
-    
     
                                                                        References
                                                                        
 
     
                                                                        Chen, J., de la Torre, J., & Zhang, Z. (2013).
@@ -281,37 +283,35 @@ 
                                                                        R
 
                                                                        Yen, W. M. (1984). Effects of local item dependence on the fit and equating
 performance of the three-parameter logistic model.
 Applied Psychological Measurement, 8, 125-145.
                                                                        
-    
 
     
                                                                        Examples
                                                                        
-    
                                                                        # NOT RUN {
-#############################################################################
+    
                                                                        #############################################################################
 # EXAMPLE 1: data.cqc01
 #############################################################################
 
-data(data.cqc01)
+data(data.cqc01)
 dat <- data.cqc01
 
 #*****************************************************
 #*** Model 1: Rasch model
-mod1 <- TAM::tam.mml( dat )
+mod1 <- TAM::tam.mml( dat )
 # assess model fit
-res1 <- TAM::tam.modelfit( tamobj=mod1 )
-summary(res1)
+res1 <- TAM::tam.modelfit( tamobj=mod1 )
+summary(res1)
 # display item pairs with five largest adjusted Q3 statistics
-res1$stat.itempair[1:5,c("item1","item2","aQ3","p","p.holm")]
+res1$stat.itempair[1:5,c("item1","item2","aQ3","p","p.holm")]
 
-# }# NOT RUN {
+if (FALSE) {
 # IRT.modelfit
 fmod1 <- IRT.modelfit(mod1)
-summary(fmod1)
+summary(fmod1)
 
 #*****************************************************
 #*** Model 2: 2PL model
-mod2 <- TAM::tam.mml.2pl( dat )
+mod2 <- TAM::tam.mml.2pl( dat )
 # IRT.modelfit
 fmod2 <- IRT.modelfit(mod2)
-summary(fmod2)
+summary(fmod2)
 
 # model comparison
 IRT.compareModels(fmod1, fmod2 )
@@ -320,93 +320,93 @@ 
                                                                        Examp
 # SIMULATED EXAMPLE 2: Rasch model
 #############################################################################
 
-set.seed(8766)
+set.seed(8766)
 N <- 1000    # number of persons
 I <- 20      # number of items
 # simulate responses
-library(sirt)
-dat <- sirt::sim.raschtype( rnorm(N), b=seq(-1.5,1.5,len=I) )
+library(sirt)
+dat <- sirt::sim.raschtype( rnorm(N), b=seq(-1.5,1.5,len=I) )
 #*** estimation
-mod1 <- TAM::tam.mml( dat )
-summary(dat)
+mod1 <- TAM::tam.mml( dat )
+summary(dat)
 #*** model fit
-res1 <- TAM::tam.modelfit( tamobj=mod1)
-summary(res1)
+res1 <- TAM::tam.modelfit( tamobj=mod1)
+summary(res1)
 
 #############################################################################
 # EXAMPLE 3: Model fit data.gpcm | Partial credit model
 #############################################################################
 
-data(data.gpcm)
+data(data.gpcm)
 dat <- data.gpcm
 
 # estimate partial credit model
-mod1 <- TAM::tam.mml( dat)
-summary(mod1)
+mod1 <- TAM::tam.mml( dat)
+summary(mod1)
 
 # assess model fit
-tmod1 <- TAM::tam.modelfit( mod1 )
-summary(tmod1)
+tmod1 <- TAM::tam.modelfit( mod1 )
+summary(tmod1)
 
 #############################################################################
 # EXAMPLE 4: data.read | Comparison Q3 statistic
 #############################################################################
 
-library(sirt)
-data(data.read, package="sirt")
+library(sirt)
+data(data.read, package="sirt")
 dat <- data.read
 
 #**** Model 1: 1PL model
-mod1 <- TAM::tam.mml( dat )
-summary(mod1)
+mod1 <- TAM::tam.mml( dat )
+summary(mod1)
 
 #**** Model 2: 2PL model
-mod2 <- TAM::tam.mml.2pl( dat )
-summary(mod2)
+mod2 <- TAM::tam.mml.2pl( dat )
+summary(mod2)
 
 #**** assess model fits
 # Q3 based on posterior
-fmod1 <- TAM::tam.modelfit(mod1)
-fmod2 <- TAM::tam.modelfit(mod2)
+fmod1 <- TAM::tam.modelfit(mod1)
+fmod2 <- TAM::tam.modelfit(mod2)
 # Q3 based on WLEs
-q3_mod1 <- TAM::tam.Q3(mod1)
-q3_mod2 <- TAM::tam.Q3(mod2)
-summary(fmod1)
-summary(fmod2)
-summary(q3_mod1)
-summary(q3_mod2)
-# }
                                                                        
+q3_mod1 <- TAM::tam.Q3(mod1)
+q3_mod2 <- TAM::tam.Q3(mod2)
+summary(fmod1)
+summary(fmod2)
+summary(q3_mod1)
+summary(q3_mod2)
+}
                                                                        
   
   
 
 
+
       
                                                                        
       
                                                                        
   
                                                                        Developed by Alexander Robitzsch.
                                                                        
 
                                                                        
 
 
                                                                        
-  
                                                                        Site built with pkgdown 1.3.0.
                                                                        
+  
                                                                        Site built with pkgdown 1.4.1.
                                                                        
 
                                                                        
+
       
                                                                        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.np.html b/docs/reference/tam.np.html
index 59c7394..8e0c5ba 100644
--- a/docs/reference/tam.np.html
+++ b/docs/reference/tam.np.html
@@ -8,21 +8,25 @@
 
 Unidimensional Non- and Semiparametric Item Response Model — tam.np • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -54,6 +59,7 @@
 
 
 
+
   
 
   
@@ -78,7 +84,7 @@
       
-      
       
                                                                          
         
                                                                        • 
   
-    
+    
      
   
 
                                                                          • 
@@ -101,6 +106,7 @@
 
 
       
+
       
 
 
                                                                          
@@ -112,7 +118,6 @@ 
                                                                          Unidimensional Non- and Semiparametric Item Response Model
                                                                          
     
                                                                          
 
     
                                                                          
-    
     
                                                                          Conducts non- and semiparametric estimation of a unidimensional item response model
 for a single group allowing polytomous item responses (Rossi, Wang & Ramsay, 2002).
                                                                          
 
                                                                          For dichotomous data, the function also allows group lasso penalty
@@ -121,19 +126,18 @@ 
                                                                          Unidimensional Non- and Semiparametric Item Response Model
                                                                          
 which is applied to the nonlinear part of the basis expansion. This approach
 automatically detects deviations from a 2PL or a 1PL model (see Examples 2 and 3).
 See Details for model specification.
                                                                          
-    
     
                                                                          
 
-    
                                                                          tam.np( dat, probs_init=NULL, pweights=NULL, lambda=NULL, control=list(),
-    model="2PL", n_basis=0, basis_type="hermite", penalty_type="lasso",
-    pars_init=NULL, orthonormalize=TRUE)
+    
                                                                          tam.np( dat, probs_init=NULL, pweights=NULL, lambda=NULL, control=list(),
+    model="2PL", n_basis=0, basis_type="hermite", penalty_type="lasso",
+    pars_init=NULL, orthonormalize=TRUE)
 
-# S3 method for tam.np
-summary(object, file=NULL, …)
+# S3 method for tam.np
+summary(object, file=NULL, ...)
+
+# S3 method for tam.np
+IRT.cv(object, kfold=10, ...)
                                                                          
 
-# S3 method for tam.np
-IRT.cv(object, kfold=10, …)
                                                                          
-    
     
                                                                          Arguments
                                                                          
     
@@ -197,11 +201,11 @@ 
                                                                          Arg
       
                                                                          
-      
+      
                                                                          
     
                                                                          Number of folds in \(k\)-fold cross-validation
                                                                          		
     
                                                                          
     
                                                                          …...
       
                                                                          Further arguments to be passed
                                                                          		
     
                                                                          
     
                                                                          
-    
+
     
                                                                          Details
                                                                          
 
     
                                                                          The basis expansion approach is applied for the logit transformation of item
@@ -219,7 +223,6 @@ 
                                                                          Details
 penalty \(J_{i,L1}=N \lambda \sqrt{p} \sqrt{ \sum_{h=1}^p \beta_{ih}^2 }\) while for
 the ridge penalty it is \(J_{i,L2}=N \lambda \sum_{h=1}^p \beta_{ih}^2 \)
 (\(N\) denoting the sample size).
                                                                          
-    
     
                                                                          Value
                                                                          
 
     
                                                                          List containing several entries
                                                                          
@@ -232,9 +235,8 @@ 
                                                                          Value
                                                                          
 
                                                                          pars
                                                                          Estimated parameters
                                                                          
 
                                                                          regularized
                                                                          Logical indicating which items are regularized
                                                                          
 
                                                                          ic
                                                                          List containing
                                                                          
-
                                                                          …
                                                                          Further values
                                                                          
+
                                                                          ...
                                                                          Further values
                                                                          
 
-    
     
                                                                          References
                                                                          
 
     
                                                                          Breheny, P., & Huang, J. (2015). Group descent algorithms for nonconvex penalized linear
@@ -248,27 +250,25 @@ 
                                                                          R
 
                                                                          Yang, Y., & Zou, H. (2015). A fast unified algorithm for solving group-lasso penalized
 learning problems. Statistics and Computing, 25(6), 1129-1141.
 http://dx.doi.org/10.1007/s11222-014-9498-5
                                                                          
-    
     
                                                                          See also
                                                                          
 
     
                                                                          Nonparametric item response models can also be estimated with the
-mirt::itemGAM function in the mirt package and the
+mirt::itemGAM function in the mirt package and the
 KernSmoothIRT::ksIRT in the KernSmoothIRT package.
                                                                          
 
                                                                          See tam.mml and tam.mml.2pl for parametric item response
 models.
                                                                          
-    
 
     
                                                                          Examples
                                                                          
-    
                                                                          # NOT RUN {
+    
                                                                          if (FALSE) {
 #############################################################################
 # EXAMPLE 1: Nonparametric estimation polytomous data
 #############################################################################
 
-data(data.cqc02, package="TAM")
+data(data.cqc02, package="TAM")
 dat <- data.cqc02
 
 #** nonparametric estimation
-mod <- TAM::tam.np(dat)
+mod <- TAM::tam.np(dat)
 
 #** extractor functions for objects of class 'tam.np'
 lmod <- IRT.likelihood(mod)
@@ -281,16 +281,16 @@ 
                                                                          Examp
 #############################################################################
 
 #- simulate data with two misfitting items
-set.seed(998)
+set.seed(998)
 I <- 10
 N <- 1000
-a <- stats::rnorm(I, mean=1, sd=.3)
-b <- stats::rnorm(I, mean=0, sd=1)
-dat <- matrix(NA, nrow=N, ncol=I)
-colnames(dat) <- paste0("I",1:I)
-theta <- stats::rnorm(N)
+a <- stats::rnorm(I, mean=1, sd=.3)
+b <- stats::rnorm(I, mean=0, sd=1)
+dat <- matrix(NA, nrow=N, ncol=I)
+colnames(dat) <- paste0("I",1:I)
+theta <- stats::rnorm(N)
 for (ii in 1:I){
-    dat[,ii] <- 1*(stats::runif(N) < stats::plogis( a[ii]*(theta-b[ii] ) ))
+    dat[,ii] <- 1*(stats::runif(N) < stats::plogis( a[ii]*(theta-b[ii] ) ))
 }
 
 #* first misfitting item with lower and upper asymptote
@@ -298,115 +298,114 @@ 
                                                                          Examp
 l <- .3
 u <- 1
 b[ii] <- 1.5
-dat[,ii] <- 1*(stats::runif(N) < l + (u-l)*stats::plogis( a[ii]*(theta-b[ii] ) ))
+dat[,ii] <- 1*(stats::runif(N) < l + (u-l)*stats::plogis( a[ii]*(theta-b[ii] ) ))
 
 #* second misfitting item with non-monotonic item response function
 ii <- 3
-dat[,ii] <- (stats::runif(N) < stats::plogis( theta-b[ii]+.6*theta^2))
+dat[,ii] <- (stats::runif(N) < stats::plogis( theta-b[ii]+.6*theta^2))
 
 #- 2PL model
-mod0 <- TAM::tam.mml.2pl(dat)
+mod0 <- TAM::tam.mml.2pl(dat)
 
 #- lasso penalty with lambda of .05
-mod1 <- TAM::tam.np(dat, n_basis=4, lambda=.05)
+mod1 <- TAM::tam.np(dat, n_basis=4, lambda=.05)
 
 #- lambda value of .03 using starting value of previous model
-mod2 <- TAM::tam.np(dat, n_basis=4, lambda=.03, pars_init=mod1$pars)
-cmod2 <- TAM::IRT.cv(mod2)  # cross-validated deviance
+mod2 <- TAM::tam.np(dat, n_basis=4, lambda=.03, pars_init=mod1$pars)
+cmod2 <- TAM::IRT.cv(mod2)  # cross-validated deviance
 
 #- lambda=.015
-mod3 <- TAM::tam.np(dat, n_basis=4, lambda=.015, pars_init=mod2$pars)
-cmod3 <- TAM::IRT.cv(mod3)
+mod3 <- TAM::tam.np(dat, n_basis=4, lambda=.015, pars_init=mod2$pars)
+cmod3 <- TAM::IRT.cv(mod3)
 
 #- lambda=.007
-mod4 <- TAM::tam.np(dat, n_basis=4, lambda=.007, pars_init=mod3$pars)
+mod4 <- TAM::tam.np(dat, n_basis=4, lambda=.007, pars_init=mod3$pars)
 
 #- lambda=.001
-mod5 <- TAM::tam.np(dat, n_basis=4, lambda=.001, pars_init=mod4$pars)
+mod5 <- TAM::tam.np(dat, n_basis=4, lambda=.001, pars_init=mod4$pars)
 
 #- final estimation using solution of mod3
 eps <- .0001
 lambda_final <- eps+(1-eps)*mod3$regularized   # lambda parameter for final estimate
-mod3b <- TAM::tam.np(dat, n_basis=4, lambda=lambda_final, pars_init=mod3$pars)
-summary(mod1)
-summary(mod2)
-summary(mod3)
-summary(mod3b)
-summary(mod4)
+mod3b <- TAM::tam.np(dat, n_basis=4, lambda=lambda_final, pars_init=mod3$pars)
+summary(mod1)
+summary(mod2)
+summary(mod3)
+summary(mod3b)
+summary(mod4)
 
 # compare models with respect to information criteria
 IRT.compareModels(mod0, mod1, mod2, mod3, mod3b, mod4, mod5)
 
 #-- compute item fit statistics RISE
 # regularized solution
-TAM::IRT.RISE(mod_p=mod1, mod_np=mod3)
+TAM::IRT.RISE(mod_p=mod1, mod_np=mod3)
 # regularized solution, final estimation
-TAM::IRT.RISE(mod_p=mod1, mod_np=mod3b, use_probs=TRUE)
-TAM::IRT.RISE(mod_p=mod1, mod_np=mod3b, use_probs=FALSE)
+TAM::IRT.RISE(mod_p=mod1, mod_np=mod3b, use_probs=TRUE)
+TAM::IRT.RISE(mod_p=mod1, mod_np=mod3b, use_probs=FALSE)
 # use TAM::IRT.RISE() function for computing the RMSD statistic
-TAM::IRT.RISE(mod_p=mod1, mod_np=mod1, use_probs=FALSE)
+TAM::IRT.RISE(mod_p=mod1, mod_np=mod1, use_probs=FALSE)
 
 #############################################################################
 # EXAMPLE 3: Mixed 1PL/2PL model
 #############################################################################
 
 #* simulate data with 2 2PL items and 8 1PL items
-set.seed(9877)
+set.seed(9877)
 N <- 2000
 I <- 10
-b <- seq(-1,1,len=I)
-a <- rep(1,I)
-a[c(3,8)] <- c(.5, 2)
-theta <- stats::rnorm(N, sd=1)
-dat <- sirt::sim.raschtype(theta, b=b, fixed.a=a)
+b <- seq(-1,1,len=I)
+a <- rep(1,I)
+a[c(3,8)] <- c(.5, 2)
+theta <- stats::rnorm(N, sd=1)
+dat <- sirt::sim.raschtype(theta, b=b, fixed.a=a)
 
 #- 1PL model
-mod1 <- TAM::tam.mml(dat)
+mod1 <- TAM::tam.mml(dat)
 #- 2PL model
-mod2 <- TAM::tam.mml.2pl(dat)
+mod2 <- TAM::tam.mml.2pl(dat)
 #- 2PL model with penalty on slopes
-mod3 <- TAM::tam.np(dat, lambda=.04, model="1PL", n_basis=0)
-summary(mod3)
+mod3 <- TAM::tam.np(dat, lambda=.04, model="1PL", n_basis=0)
+summary(mod3)
 #- final mixed 1PL/2PL model
 lambda <- 1*mod3$regularized
-mod4 <- TAM::tam.np(dat, lambda=lambda, model="1PL", n_basis=0)
-summary(mod4)
+mod4 <- TAM::tam.np(dat, lambda=lambda, model="1PL", n_basis=0)
+summary(mod4)
 
 IRT.compareModels(mod1, mod2, mod3, mod4)
-# }
                                                                          
+}
                                                                          
   
   
 
 
+
       
                                                                          
       
                                                                          
   
                                                                          Developed by Alexander Robitzsch.
                                                                          
 
                                                                          
 
 
                                                                          
-  
                                                                          Site built with pkgdown 1.3.0.
                                                                          
+  
                                                                          Site built with pkgdown 1.4.1.
                                                                          
 
                                                                          
+
       
                                                                          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.personfit.html b/docs/reference/tam.personfit.html
index 2ad0f20..2dde2a9 100644
--- a/docs/reference/tam.personfit.html
+++ b/docs/reference/tam.personfit.html
@@ -8,21 +8,25 @@
 
 Person Outfit and Infit Statistics — tam.personfit • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,13 +34,14 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -47,6 +52,7 @@
 
 
 
+
   
 
   
@@ -71,7 +77,7 @@
       
-      
       
                                                                            
         
                                                                          • 
   
-    
+    
      
   
 
                                                                            • 
@@ -94,6 +99,7 @@
 
 
       
+
       
 
 
                                                                            
@@ -105,13 +111,11 @@ 
                                                                            Person Outfit and Infit Statistics
                                                                            
     
                                                                            
 
     
                                                                            
-    
     
                                                                            Computes person outfit and infit statistics.
                                                                            
-    
     
                                                                            
 
     
                                                                            tam.personfit(tamobj)
                                                                            
-    
+
     
                                                                            Arguments
                                                                            
     
@@ -120,63 +124,60 @@ 
                                                                            Arg
       
                                                                            
     
                                                                            Fitted object in TAM
                                                                            		
     
                                                                            
     
                                                                            
-    
+
     
                                                                            Value
                                                                            
 
     
                                                                            Data frame containing person outfit and infit statistics
                                                                            
-    
     
                                                                            See also
                                                                            
 
     
                                                                            See tam.fit and msq.itemfit for item fit
 statistics.
                                                                            
-    
 
     
                                                                            Examples
                                                                            
-    
                                                                            # NOT RUN {
-#############################################################################
+    
                                                                            #############################################################################
 # EXAMPLE 1: Person fit dichotomous data
 #############################################################################
 
-data(data.sim.rasch, package="TAM")
+data(data.sim.rasch, package="TAM")
 resp <- data.sim.rasch
 
 #*** estimate Rasch model
-mod1 <- TAM::tam.mml(resp=resp)
-summary(mod1)
+mod1 <- TAM::tam.mml(resp=resp)
+summary(mod1)
 
 #*** compute person fit statistics
-fmod1 <- TAM::tam.personfit(mod1)
-head(fmod1)
-# }
                                                                            
+fmod1 <- TAM::tam.personfit(mod1)
+head(fmod1)
                                                                            
   
   
 
 
+
       
                                                                            
       
                                                                            
   
                                                                            Developed by Alexander Robitzsch.
                                                                            
 
                                                                            
 
 
                                                                            
-  
                                                                            Site built with pkgdown 1.3.0.
                                                                            
+  
                                                                            Site built with pkgdown 1.4.1.
                                                                            
 
                                                                            
+
       
                                                                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.pv.html b/docs/reference/tam.pv.html
index 7b67241..ce7dcc7 100644
--- a/docs/reference/tam.pv.html
+++ b/docs/reference/tam.pv.html
@@ -8,21 +8,25 @@
 
 Plausible Value Imputation — tam.pv • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -59,6 +64,7 @@
 
 
 
+
   
 
   
@@ -83,7 +89,7 @@
       
-      
       
                                                                              
         
                                                                            • 
   
-    
+    
      
   
 
                                                                              • 
@@ -106,6 +111,7 @@
 
 
       
+
       
 
 
                                                                              
@@ -117,7 +123,6 @@ 
                                                                              Plausible Value Imputation
                                                                              
     
                                                                              
 
     
                                                                              
-    
     
                                                                              Plausible value imputation for objects of the classes tam and tam.mml
 (Adams & Wu, 2007). For converting generated plausible values into
 a list of multiply imputed datasets see tampv2datalist
@@ -131,23 +136,22 @@ 
                                                                              Plausible Value Imputation
                                                                              
 argument sample_integers=FALSE) or ordinary
 bootstrap (i.e., sampling cases with replacement; argument sample_integers=TRUE)
 can be used for the Bootstrap step by obtaining posterior draws of regression parameters.
                                                                              
-    
     
                                                                              
 
-    
                                                                              tam.pv(tamobj, nplausible=10, ntheta=2000, normal.approx=FALSE,
-    samp.regr=FALSE, theta.model=FALSE, np.adj=8, na.grid=5, verbose=TRUE)
+    
                                                                              tam.pv(tamobj, nplausible=10, ntheta=2000, normal.approx=FALSE,
+    samp.regr=FALSE, theta.model=FALSE, np.adj=8, na.grid=5, verbose=TRUE)
 
-tam.pv.mcmc( tamobj, Y=NULL, group=NULL, beta_groups=TRUE, nplausible=10, level=.95,
-    n.iter=1000, n.burnin=500, adj_MH=.5, adj_change_MH=.05, refresh_MH=50,
-    accrate_bound_MH=c(.45, .55), sample_integers=FALSE, theta_init=NULL,
-    print_iter=20, verbose=TRUE, calc_ic=TRUE)
+tam.pv.mcmc( tamobj, Y=NULL, group=NULL, beta_groups=TRUE, nplausible=10, level=.95,
+    n.iter=1000, n.burnin=500, adj_MH=.5, adj_change_MH=.05, refresh_MH=50,
+    accrate_bound_MH=c(.45, .55), sample_integers=FALSE, theta_init=NULL,
+    print_iter=20, verbose=TRUE, calc_ic=TRUE)
 
-# S3 method for tam.pv.mcmc
-summary(object, file=NULL, …)
+# S3 method for tam.pv.mcmc
+summary(object, file=NULL, ...)
+
+# S3 method for tam.pv.mcmc
+plot(x, ...)
                                                                              
 
-# S3 method for tam.pv.mcmc
-plot(x, …)
                                                                              
-    
     
                                                                              Arguments
                                                                              
     
@@ -207,7 +211,7 @@ 
                                                                              Arg
 -5 to 5.
                                                                              
     
     
                                                                              
-      
+      
@@ -289,7 +293,7 @@ 
                                                                              Arg
       
                                                                              
     
                                                                              …...
       
                                                                              Further arguments to be passed
                                                                              		
     
                                                                              
     
                                                                              A file name in which the summary output will be written
                                                                              		
     
                                                                              
     
                                                                              
-    
+
     
                                                                              Value
                                                                              
 
     
                                                                              The value of tam.pv is a list with following entries:
                                                                              
@@ -313,9 +317,8 @@ 
                                                                              Value
                                                                              
 
                                                                              theta_acceptance_MH
                                                                              Acceptance rates and acceptance MH factors for each
 individual
                                                                              
 
                                                                              theta_last
                                                                              Last sampled \(\bold{\theta}\) value
                                                                              
-
                                                                              …
                                                                              Further values
                                                                              
+
                                                                              ...
                                                                              Further values
                                                                              
 
-    
     
                                                                              References
                                                                              
 
     
                                                                              Adams, R. J., & Wu, M. L. (2007). The mixed-coefficients multinomial logit model.
@@ -324,53 +327,50 @@ 
                                                                              R
 (pp. 55-76). New York: Springer.
                                                                              
 
                                                                              Rubin, D. B. (1981). The Bayesian bootstrap. The Annals of Statistics, 9(1),
 130-134.
                                                                              
-    
     
                                                                              See also
                                                                              
 
     
                                                                              See tam.latreg for further examples of
 fitting latent regression models and drawing plausible values
 from models which provides an individual likelihood as an input.
                                                                              
-    
 
     
                                                                              Examples
                                                                              
-    
                                                                              # NOT RUN {
-#############################################################################
+    
                                                                              #############################################################################
 # EXAMPLE 1: Dichotomous unidimensional data sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 resp <- data.sim.rasch[ 1:500, 1:15 ]  # select subsample of students and items
 
 # estimate Rasch model
-mod <- TAM::tam.mml(resp)
+mod <- TAM::tam.mml(resp)
 
 # draw 5 plausible values without a normality
 # assumption of the posterior and 2000 ability nodes
-pv1a <- TAM::tam.pv( mod, nplausible=5, ntheta=2000 )
+pv1a <- TAM::tam.pv( mod, nplausible=5, ntheta=2000 )
 
 # draw 5 plausible values with a normality
 # assumption of the posterior and 500 ability nodes
-pv1b <- TAM::tam.pv( mod, nplausible=5, ntheta=500, normal.approx=TRUE )
+pv1b <- TAM::tam.pv( mod, nplausible=5, ntheta=500, normal.approx=TRUE )
 
 # distribution of first plausible value from imputation pv1
-hist(pv1a$pv$PV1.Dim1 )
+hist(pv1a$pv$PV1.Dim1 )
 # boxplot of all plausible values from imputation pv2
-boxplot(pv1b$pv[, 2:6 ] )
+boxplot(pv1b$pv[, 2:6 ] )
 
-# }# NOT RUN {
+if (FALSE) {
 # draw plausible values with tam.pv.mcmc function
-Y <- matrix(1, nrow=500, ncol=1)
-pv2 <- TAM::tam.pv.mcmc( tamobj=mod, Y=Y, nplausible=5 )
-str(pv2)
+Y <- matrix(1, nrow=500, ncol=1)
+pv2 <- TAM::tam.pv.mcmc( tamobj=mod, Y=Y, nplausible=5 )
+str(pv2)
 
 # summary output
-summary(pv2)
+summary(pv2)
 # assessing convergence with traceplots
-plot(pv2, ask=TRUE)
+plot(pv2, ask=TRUE)
 
 # use starting values for theta and MH factors which fulfill acceptance rates
 # from previously fitted model
-pv3 <- TAM::tam.pv.mcmc( tamobj=mod, Y=Y, nplausible=5, theta_init=pv2$theta_last,
+pv3 <- TAM::tam.pv.mcmc( tamobj=mod, Y=Y, nplausible=5, theta_init=pv2$theta_last,
             adj_MH=pv2$theta_acceptance_MH$adj_MH )
 
 #############################################################################
@@ -378,7 +378,7 @@ 
                                                                              Examp
 #            background variables; dataset data.pisaRead from sirt package
 #############################################################################
 
-data(data.pisaRead, package="sirt")
+data(data.pisaRead, package="sirt")
 dat <- data.pisaRead$data
   ##   > colnames(dat)
   ##    [1] "idstud"   "idschool" "female"   "hisei"    "migra"    "R432Q01"
@@ -388,71 +388,71 @@ 
                                                                              Examp
 ## Note that reading items have variable names starting with R4
 
 # estimate 2PL model without covariates
-items <- grep("R4", colnames(dat) )    # select test items from data
-mod2a <- TAM::tam.mml.2pl( resp=dat[,items] )
-summary(mod2a)
+items <- grep("R4", colnames(dat) )    # select test items from data
+mod2a <- TAM::tam.mml.2pl( resp=dat[,items] )
+summary(mod2a)
 
 # fix item parameters for plausible value imputation
    # fix item intercepts by defining xsi.fixed
 xsi0 <- mod2a$xsi$xsi
-xsi.fixed <- cbind( seq(1,length(xsi0)), xsi0 )
+xsi.fixed <- cbind( seq(1,length(xsi0)), xsi0 )
    # fix item slopes using mod2$B
 # matrix of latent regressors female, hisei and migra
-Y <- dat[, c("female", "hisei", "migra") ]
-mod2b <- TAM::tam.mml( resp=dat[,items], B=mod2a$B, xsi.fixed=xsi.fixed, Y=Y,
+Y <- dat[, c("female", "hisei", "migra") ]
+mod2b <- TAM::tam.mml( resp=dat[,items], B=mod2a$B, xsi.fixed=xsi.fixed, Y=Y,
             pid=dat$idstud)
 
 # plausible value imputation with normality assumption
 # and ignoring uncertainty about regression coefficients
 #    -> the default is samp.regr=FALSE
-pv2c <- TAM::tam.pv( mod2b, nplausible=10, ntheta=500, normal.approx=TRUE )
+pv2c <- TAM::tam.pv( mod2b, nplausible=10, ntheta=500, normal.approx=TRUE )
 # sampling of regression coefficients
-pv2d <- TAM::tam.pv( mod2b, nplausible=10, ntheta=500, samp.regr=TRUE)
+pv2d <- TAM::tam.pv( mod2b, nplausible=10, ntheta=500, samp.regr=TRUE)
 # sampling of regression coefficients, normal approximation using the
 # theta grid from the model
-pv2e <- TAM::tam.pv( mod2b, samp.regr=TRUE, theta.model=TRUE, normal.approx=TRUE)
+pv2e <- TAM::tam.pv( mod2b, samp.regr=TRUE, theta.model=TRUE, normal.approx=TRUE)
 
 #--- create list of multiply imputed datasets with plausible values
 # define dataset with covariates to be matched
-Y <- dat[, c("idstud", "idschool", "female", "hisei", "migra") ]
+Y <- dat[, c("idstud", "idschool", "female", "hisei", "migra") ]
 
 # define plausible value names
-pvnames <- c("PVREAD")
+pvnames <- c("PVREAD")
 # create list of imputed datasets
-datlist1 <- TAM::tampv2datalist( pv2e, pvnames=pvnames, Y=Y, Y.pid="idstud")
-str(datlist1)
+datlist1 <- TAM::tampv2datalist( pv2e, pvnames=pvnames, Y=Y, Y.pid="idstud")
+str(datlist1)
 
 # create a matrix of covariates with different set of students than in pv2e
-Y1 <- Y[ seq( 1, 600, 2 ), ]
+Y1 <- Y[ seq( 1, 600, 2 ), ]
 # create list of multiply imputed datasets
-datlist2 <- TAM::tampv2datalist( pv2e, pvnames=c("PVREAD"), Y=Y1, Y.pid="idstud")
+datlist2 <- TAM::tampv2datalist( pv2e, pvnames=c("PVREAD"), Y=Y1, Y.pid="idstud")
 
 #--- fit some models in lavaan and semTools
-library(lavaan)
-library(semTools)
+library(lavaan)
+library(semTools)
 
 #*** Model 1: Linear regression
 lavmodel <- "
    PVREAD ~ migra + hisei
    PVREAD ~~ PVREAD
         "
-mod1 <- semTools::lavaan.mi( lavmodel, data=datlist1, m=0)
-summary(mod1, standardized=TRUE, rsquare=TRUE)
+mod1 <- semTools::lavaan.mi( lavmodel, data=datlist1, m=0)
+summary(mod1, standardized=TRUE, rsquare=TRUE)
 
 # apply lavaan for third imputed dataset
-mod1a <- lavaan::lavaan( lavmodel, data=datlist1[[3]] )
-summary(mod1a, standardized=TRUE, rsquare=TRUE)
+mod1a <- lavaan::lavaan( lavmodel, data=datlist1[[3]] )
+summary(mod1a, standardized=TRUE, rsquare=TRUE)
 
 # compare with mod1 by looping over all datasets
-mod1b <- lapply( datlist1, FUN=function(dat0){
-    mod1a <- lavaan( lavmodel, data=dat0 )
-    coef( mod1a)
+mod1b <- lapply( datlist1, FUN=function(dat0){
+    mod1a <- lavaan( lavmodel, data=dat0 )
+    coef( mod1a)
         } )
 mod1b
-mod1b <- matrix( unlist( mod1b ), ncol=length( coef(mod1)), byrow=TRUE )
+mod1b <- matrix( unlist( mod1b ), ncol=length( coef(mod1)), byrow=TRUE )
 mod1b
-round( colMeans(mod1b), 3 )
-coef(mod1)   # -> results coincide
+round( colMeans(mod1b), 3 )
+coef(mod1)   # -> results coincide
 
 #*** Model 2: Path model
 lavmodel <- "
@@ -461,133 +461,133 @@ 
                                                                              Examp
    PVREAD ~~ PVREAD
    hisei ~~ hisei
         "
-mod2 <- semTools::lavaan.mi( lavmodel, data=datlist1 )
-summary(mod2, standardized=TRUE, rsquare=TRUE)
+mod2 <- semTools::lavaan.mi( lavmodel, data=datlist1 )
+summary(mod2, standardized=TRUE, rsquare=TRUE)
 # fit statistics
-inspect( mod2, what="fit")
+inspect( mod2, what="fit")
 
 #--- using mitools package
-library(mitools)
+library(mitools)
 # convert datalist into an object of class imputationList
-datlist1a <- mitools::imputationList( datlist1 )
+datlist1a <- mitools::imputationList( datlist1 )
 # fit linear regression
-mod1c <- with( datlist1a, stats::lm( PVREAD ~ migra + hisei ) )
-summary( mitools::MIcombine(mod1c) )
+mod1c <- with( datlist1a, stats::lm( PVREAD ~ migra + hisei ) )
+summary( mitools::MIcombine(mod1c) )
 
 #--- using mice package
-library(mice)
-library(miceadds)
+library(mice)
+library(miceadds)
 # convert datalist into a mids object
-mids1 <- miceadds::datalist2mids( datlist1 )
+mids1 <- miceadds::datalist2mids( datlist1 )
 # fit linear regression
-mod1c <- with( mids1, stats::lm( PVREAD ~ migra + hisei ) )
-summary( mice::pool(mod1c) )
+mod1c <- with( mids1, stats::lm( PVREAD ~ migra + hisei ) )
+summary( mice::pool(mod1c) )
 
 #############################################################################
 # EXAMPLE 3: Multidimensional plausible value imputation
 #############################################################################
 
 # (1) simulate some data
-set.seed(6778)
-library(mvtnorm)
+set.seed(6778)
+library(mvtnorm)
 N <- 1000
-Y <- cbind( stats::rnorm(N), stats::rnorm(N) )
-theta <- mvtnorm::rmvnorm( N, mean=c(0,0), sigma=matrix( c(1,.5,.5,1), 2, 2 ))
+Y <- cbind( stats::rnorm(N), stats::rnorm(N) )
+theta <- mvtnorm::rmvnorm( N, mean=c(0,0), sigma=matrix( c(1,.5,.5,1), 2, 2 ))
 theta[,1] <- theta[,1] + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
 theta[,2] <- theta[,2] + .8 * Y[,1] + .5 * Y[,2]  # latent regression model
 I <- 20
-p1 <- stats::plogis( outer( theta[,1], seq( -2, 2, len=I ), "-" ) )
-resp1 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
-p1 <- stats::plogis( outer( theta[,2], seq( -2, 2, len=I ), "-" ) )
-resp2 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+p1 <- stats::plogis( outer( theta[,1], seq( -2, 2, len=I ), "-" ) )
+resp1 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
+p1 <- stats::plogis( outer( theta[,2], seq( -2, 2, len=I ), "-" ) )
+resp2 <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
 resp <- cbind(resp1,resp2)
-colnames(resp) <- paste("I", 1:(2*I), sep="")
+colnames(resp) <- paste("I", 1:(2*I), sep="")
 
 # (2) define loading Matrix
-Q <- array( 0, dim=c( 2*I, 2 ))
-Q[cbind(1:(2*I), c( rep(1,I), rep(2,I) ))] <- 1
+Q <- array( 0, dim=c( 2*I, 2 ))
+Q[cbind(1:(2*I), c( rep(1,I), rep(2,I) ))] <- 1
 
 # (3) fit latent regression model
-mod <- TAM::tam.mml( resp=resp, Y=Y, Q=Q )
+mod <- TAM::tam.mml( resp=resp, Y=Y, Q=Q )
 
 # (4) draw plausible values
-pv1 <- TAM::tam.pv( mod, theta.model=TRUE )
+pv1 <- TAM::tam.pv( mod, theta.model=TRUE )
 
 # (5) convert plausible values to list of imputed datasets
-Y1 <- data.frame(Y)
-colnames(Y1) <- paste0("Y",1:2)
-pvnames <- c("PVFA","PVFB")
+Y1 <- data.frame(Y)
+colnames(Y1) <- paste0("Y",1:2)
+pvnames <- c("PVFA","PVFB")
 # create list of imputed datasets
-datlist1 <- TAM::tampv2datalist( pv1, pvnames=pvnames, Y=Y1 )
-str(datlist1)
+datlist1 <- TAM::tampv2datalist( pv1, pvnames=pvnames, Y=Y1 )
+str(datlist1)
 
 # (6) apply statistical models
-library(semTools)
+library(semTools)
 # define linear regression
 lavmodel <- "
    PVFA ~ Y1 + Y2
    PVFA ~~ PVFA
         "
-mod1 <- semTools::lavaan.mi( lavmodel, data=datlist1 )
-summary(mod1, standardized=TRUE, rsquare=TRUE)
+mod1 <- semTools::lavaan.mi( lavmodel, data=datlist1 )
+summary(mod1, standardized=TRUE, rsquare=TRUE)
 
 # (7) draw plausible values with tam.pv.mcmc function
 Y1 <- cbind( 1, Y )
-pv2 <- TAM::tam.pv.mcmc( tamobj=mod, Y=Y1, n.iter=1000, n.burnin=200 )
+pv2 <- TAM::tam.pv.mcmc( tamobj=mod, Y=Y1, n.iter=1000, n.burnin=200 )
 
 # (8) group-specific plausible values
-set.seed(908)
+set.seed(908)
 # create artificial grouping variable
-group <- sample( 1:3, N, replace=TRUE )
-pv3 <- TAM::tam.pv.mcmc( tamobj, Y=Y1, n.iter=1000, n.burnin=200, group=group )
+group <- sample( 1:3, N, replace=TRUE )
+pv3 <- TAM::tam.pv.mcmc( tamobj, Y=Y1, n.iter=1000, n.burnin=200, group=group )
 
 # (9) plausible values with no fitted object in TAM
 
 # fit IRT model without covariates
-mod4a <- TAM::tam.mml( resp=resp, Q=Q )
+mod4a <- TAM::tam.mml( resp=resp, Q=Q )
 # define input for tam.pv.mcmc
-tamobj1 <- list( AXsi=mod4a$AXsi, B=mod4a$B, resp=mod4a$resp )
-pmod4 <- TAM::tam.pv.mcmc( tamobj1, Y=Y1 )
+tamobj1 <- list( AXsi=mod4a$AXsi, B=mod4a$B, resp=mod4a$resp )
+pmod4 <- TAM::tam.pv.mcmc( tamobj1, Y=Y1 )
 
 #############################################################################
 # EXAMPLE 4: Plausible value imputation with measurement errors in covariates
 #############################################################################
 
-library(sirt)
-set.seed(7756)
+library(sirt)
+set.seed(7756)
 N <- 2000    # number of persons
 I <- 10     # number of items
 
 # simulate covariates
-X <- mvrnorm( N, mu=c(0,0), Sigma=matrix( c(1,.5,.5,1),2,2 ) )
-colnames(X) <- paste0("X",1:2)
+X <- mvrnorm( N, mu=c(0,0), Sigma=matrix( c(1,.5,.5,1),2,2 ) )
+colnames(X) <- paste0("X",1:2)
 # second covariate with measurement error with variance var.err
 var.err <- .3
 X.err <- X
-X.err[,2] <-X[,2] + rnorm(N, sd=sqrt(var.err) )
+X.err[,2] <-X[,2] + rnorm(N, sd=sqrt(var.err) )
 # simulate theta
-theta <- .5*X[,1] + .4*X[,2] + rnorm( N, sd=.5 )
+theta <- .5*X[,1] + .4*X[,2] + rnorm( N, sd=.5 )
 # simulate item responses
-itemdiff <- seq( -2, 2, length=I)  # item difficulties
-dat <- sirt::sim.raschtype( theta, b=itemdiff )
+itemdiff <- seq( -2, 2, length=I)  # item difficulties
+dat <- sirt::sim.raschtype( theta, b=itemdiff )
 
 #***********************
 #*** Model 0: Regression model with true variables
-mod0 <- stats::lm( theta ~ X )
-summary(mod0)
+mod0 <- stats::lm( theta ~ X )
+summary(mod0)
 
 #***********************
 #*** Model 1: latent regression model with true covariates X
 xsi.fixed <- cbind( 1:I, itemdiff )
-mod1 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed, Y=X)
-summary(mod1)
+mod1 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed, Y=X)
+summary(mod1)
 
 # draw plausible values
-res1a <- TAM::tam.pv( mod1, normal.approx=TRUE, ntheta=200, samp.regr=TRUE)
+res1a <- TAM::tam.pv( mod1, normal.approx=TRUE, ntheta=200, samp.regr=TRUE)
 # create list of multiply imputed datasets
-library(miceadds)
-datlist1a <- TAM::tampv2datalist( res1a, Y=X )
-imp1a <- miceadds::datalist2mids( datlist1a )
+library(miceadds)
+datlist1a <- TAM::tampv2datalist( res1a, Y=X )
+imp1a <- miceadds::datalist2mids( datlist1a )
 
 # fit linear model
 # linear regression with measurement errors in X
@@ -598,46 +598,46 @@ 
                                                                              Examp
    PV.Dim1 ~~ PV.Dim1
    X2true ~~ X2true
         "
-mod1a <- semTools::lavaan.mi( lavmodel, datlist1a)
-summary(mod1a, standardized=TRUE, rsquare=TRUE)
+mod1a <- semTools::lavaan.mi( lavmodel, datlist1a)
+summary(mod1a, standardized=TRUE, rsquare=TRUE)
 
 #***********************
 #*** Model 2: latent regression model with error-prone covariates X.err
-mod2 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed, Y=X.err)
-summary(mod2)
+mod2 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed, Y=X.err)
+summary(mod2)
 
 #***********************
 #*** Model 3: Adjustment of covariates
 
-cov.X.err <- cov( X.err )
+cov.X.err <- cov( X.err )
 # matrix of variance of measurement errors
-measerr <- diag( c(0,var.err) )
+measerr <- diag( c(0,var.err) )
 # true covariance matrix
 cov.X <- cov.X.err - measerr
 # mean of X.err
-mu <- colMeans(X.err)
-muM <- matrix( mu, nrow=nrow(X.err), ncol=ncol(X.err), byrow=TRUE)
+mu <- colMeans(X.err)
+muM <- matrix( mu, nrow=nrow(X.err), ncol=ncol(X.err), byrow=TRUE)
 # reliability matrix
-W <- solve( cov.X.err ) %*% cov.X
-ident <- diag(2)
+W <- solve( cov.X.err ) %*% cov.X
+ident <- diag(2)
 # adjusted scores of X
 X.adj <- ( X.err - muM ) %*% W   + muM %*% ( ident - W )
 
 # fit latent regression model
-mod3 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed, Y=X.adj)
-summary(mod3)
+mod3 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed, Y=X.adj)
+summary(mod3)
 
 # draw plausible values
-res3a <- TAM::tam.pv( mod3, normal.approx=TRUE, ntheta=200, samp.regr=TRUE)
+res3a <- TAM::tam.pv( mod3, normal.approx=TRUE, ntheta=200, samp.regr=TRUE)
 
 # create list of multiply imputed datasets
-library(semTools)
+library(semTools)
 
 #*** PV dataset 1
 # datalist with error-prone covariates
-datlist3a <- TAM::tampv2datalist( res3a, Y=X.err )
+datlist3a <- TAM::tampv2datalist( res3a, Y=X.err )
 # datalist with adjusted covariates
-datlist3b <- TAM::tampv2datalist( res3a, Y=X.adj )
+datlist3b <- TAM::tampv2datalist( res3a, Y=X.adj )
 
 # linear regression with measurement errors in X
 lavmodel <- "
@@ -647,97 +647,97 @@ 
                                                                              Examp
    PV.Dim1 ~~ PV.Dim1
    X2true ~~ X2true
         "
-mod3a <- semTools::lavaan.mi( lavmodel, datlist3a)
-summary(mod3a, standardized=TRUE, rsquare=TRUE)
+mod3a <- semTools::lavaan.mi( lavmodel, datlist3a)
+summary(mod3a, standardized=TRUE, rsquare=TRUE)
 
 lavmodel <- "
    PV.Dim1 ~ X1 + X2
    PV.Dim1 ~~ PV.Dim1
         "
-mod3b <- semTools::lavaan.mi( lavmodel, datlist3b)
-summary(mod3b, standardized=TRUE, rsquare=TRUE)
+mod3b <- semTools::lavaan.mi( lavmodel, datlist3b)
+summary(mod3b, standardized=TRUE, rsquare=TRUE)
 #=> mod3b leads to the correct estimate.
 
 #*********************************************
 # plausible value imputation for abilities and error-prone
 # covariates using the mice package
 
-library(mice)
-library(miceadds)
+library(mice)
+library(miceadds)
 
 # creating the likelihood for plausible value for abilities
-mod11 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed )
+mod11 <- TAM::tam.mml( dat, xsi.fixed=xsi.fixed )
 likePV <- IRT.likelihood(mod11)
 # creating the likelihood for error-prone covariate X2
 lavmodel <- "
   X2true=~ 1*X2
   X2 ~~ 0.3*X2
     "
-mod12 <- lavaan::cfa( lavmodel, data=as.data.frame(X.err) )
-summary(mod12)
-likeX2 <- TAM::IRTLikelihood.cfa( data=X.err, cfaobj=mod12)
-str(likeX2)
+mod12 <- lavaan::cfa( lavmodel, data=as.data.frame(X.err) )
+summary(mod12)
+likeX2 <- TAM::IRTLikelihood.cfa( data=X.err, cfaobj=mod12)
+str(likeX2)
 
 #-- create data input for mice package
-data <- data.frame( "PVA"=NA, "X1"=X[,1], "X2"=NA  )
-vars <- colnames(data)
-V <- length(vars)
-predictorMatrix <- 1 - diag(V)
-rownames(predictorMatrix) <- colnames(predictorMatrix) <- vars
-imputationMethod <- rep("norm", V )
-names(imputationMethod) <- vars
-imputationMethod[c("PVA","X2")] <- "plausible.values"
+data <- data.frame( "PVA"=NA, "X1"=X[,1], "X2"=NA  )
+vars <- colnames(data)
+V <- length(vars)
+predictorMatrix <- 1 - diag(V)
+rownames(predictorMatrix) <- colnames(predictorMatrix) <- vars
+imputationMethod <- rep("norm", V )
+names(imputationMethod) <- vars
+imputationMethod[c("PVA","X2")] <- "plausible.values"
 
 #-- create argument lists for plausible value imputation
 # likelihood and theta grid of plausible value derived from IRT model
-like <- list( "PVA"=likePV, "X2"=likeX2 )
-theta <- list( "PVA"=attr(likePV,"theta"), "X2"=attr(likeX2, "theta") )
+like <- list( "PVA"=likePV, "X2"=likeX2 )
+theta <- list( "PVA"=attr(likePV,"theta"), "X2"=attr(likeX2, "theta") )
 #-- initial imputations
 data.init <- data
 data.init$PVA <- mod11$person$EAP
 data.init$X2 <- X.err[,"X2"]
 
 #-- imputation using the mice and miceadds package
-imp1 <- mice::mice( as.matrix(data), predictorMatrix=predictorMatrix, m=4, maxit=6,
+imp1 <- mice::mice( as.matrix(data), predictorMatrix=predictorMatrix, m=4, maxit=6,
              method=imputationMethod,  allow.na=TRUE,
              theta=theta, like=like, data.init=data.init )
-summary(imp1)
+summary(imp1)
 
 # compute linear regression
-mod4a <- with( imp1, stats::lm( PVA ~ X1 + X2 ) )
-summary( mice::pool(mod4a) )
-# }
                                                                              
+mod4a <- with( imp1, stats::lm( PVA ~ X1 + X2 ) )
+summary( mice::pool(mod4a) )
+}
                                                                              
   
   
 
 
+
       
                                                                              
       
                                                                              
   
                                                                              Developed by Alexander Robitzsch.
                                                                              
 
                                                                              
 
 
                                                                              
-  
                                                                              Site built with pkgdown 1.3.0.
                                                                              
+  
                                                                              Site built with pkgdown 1.4.1.
                                                                              
 
                                                                              
+
       
                                                                              
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.se.html b/docs/reference/tam.se.html
index f3f34b4..4ac7bde 100644
--- a/docs/reference/tam.se.html
+++ b/docs/reference/tam.se.html
@@ -8,21 +8,25 @@
 
 Standard Error Estimation — tam.se • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                                                                
         
                                                                              • 
   
-    
+    
      
   
 
                                                                                • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                                                                
@@ -106,10 +112,8 @@ 
                                                                                Standard Error Estimation
                                                                                
     
                                                                                
 
     
                                                                                
-    
     
                                                                                Standard error computation for objects of the classes tam
 and tam.mml.
                                                                                
-    
     
                                                                                
 
     
                                                                                tam.se(tamobj, item_pars=TRUE, ...)
@@ -117,7 +121,7 @@ 
                                                                                Standard Error Estimation
                                                                                
 tam_mml_se_quick(tamobj, numdiff.parm=0.001, item_pars=TRUE )
 
 tam_latreg_se_quick(tamobj, numdiff.parm=0.001 )
                                                                                
-    
+
     
                                                                                Arguments
                                                                                
     
@@ -136,17 +140,16 @@ 
                                                                                Arg
   differentiation
                                                                                
     
     
                                                                                
-      
+      
                                                                                
     
                                                                                …...
       
                                                                                Further arguments to be passed
                                                                                		
     
                                                                                
     
                                                                                
-    
+
     
                                                                                Details
                                                                                
 
     
                                                                                Covariances between parameters estimates are ignored in this standard
 error calculation. The standard error is obtained by numerical
 differentiation.
                                                                                
-    
     
                                                                                Value
                                                                                
 
     
                                                                                A list with following entries:
                                                                                
@@ -157,26 +160,23 @@ 
                                                                                Value
                                                                                
 
                                                                                B
                                                                                Data frame with loading  parameters and their
     corresponding standard errors
                                                                                
 
-    
     
                                                                                Note
                                                                                
 
     
                                                                                Standard error estimation for variances and covariances is not yet
 implemented.
 Standard error estimation for loading parameters in case of
 irtmodel='GPCM.design' is highly experimental.
                                                                                
-    
 
     
                                                                                Examples
                                                                                
-    
                                                                                # NOT RUN {
-#############################################################################
+    
                                                                                #############################################################################
 # EXAMPLE 1: 1PL model, data.sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # estimate Rasch model
-mod1 <- TAM::tam.mml(resp=data.sim.rasch[1:500,1:10])
+mod1 <- TAM::tam.mml(resp=data.sim.rasch[1:500,1:10])
 # standard error estimation
-se1 <- TAM::tam.se( mod1 )
+se1 <- TAM::tam.se( mod1 )
 # proportion of standard errors estimated by 'tam.se' and 'tam.mml'
 prop1 <- se1$xsi$se / mod1$xsi$se
 ##   > summary( prop1 )
@@ -184,28 +184,28 @@ 
                                                                                Examp
 ##     1.030   1.034   1.035   1.036   1.039   1.042
 ##=> standard errors estimated by tam.se are a bit larger
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: Standard errors differential item functioning
 #############################################################################
-data(data.ex08)
+data(data.ex08)
 
 formulaA <- ~ item*female
 resp <- data.ex08[["resp"]]
-facets <- as.data.frame( data.ex08[["facets"]] )
+facets <- as.data.frame( data.ex08[["facets"]] )
 # investigate DIF
-mod <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA )
-summary(mod)
+mod <- TAM::tam.mml.mfr( resp=resp, facets=facets, formulaA=formulaA )
+summary(mod)
 # estimate standard errors
-semod <- TAM::tam.se(mod)
+semod <- TAM::tam.se(mod)
 prop1 <- semod$xsi$se / mod$xsi$se
-summary(prop1)
+summary(prop1)
 # plot differences in standard errors
-plot( mod$xsi$se, semod$xsi$se, pch=16, xlim=c(0,.15), ylim=c(0,.15),
+plot( mod$xsi$se, semod$xsi$se, pch=16, xlim=c(0,.15), ylim=c(0,.15),
     xlab="Standard error 'tam.mml'", ylab="Standard error 'tam.se'" )
-lines( c(-6,6), c(-6,6), col="gray")
+lines( c(-6,6), c(-6,6), col="gray")
 
-round( cbind( mod$xsi, semod$xsi[,-1] ), 3 )
+round( cbind( mod$xsi, semod$xsi[,-1] ), 3 )
   ##                    xsi se.xsi   N    est    se
   ##   I0001         -1.956  0.092 500 -1.956 0.095
   ##   I0002         -1.669  0.085 500 -1.669 0.088
@@ -219,38 +219,38 @@ 
                                                                                Examp
   ##
 #=> The largest discrepancy in standard errors is observed for the
 #    main female effect (.041 in 'tam.se' instead of .025 in 'tam.mml')
-# }
                                                                                
+}
                                                                                
   
   
 
 
+
       
                                                                                
       
                                                                                
   
                                                                                Developed by Alexander Robitzsch.
                                                                                
 
                                                                                
 
 
                                                                                
-  
                                                                                Site built with pkgdown 1.3.0.
                                                                                
+  
                                                                                Site built with pkgdown 1.4.1.
                                                                                
 
                                                                                
+
       
                                                                                
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.threshold.html b/docs/reference/tam.threshold.html
index 646f7be..6baa1c9 100644
--- a/docs/reference/tam.threshold.html
+++ b/docs/reference/tam.threshold.html
@@ -8,21 +8,25 @@
 
 Calculation of Thurstonian Thresholds — tam.threshold • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                                                                  
         
                                                                                • 
   
-    
+    
      
   
 
                                                                                  • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                                                                  
@@ -106,14 +112,12 @@ 
                                                                                  Calculation of Thurstonian Thresholds
                                                                                  
     
                                                                                  
 
     
                                                                                  
-    
     
                                                                                  This function estimates Thurstonian thresholds for item category
 parameters of (generalized) partial credit models (see Details).
                                                                                  
-    
     
                                                                                  
 
     
                                                                                  tam.threshold(tamobj, prob.lvl=0.5)
                                                                                  
-    
+
     
                                                                                  Arguments
                                                                                  
     
@@ -127,34 +131,30 @@ 
                                                                                  Arg
 The default is prob.lvl=0.5.
                                                                                  
     
     
                                                                                  
     
                                                                                  
-    
+
     
                                                                                  Details
                                                                                  
 
     
                                                                                  This function only works appropriately for unidimensional models
 or between item multidimensional models.
                                                                                  
-    
     
                                                                                  Value
                                                                                  
 
     
                                                                                  A data frame with Thurstonian thresholds. Rows correspond to items and
 columns to item steps.
                                                                                  
-    
     
                                                                                  See also
                                                                                  
 
     
                                                                                  See the WrightMap package and Example 3 for creating Wright maps
 with fitted models in TAM, see
-wrightMap.
                                                                                  
-    
+wrightMap.
                                                                                  
 
     
                                                                                  Examples
                                                                                  
-    
                                                                                  # NOT RUN {
-#############################################################################
+    
                                                                                  #############################################################################
 # EXAMPLE 1: ordered data - Partial credit model
 #############################################################################
-data( data.gpcm )
+data( data.gpcm )
 
 # Model 1: partial credit model
-mod1 <- TAM::tam.mml( resp=data.gpcm,control=list( maxiter=200) )
-summary(mod1)
+mod1 <- TAM::tam.mml( resp=data.gpcm,control=list( maxiter=200) )
+summary(mod1)
   ##   Item Parameters -A*Xsi
   ##        item   N     M AXsi_.Cat1 AXsi_.Cat2 AXsi_.Cat3 B.Cat1.Dim1 B.Cat2.Dim1 B.Cat3.Dim1
   ##   1 Comfort 392 0.880     -1.302      1.154      3.881           1           2           3
@@ -162,96 +162,96 @@ 
                                                                                  Examp
   ##   3 Benefit 392 1.163     -1.233     -0.404      1.806           1           2           3
 
 # Calculation of Thurstonian thresholds
-TAM::tam.threshold(mod1)
+TAM::tam.threshold(mod1)
   ##                Cat1      Cat2     Cat3
   ##   Comfort -1.325226 2.0717468 3.139801
   ##   Work    -1.777679 0.6459045 1.971222
   ##   Benefit -1.343536 0.7491760 2.403168
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: Multidimensional model data.math
 #############################################################################
 
-library(sirt)
-data(data.math, package="sirt")
+library(sirt)
+data(data.math, package="sirt")
 dat <- data.math$data
 # select items
-items1 <- grep("M[A-D]", colnames(dat), value=TRUE)
-items2 <- grep("M[H-I]", colnames(dat), value=TRUE)
+items1 <- grep("M[A-D]", colnames(dat), value=TRUE)
+items2 <- grep("M[H-I]", colnames(dat), value=TRUE)
 # select dataset
-dat <- dat[ c(items1,items2)]
+dat <- dat[ c(items1,items2)]
 # create Q-matrix
-Q <- matrix( 0, nrow=ncol(dat), ncol=2 )
-Q[ seq(1,length(items1) ), 1 ] <- 1
-Q[ length(items1) + seq(1,length(items2) ), 2 ] <- 1
+Q <- matrix( 0, nrow=ncol(dat), ncol=2 )
+Q[ seq(1,length(items1) ), 1 ] <- 1
+Q[ length(items1) + seq(1,length(items2) ), 2 ] <- 1
 
 # fit two-dimensional model
-mod1 <- TAM::tam.mml( dat, Q=Q )
+mod1 <- TAM::tam.mml( dat, Q=Q )
 # compute thresholds (specify a probability level of .625)
-tmod1 <- TAM::tam.threshold( mod1, prob.lvl=.625 )
+tmod1 <- TAM::tam.threshold( mod1, prob.lvl=.625 )
 
 #############################################################################
 # EXAMPLE 3: Creating Wright maps with the WrightMap package
 #############################################################################
 
-library(WrightMap)
+library(WrightMap)
 # For conducting Wright maps in combination with TAM, see
 # http://wrightmap.org/post/100850738072/using-wrightmap-with-the-tam-package
-data(sim.rasch)
+data(sim.rasch)
 dat <- sim.rasch
 
 # estimate Rasch model in TAM
-mod1 <- TAM::tam.mml(dat)
-summary(mod1)
+mod1 <- TAM::tam.mml(dat)
+summary(mod1)
 
 #--- A: creating a Wright map with WLEs
 
 # compute WLE
-wlemod1 <- TAM::tam.wle(mod1)$theta
+wlemod1 <- TAM::tam.wle(mod1)$theta
 # extract thresholds
-tmod1 <- TAM::tam.threshold(mod1)
+tmod1 <- TAM::tam.threshold(mod1)
 # create Wright map
-WrightMap::wrightMap( thetas=wlemod1, thresholds=tmod1, label.items.srt=-90)
+WrightMap::wrightMap( thetas=wlemod1, thresholds=tmod1, label.items.srt=-90)
 
 #--- B: creating a Wright Map with population distribution
 
 # extract ability distribution and replicate observations
-uni.proficiency <- rep( mod1$theta[,1], round( mod1$pi.k * mod1$ic$n) )
+uni.proficiency <- rep( mod1$theta[,1], round( mod1$pi.k * mod1$ic$n) )
 # draw WrightMap
-WrightMap::wrightMap( thetas=uni.proficiency, thresholds=tmod1, label.items.rows=3)
-# }
                                                                                  
+WrightMap::wrightMap( thetas=uni.proficiency, thresholds=tmod1, label.items.rows=3)
+}
                                                                                  
   
   
 
 
+
       
                                                                                  
       
                                                                                  
   
                                                                                  Developed by Alexander Robitzsch.
                                                                                  
 
                                                                                  
 
 
                                                                                  
-  
                                                                                  Site built with pkgdown 1.3.0.
                                                                                  
+  
                                                                                  Site built with pkgdown 1.4.1.
                                                                                  
 
                                                                                  
+
       
                                                                                  
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam.wle.html b/docs/reference/tam.wle.html
index 8d156d0..d495a08 100644
--- a/docs/reference/tam.wle.html
+++ b/docs/reference/tam.wle.html
@@ -9,21 +9,25 @@
 Weighted Likelihood Estimation and Maximum Likelihood Estimation of
 Person Parameters — tam.wle • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -31,9 +35,9 @@
 
 
 
+
 
-
 
@@ -41,6 +45,7 @@
 
 
 
+
 
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
                                                                                    
         
                                                                                  • 
   
-    
+    
      
   
 
                                                                                    • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
                                                                                    
@@ -110,31 +116,29 @@ 
                                                                                    Weighted Likelihood Estimation and Maximum Likelihood Estimation of
     
                                                                                    
 
     
                                                                                    
-    
     
                                                                                    Compute the weighted likelihood estimator (Warm, 1989)
 for objects of classes tam, tam.mml and tam.jml,
 respectively.
                                                                                    
-    
     
                                                                                    
 
-    
                                                                                    tam.wle(tamobj, ...)
+    
                                                                                    tam.wle(tamobj, ...)
 
-tam.mml.wle( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
-       convM=.0001, progress=TRUE,    output.prob=FALSE )
+tam.mml.wle( tamobj, score.resp=NULL, WLE=TRUE, adj=.3, Msteps=20,
+       convM=.0001, progress=TRUE,    output.prob=FALSE )
 
-tam.mml.wle2(tamobj, score.resp=NULL, WLE=TRUE, adj=0.3, Msteps=20, convM=1e-04,
-        progress=TRUE, output.prob=FALSE, pid=NULL )
+tam.mml.wle2(tamobj, score.resp=NULL, WLE=TRUE, adj=0.3, Msteps=20, convM=1e-04,
+        progress=TRUE, output.prob=FALSE, pid=NULL )
 
-tam_jml_wle(tamobj, resp, resp.ind, A, B, nstud, nitems, maxK, convM,
-    PersonScores, theta, xsi, Msteps, WLE=FALSE, theta.fixed=NULL, progress=FALSE,
-    output.prob=TRUE)
+tam_jml_wle(tamobj, resp, resp.ind, A, B, nstud, nitems, maxK, convM,
+    PersonScores, theta, xsi, Msteps, WLE=FALSE, theta.fixed=NULL, progress=FALSE,
+    output.prob=TRUE)
 
-# S3 method for tam.wle
-summary(object, file=NULL, digits=3, …)
+# S3 method for tam.wle
+summary(object, file=NULL, digits=3, ...)
+
+# S3 method for tam.wle
+print(x, digits=3, ...)
                                                                                    
 
-# S3 method for tam.wle
-print(x, digits=3, …)
                                                                                    
-    
     
                                                                                    Arguments
                                                                                    
     
@@ -234,7 +238,7 @@ 
                                                                                    Arg
 the fixed value.
                                                                                    
     
     
                                                                                    
-      
+      
@@ -254,7 +258,7 @@ 
                                                                                    Arg
       
                                                                                    
     
                                                                                    …...
       
                                                                                    Further arguments to be passed
                                                                                    		
     
                                                                                    
     
                                                                                    Number of digits for rounding
                                                                                    		
     
                                                                                    
     
                                                                                    
-    
+
     
                                                                                    Value
                                                                                    
 
     
                                                                                    For tam.wle.mml and tam.wle.mml2, it is a data frame with following
@@ -272,7 +276,6 @@ 
                                                                                    Value
                                                                                    
 
                                                                                    meanChangeWLE
                                                                                    Mean change between updated and previous ability
     estimates from last iteration
                                                                                    
 
-    
     
                                                                                    References
                                                                                    
 
     
                                                                                    Penfield, R. D., & Bergeron, J. M. (2005). Applying a weighted maximum
@@ -280,50 +283,47 @@ 
                                                                                    R
 Applied Psychological Measurement, 29, 218-233.
                                                                                    
 
                                                                                    Warm, T. A. (1989). Weighted likelihood estimation of ability in item
 response theory. Psychometrika, 54, 427-450.
                                                                                    
-    
     
                                                                                    See also
                                                                                    
 
     
                                                                                    See the PP::PP_gpcm function
 in the PP package for more person
 parameter estimators for the partial credit model (Penfield & Bergeron, 2005).
                                                                                    
 
                                                                                    See the S3 method IRT.factor.scores.tam.
                                                                                    
-    
 
     
                                                                                    Examples
                                                                                    
-    
                                                                                    # NOT RUN {
-#############################################################################
+    
                                                                                    #############################################################################
 # EXAMPLE 1: 1PL model, data.sim.rasch
 #############################################################################
 
-data(data.sim.rasch)
+data(data.sim.rasch)
 # estimate Rasch model
-mod1 <- TAM::tam.mml(resp=data.sim.rasch)
+mod1 <- TAM::tam.mml(resp=data.sim.rasch)
 # WLE estimation
-wle1 <- TAM::tam.wle( mod1 )
+wle1 <- TAM::tam.wle( mod1 )
   ## WLE Reliability=0.894
 
-print(wle1)
-summary(wle1)
+print(wle1)
+summary(wle1)
 
 # scoring for a different dataset containing same items (first 10 persons in sim.rasch)
-wle2 <- TAM::tam.wle( mod1, score.resp=data.sim.rasch[1:10,])
+wle2 <- TAM::tam.wle( mod1, score.resp=data.sim.rasch[1:10,])
 
 #--- WLE estimation without using a TAM object
 
 #* create an input list
-input <- list( resp=data.sim.rasch, AXsi=mod1$AXsi, B=mod1$B )
+input <- list( resp=data.sim.rasch, AXsi=mod1$AXsi, B=mod1$B )
 #* estimation
-wle2b <- TAM::tam.mml.wle2( input )
+wle2b <- TAM::tam.mml.wle2( input )
 
-# }# NOT RUN {
+if (FALSE) {
 #############################################################################
 # EXAMPLE 2: 3-dimensional Rasch model | data.read from sirt package
 #############################################################################
 
-data(data.read, package="sirt")
+data(data.read, package="sirt")
 # define Q-matrix
-Q <- matrix(0,12,3)
-Q[ cbind( 1:12, rep(1:3,each=4) ) ] <- 1
+Q <- matrix(0,12,3)
+Q[ cbind( 1:12, rep(1:3,each=4) ) ] <- 1
 # redefine data: create some missings for first three cases
 resp <- data.read
 resp[1:2, 5:12] <- NA
@@ -338,12 +338,12 @@ 
                                                                                    Examp
   ##   63  1  1  0  0  1  0  1  1  1  1  1  1
 
 # estimate 3-dimensional Rasch model
-mod <- TAM::tam.mml( resp=resp, Q=Q, control=list(snodes=1000,maxiter=50) )
-summary(mod)
+mod <- TAM::tam.mml( resp=resp, Q=Q, control=list(snodes=1000,maxiter=50) )
+summary(mod)
 
 # WLE estimates
-wmod <- TAM::tam.wle(mod, Msteps=3)
-summary(wmod)
+wmod <- TAM::tam.wle(mod, Msteps=3)
+summary(wmod)
   ##   head(round(wmod,2))
   ##      pid N.items PersonScores.Dim01 PersonScores.Dim02 PersonScores.Dim03
   ##   2    1       4                3.7                0.3                0.3
@@ -374,35 +374,35 @@ 
                                                                                    Examp
 # EXAMPLE 3: Partial credit model | Comparison WLEs with PP package
 #############################################################################
 
-library(PP)
-data(data.gpcm)
+library(PP)
+data(data.gpcm)
 dat <- data.gpcm
-I <- ncol(dat)
+I <- ncol(dat)
 
 #****************************************
 #*** Model 1: Partial Credit Model
 
 # estimation in TAM
-mod1 <- TAM::tam.mml( dat )
-summary(mod1)
+mod1 <- TAM::tam.mml( dat )
+summary(mod1)
 
 #-- WLE estimation in TAM
-tamw1 <- TAM::tam.wle( mod1 )
+tamw1 <- TAM::tam.wle( mod1 )
 
 #-- WLE estimation with PP package
 # convert AXsi parameters into thres parameters for PP
 AXsi0 <- - mod1$AXsi[,-1]
 b <- AXsi0
-K <- ncol(AXsi0)
+K <- ncol(AXsi0)
 for (cc in 2:K){
     b[,cc] <- AXsi0[,cc] - AXsi0[,cc-1]
 }
 # WLE estimation in PP
-ppw1 <- PP::PP_gpcm( respm=as.matrix(dat),  thres=t(b), slopes=rep(1,I) )
+ppw1 <- PP::PP_gpcm( respm=as.matrix(dat),  thres=t(b), slopes=rep(1,I) )
 
 #-- compare results
-dfr <- cbind( tamw1[, c("theta","error") ], ppw1$resPP)
-head( round(dfr,3))
+dfr <- cbind( tamw1[, c("theta","error") ], ppw1$resPP)
+head( round(dfr,3))
   ##      theta error resPP.estimate resPP.SE nsteps
   ##   1 -1.006 0.973         -1.006    0.973      8
   ##   2 -0.122 0.904         -0.122    0.904      8
@@ -410,36 +410,36 @@ 
                                                                                    Examp
   ##   4  0.640 0.836          0.640    0.836      8
   ##   5  0.640 0.836          0.640    0.836      8
   ##   6 -1.941 1.106         -1.941    1.106      8
-plot( dfr$resPP.estimate, dfr$theta, pch=16, xlab="PP", ylab="TAM")
-lines( c(-10,10), c(-10,10) )
+plot( dfr$resPP.estimate, dfr$theta, pch=16, xlab="PP", ylab="TAM")
+lines( c(-10,10), c(-10,10) )
 
 #****************************************
 #*** Model 2: Generalized partial Credit Model
 
 # estimation in TAM
-mod2 <- TAM::tam.mml.2pl( dat, irtmodel="GPCM" )
-summary(mod2)
+mod2 <- TAM::tam.mml.2pl( dat, irtmodel="GPCM" )
+summary(mod2)
 
 #-- WLE estimation in TAM
-tamw2 <- TAM::tam.wle( mod2 )
+tamw2 <- TAM::tam.wle( mod2 )
 
 #-- WLE estimation in PP
 # convert AXsi parameters into thres and slopes parameters for PP
 AXsi0 <- - mod2$AXsi[,-1]
 slopes <- mod2$B[,2,1]
-K <- ncol(AXsi0)
-slopesM <- matrix( slopes, I, ncol=K )
+K <- ncol(AXsi0)
+slopesM <- matrix( slopes, I, ncol=K )
 AXsi0 <- AXsi0 / slopesM
 b <- AXsi0
 for (cc in 2:K){
     b[,cc] <- AXsi0[,cc] - AXsi0[,cc-1]
 }
 # estimation in PP
-ppw2 <- PP::PP_gpcm( respm=as.matrix(dat),  thres=t(b), slopes=slopes )
+ppw2 <- PP::PP_gpcm( respm=as.matrix(dat),  thres=t(b), slopes=slopes )
 
 #-- compare results
-dfr <- cbind( tamw2[, c("theta","error") ], ppw2$resPP)
-head( round(dfr,3))
+dfr <- cbind( tamw2[, c("theta","error") ], ppw2$resPP)
+head( round(dfr,3))
   ##      theta error resPP.estimate resPP.SE nsteps
   ##   1 -0.476 0.971         -0.476    0.971     13
   ##   2 -0.090 0.973         -0.090    0.973     13
@@ -447,38 +447,38 @@ 
                                                                                    Examp
   ##   4  0.311 0.960          0.311    0.960     13
   ##   5  1.749 0.813          1.749    0.813     13
   ##   6 -1.513 1.032         -1.513    1.032     13
-# }
                                                                                    
+}
                                                                                    
   
   
 
 
+
       
                                                                                    
       
                                                                                    
   
                                                                                    Developed by Alexander Robitzsch.
                                                                                    
 
                                                                                    
 
 
                                                                                    
-  
                                                                                    Site built with pkgdown 1.3.0.
                                                                                    
+  
                                                                                    Site built with pkgdown 1.4.1.
                                                                                    
 
                                                                                    
+
       
                                                                                    
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam_NA_pattern.html b/docs/reference/tam_NA_pattern.html
index a82ddbd..7d15d0f 100644
--- a/docs/reference/tam_NA_pattern.html
+++ b/docs/reference/tam_NA_pattern.html
@@ -8,21 +8,25 @@
 
 Missing Data Patterns — tam_NA_pattern • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                                                                      
         
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-    
+    
      
   
 
                                                                                      • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                                                                      
@@ -106,16 +112,14 @@ 
                                                                                      Missing Data Patterns
                                                                                      
     
                                                                                      
 
     
                                                                                      
-    
     
                                                                                      Determines patterns of missing values or pattern of dichotomous item
 responses.
                                                                                      
-    
     
                                                                                      
 
     
                                                                                      tam_NA_pattern(x)
 
 tam_01_pattern(x)
                                                                                      
-    
+
     
                                                                                      Arguments
                                                                                      
     
@@ -124,61 +128,60 @@ 
                                                                                      Arg
       
                                                                                      
     
                                                                                      Matrix or data frame
                                                                                      		
     
                                                                                      
     
                                                                                      
-    
+
     
                                                                                      Value
                                                                                      
 
     
                                                                                      List containing identifiers and indices
                                                                                      
-    
 
     
                                                                                      Examples
                                                                                      
-    
                                                                                      # NOT RUN {
-#############################################################################
+    
                                                                                      #############################################################################
 # EXAMPLE 1: Missing data patterns
 #############################################################################
 
-data(data.sim.rasch.missing, package="TAM")
+data(data.sim.rasch.missing, package="TAM")
 dat <- data.sim.rasch.missing
 
-res <- TAM::tam_NA_pattern(dat)
-str(res)
+res <- TAM::tam_NA_pattern(dat)
+str(res)
 
 #############################################################################
 # EXAMPLE 2: Item response patterns
 #############################################################################
 
-data(data.read, package="sirt")
+data(data.read, package="sirt")
 dat <- data.read
 
-res <- TAM::tam_01_pattern(dat)
-str(res)
-# }
                                                                                      
+res <- TAM::tam_01_pattern(dat)
+str(res)
                                                                                      
   
   
 
 
+
       
                                                                                      
       
                                                                                      
   
                                                                                      Developed by Alexander Robitzsch.
                                                                                      
 
                                                                                      
 
 
                                                                                      
-  
                                                                                      Site built with pkgdown 1.3.0.
                                                                                      
+  
                                                                                      Site built with pkgdown 1.4.1.
                                                                                      
 
                                                                                      
+
       
                                                                                      
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tam_downcode.html b/docs/reference/tam_downcode.html
new file mode 100644
index 0000000..6900394
--- /dev/null
+++ b/docs/reference/tam_downcode.html
@@ -0,0 +1,189 @@
+
+
+
+  
+  
+
+
+
+Downcoding an Item Response Dataset — tam_downcode • TAM
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                      
+      
                                                                                      
+      
+
+      
+
+      
                                                                                      
+
+
                                                                                      
+  
                                                                                      
+    
                                                                                      
+    
                                                                                      Downcoding an Item Response Dataset
                                                                                      
+    
+    
+    
                                                                                      
+
+    
                                                                                      
+    
                                                                                      Recodes item categories in a data frame such that each item has values
+\(0,1,\ldots,K_i\).
                                                                                      
+    
                                                                                      
+
+    
                                                                                      tam_downcode(dat)
                                                                                      
+
+    
                                                                                      Arguments
                                                                                      
+    
+    
+    
+      
+      
+    
+    
                                                                                      dat
                                                                                      Data frame containing item responses
                                                                                      
+
+    
                                                                                      Value
                                                                                      
+
+    
                                                                                      List with following entries
                                                                                      
+
                                                                                      dat
                                                                                      Recoded dataset
                                                                                      
+
                                                                                      rec
                                                                                      Recoding table
                                                                                      
+
+
+    
                                                                                      Examples
                                                                                      
+    
                                                                                      #############################################################################
+# EXAMPLE 1: Downcoding in a toy example
+#############################################################################
+
+#-- simulate some toy data
+set.seed(989)
+# values to be sampled
+vals <- c(NA, 0:6)
+# number of persons and items
+N <- 10; I <- 5
+dat <- as.data.frame(matrix(NA, nrow=N, ncol=I))
+colnames(dat) <- paste0("I",1:I)
+for (ii in 1L:I){
+    dat[,ii] <- sample(vals, size=N, replace=TRUE)
+}
+
+#-- apply downcoding
+res <- TAM::tam_downcode(dat)
+dat <- res$dat   # extract downcoded dataset
+rec <- res$rec   # extract recoded table
                                                                                      
+  
                                                                                      
+  
+
                                                                                      
+
+
+      
                                                                                      
+      
                                                                                      
+  
                                                                                      Developed by Alexander Robitzsch.
                                                                                      
+
                                                                                      
+
+
                                                                                      
+  
                                                                                      Site built with pkgdown 1.4.1.
                                                                                      
+
                                                                                      
+
+      
                                                                                      
+   
                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/tam_irf_3pl.html b/docs/reference/tam_irf_3pl.html
index f989718..ac2c9d9 100644
--- a/docs/reference/tam_irf_3pl.html
+++ b/docs/reference/tam_irf_3pl.html
@@ -8,21 +8,25 @@
 
 Item Response Function for the 3PL Model — tam_irf_3pl • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,14 +34,15 @@
 
 
 
-
 
+
 
 
 
 
 
+
 
 
 
@@ -48,6 +53,7 @@
 
 
 
+
   
 
   
@@ -72,7 +78,7 @@
       
-      
       
                                                                                        
         
                                                                                      • 
   
-    
+    
      
   
 
                                                                                        • 
@@ -95,6 +100,7 @@
 
 
       
+
       
 
 
                                                                                        
@@ -106,14 +112,12 @@ 
                                                                                        Item Response Function for the 3PL Model
                                                                                        
     
                                                                                        
 
     
                                                                                        
-    
     
                                                                                        Computes the item response function for the 3PL model in the TAM
 package.
                                                                                        
-    
     
                                                                                        
 
     
                                                                                        tam_irf_3pl(theta, AXsi, B, guess=NULL, subtract_max=TRUE)
                                                                                        
-    
+
     
                                                                                        Arguments
                                                                                        
     
@@ -139,58 +143,59 @@ 
                                                                                        Arg
 probabilities should be explicitly avoided
                                                                                        
     
     
                                                                                        
     
                                                                                        
-    
+
     
                                                                                        Value
                                                                                        
 
     
                                                                                        Array containing item response probabilities arranged by the dimensions
 theta points \(\times\) items \(\times\) categories
                                                                                        
-    
 
     
                                                                                        Examples
                                                                                        
-    
                                                                                        # NOT RUN {
+    
                                                                                        if (FALSE) {
 #############################################################################
 # EXAMPLE 1: 2PL example
 #############################################################################
 
-library(sirt)
-data(data.read, package="sirt")
+library(sirt)
+data(data.read, package="sirt")
 dat <- data.read
 
 #* estimate 2PL model
-mod <- TAM::tam.mml.2pl( resp=dat )
+mod <- TAM::tam.mml.2pl( resp=dat )
 #* define theta vector
-theta <- seq(-3,3, len=41)
+theta <- seq(-3,3, len=41)
 #* compute item response probabilities
-probs <- TAM::tam_irf_3pl( theta=theta, AXsi=mod$AXsi, B=mod$B )
-str(probs)
-# }
                                                                                        
+probs <- TAM::tam_irf_3pl( theta=theta, AXsi=mod$AXsi, B=mod$B )
+str(probs)
+}
                                                                                        
   
   
 
 
+
       
                                                                                        
       
                                                                                        
   
                                                                                        Developed by Alexander Robitzsch.
                                                                                        
 
                                                                                        
 
 
                                                                                        
-  
                                                                                        Site built with pkgdown 1.3.0.
                                                                                        
+  
                                                                                        Site built with pkgdown 1.4.1.
                                                                                        
 
                                                                                        
+
       
                                                                                        
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tamaan.html b/docs/reference/tamaan.html
index b18d38c..51a31e0 100644
--- a/docs/reference/tamaan.html
+++ b/docs/reference/tamaan.html
@@ -8,21 +8,25 @@
 
 Wrapper Function for <span class="pkg">TAM</span> Language — tamaan • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM Language — tamaan" />
 
+TAM Language — tamaan" />
 
 
@@ -51,6 +56,7 @@
 
 
 
+
   
 
   
@@ -75,7 +81,7 @@
       
-      
       
                                                                                          
         
                                                                                        • 
   
-    
+    
      
   
 
                                                                                          • 
@@ -98,6 +103,7 @@
 
 
       
+
       
 
 
                                                                                          
@@ -109,23 +115,21 @@ 
                                                                                          Wrapper Function for TAM Language
                                                                                          
     
                                                                                          
 
     
                                                                                          
-    
     
                                                                                          This function is a convenience wrapper function for
 several item response models in TAM. Using the
 tamaanify framework, multidimensional item response models,
 latent class models, located and ordered latent class models
 and mixture item response models can be estimated.
                                                                                          
-    
     
                                                                                          
 
-    
                                                                                          tamaan(tammodel, resp, tam.method=NULL, control=list(), doparse=TRUE, ...)
+    
                                                                                          tamaan(tammodel, resp, tam.method=NULL, control=list(), doparse=TRUE, ...)
 
-# S3 method for tamaan
-summary(object,file=NULL,…)
+# S3 method for tamaan
+summary(object,file=NULL,...)
+
+# S3 method for tamaan
+print(x,...)
                                                                                          
 
-# S3 method for tamaan
-print(x,…)
                                                                                          
-    
     
                                                                                          Arguments
                                                                                          
     
@@ -152,7 +156,7 @@ 
                                                                                          Arg
 should be parsed for DO statements, see doparse.
                                                                                          
     
     
                                                                                          
-      
+      
@@ -170,7 +174,7 @@ 
                                                                                          Arg
       
                                                                                          
     
                                                                                          …...
       
                                                                                          Further arguments to be passed to
 tam.mml, tam.mml.2pl
 or tam.mml.3pl.
                                                                                          Object of class tamaan
                                                                                          		
     
                                                                                          
     
                                                                                          
-    
+
     
                                                                                          Value
                                                                                          
 
     
                                                                                          Values generated by tam.mml, tam.mml.2pl
@@ -184,23 +188,20 @@ 
                                                                                          Value
                                                                                          
 
                                                                                          ind_classprobs
                                                                                          Individual posterior probabilities for
   latent classes (for TYPE="MIXTURE")
                                                                                          
 
-    
     
                                                                                          See also
                                                                                          
 
     
                                                                                          See tamaanify for more details about model specification
 using tammodel.
                                                                                          
 
                                                                                          See tam.mml or tam.mml.3pl
 for more examples.
                                                                                          
-    
 
     
                                                                                          Examples
                                                                                          
-    
                                                                                          # NOT RUN {
-#############################################################################
+    
                                                                                          #############################################################################
 # EXAMPLE 1: Examples dichotomous data data.read
 #############################################################################
 
-library(sirt)
-data(data.read,package="sirt")
+library(sirt)
+data(data.read,package="sirt")
 dat <- data.read
 
 #*********************************************************************
@@ -214,10 +215,10 @@ 
                                                                                          Examp
   ALL(Rasch);
     "
 # estimate model
-mod1 <- TAM::tamaan( tammodel, resp=dat)
-summary(mod1)
+mod1 <- TAM::tamaan( tammodel, resp=dat)
+summary(mod1)
 
-# }# NOT RUN {
+if (FALSE) {
 #*********************************************************************
 #*** Model 2: 2PL model with some selected items
 
@@ -226,8 +227,8 @@ 
                                                                                          Examp
   F1=~ A1__B1 + B3 + C1__C3
   F1 ~~ F1
     "
-mod2 <- TAM::tamaan( tammodel, resp=dat)
-summary(mod2)
+mod2 <- TAM::tamaan( tammodel, resp=dat)
+summary(mod2)
 
 #*********************************************************************
 #*** Model 3: Multidimensional IRT model
@@ -245,8 +246,8 @@ 
                                                                                          Examp
   G  ~~ 0.3*F2
   G  ~~ 0.7*G
     "
-mod3 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=30))
-summary(mod3)
+mod3 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=30))
+summary(mod3)
 
 #*********************************************************************
 #*** Model 4: Some linear constraints for item slopes and intercepts
@@ -278,8 +279,8 @@ 
                                                                                          Examp
   lam8==lam0
   lam9==lam0
     "
-mod4 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=5) )
-summary(mod4)
+mod4 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=5) )
+summary(mod4)
 
 #*********************************************************************
 #*** Model 5: Latent class analysis with three classes
@@ -292,8 +293,8 @@ 
                                                                                          Examp
 LAVAAN MODEL:
   F=~ A1__C4
     "
-mod5 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=100)  )
-summary(mod5)
+mod5 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=100)  )
+summary(mod5)
 
 #*********************************************************************
 #*** Model 6: Ordered latent class analysis with three classes
@@ -306,8 +307,8 @@ 
                                                                                          Examp
 LAVAAN MODEL:
   F=~ A1__C4
     "
-mod6 <- TAM::tamaan( tammodel, dat )
-summary(mod6)
+mod6 <- TAM::tamaan( tammodel, dat )
+summary(mod6)
 
 #*********************************************************************
 #*** Model 7: Unidimensional located latent class model with three classes
@@ -321,8 +322,8 @@ 
                                                                                          Examp
   F=~ A1__C4
   B2 | 0*t1
     "
-mod7 <- TAM::tamaan( tammodel, resp=dat)
-summary(mod7)
+mod7 <- TAM::tamaan( tammodel, resp=dat)
+summary(mod7)
 
 #*********************************************************************
 #*** Model 8: Two-dimensional located latent class analysis with some
@@ -347,8 +348,8 @@ 
                                                                                          Examp
   DOEND
     "
 # estimate model
-mod8 <- TAM::tamaan( tammodel, resp=dat )
-summary(mod8)
+mod8 <- TAM::tamaan( tammodel, resp=dat )
+summary(mod8)
 
 #*********************************************************************
 #*** Model 9: Two-dimensional model with constraints on parameters
@@ -365,15 +366,15 @@ 
                                                                                          Examp
   A2 | .7*t1
     "
 # estimate model
-mod9 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=30) )
-summary(mod9)
+mod9 <- TAM::tamaan( tammodel, resp=dat, control=list(maxiter=30) )
+summary(mod9)
 
 #############################################################################
 # EXAMPLE 2: Examples polytomous data | data.Students
 #############################################################################
 
-library(CDM)
-data( data.Students, package="CDM")
+library(CDM)
+data( data.Students, package="CDM")
 dat <- data.Students[,3:13]
   ##   > colnames(dat)
   ##    [1] "act1" "act2" "act3" "act4" "act5" "sc1"  "sc2"  "sc3"  "sc4"  "mj1"  "mj2"
@@ -390,8 +391,8 @@ 
                                                                                          Examp
   FA ~~ FS
     "
 # estimate model
-mod1 <- TAM::tamaan( tammodel, dat, control=list(maxiter=10)  )
-summary(mod1)
+mod1 <- TAM::tamaan( tammodel, dat, control=list(maxiter=10)  )
+summary(mod1)
 
 #*********************************************************************
 #*** Model 2: Two-dimensional model, some constraints
@@ -408,11 +409,11 @@ 
                                                                                          Examp
   act1 + act3 | a13_t2 * t2
     "
 # only create design matrices with tamaanify
-mod2 <- TAM::tamaanify( tammodel, dat  )
+mod2 <- TAM::tamaanify( tammodel, dat  )
 mod2$lavpartable
 # estimate model (only few iterations as a test)
-mod2 <- TAM::tamaan( tammodel, dat, control=list(maxiter=10)  )
-summary(mod2)
+mod2 <- TAM::tamaan( tammodel, dat, control=list(maxiter=10)  )
+summary(mod2)
 
 #*********************************************************************
 #*** Model 3: Two-dimensional model, some more linear constraints
@@ -434,8 +435,8 @@ 
                                                                                          Examp
   a5==q2 + q1
     "
 # estimate
-mod3 <- TAM::tamaan( tammodel, dat, control=list(maxiter=300 )  )
-summary(mod3)
+mod3 <- TAM::tamaan( tammodel, dat, control=list(maxiter=300 )  )
+summary(mod3)
 
 #*********************************************************************
 #*** Model 4: Latent class analysis with three latent classes
@@ -449,14 +450,14 @@ 
                                                                                          Examp
   F=~ act1__act5
     "
 # estimate model
-mod4 <- TAM::tamaan( tammodel, resp=dat)
-summary(mod4)
+mod4 <- TAM::tamaan( tammodel, resp=dat)
+summary(mod4)
 
 #*********************************************************************
 #*** Model 5: Partial credit model with "PCM2" parametrization
 
 # select data
-dat1 <- dat[, paste0("act",1:5) ]
+dat1 <- dat[, paste0("act",1:5) ]
 # specify tamaan model
 tammodel <- "
   LAVAAN MODEL:
@@ -476,18 +477,18 @@ 
                                                                                          Examp
     ALL(PCM)
   "
 # estimate model
-mod5 <- TAM::tamaan( tammodel, dat1 )
-summary(mod5)
+mod5 <- TAM::tamaan( tammodel, dat1 )
+summary(mod5)
 # compare with PCM2 parametrization in tam.mml
-mod5b <- TAM::tam.mml( dat1, irtmodel="PCM2" )
-summary(mod5b)
+mod5b <- TAM::tam.mml( dat1, irtmodel="PCM2" )
+summary(mod5b)
 
 #*********************************************************************
 #*** Model 6: Rating scale model
 
 # select data
-dat1 <- dat[, paste0("sc",1:4) ]
-psych::describe(dat1)
+dat1 <- dat[, paste0("sc",1:4) ]
+psych::describe(dat1)
 
 # specify tamaan model
 tammodel <- "
@@ -510,11 +511,11 @@ 
                                                                                          Examp
     ALL(PCM)
   "
 # estimate model
-mod6 <- TAM::tamaan( tammodel, dat1 )
-summary(mod6)
+mod6 <- TAM::tamaan( tammodel, dat1 )
+summary(mod6)
 # compare with RSM in tam.mml
-mod6b <- TAM::tam.mml( dat1, irtmodel="RSM" )
-summary(mod6b)
+mod6b <- TAM::tam.mml( dat1, irtmodel="RSM" )
+summary(mod6b)
 
 #*********************************************************************
 #*** Model 7: Partial credit model with Fourier basis for
@@ -533,8 +534,8 @@ 
                                                                                          Examp
   mj4(PCM,1)
    "
 # estimate model
-mod7 <- TAM::tamaan( tammodel, dat )
-summary(mod7)
+mod7 <- TAM::tamaan( tammodel, dat )
+summary(mod7)
 # -> This function can also be applied for the generalized partial credit
 #    model (GPCM).
 
@@ -542,7 +543,7 @@ 
                                                                                          Examp
 # EXAMPLE 3: Rasch model and mixture Rasch model (Geiser & Eid, 2010)
 #############################################################################
 
-data(data.geiser, package="TAM")
+data(data.geiser, package="TAM")
 dat <- data.geiser
 
 #*********************************************************************
@@ -554,8 +555,8 @@ 
                                                                                          Examp
 ITEM TYPE:
   ALL(Rasch);
     "
-mod1 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod1)
+mod1 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod1)
 
 #*********************************************************************
 #*** Model 2: Mixed Rasch model with two classes
@@ -570,19 +571,19 @@ 
                                                                                          Examp
 ITEM TYPE:
   ALL(Rasch);
     "
-mod2 <- TAM::tamaan( tammodel, resp=dat  )
-summary(mod2)
+mod2 <- TAM::tamaan( tammodel, resp=dat  )
+summary(mod2)
 
 # plot item parameters
 ipars <- mod2$itempartable_MIXTURE[ 1:6, ]
-plot( 1:6, ipars[,3], type="o", ylim=c(-3,2), pch=16,
+plot( 1:6, ipars[,3], type="o", ylim=c(-3,2), pch=16,
         xlab="Item", ylab="Item difficulty")
-lines( 1:6, ipars[,4], type="l", col=2, lty=2)
-points( 1:6, ipars[,4],  col=2, pch=2)
+lines( 1:6, ipars[,4], type="l", col=2, lty=2)
+points( 1:6, ipars[,4],  col=2, pch=2)
 
 # extract individual posterior distribution
-post2 <- IRT.posterior(mod2)
-str(post2)
+post2 <- IRT.posterior(mod2)
+str(post2)
 # num [1:519, 1:30] 0.000105 0.000105 0.000105 0.000105 0.000105 ...
 # - attr(*, "theta")=num [1:30, 1:30] 1 0 0 0 0 0 0 0 0 0 ...
 # - attr(*, "prob.theta")=num [1:30, 1] 1.21e-05 2.20e-04 2.29e-03 1.37e-02 4.68e-02 ...
@@ -594,39 +595,40 @@ 
                                                                                          Examp
 mod2$E[1,2,,"mrt1_F_load_Cl2"]
 
 # compute individual posterior probability for class 1 (first 15 columns)
-round( rowSums( post2[, 1:15] ), 3 )
+round( rowSums( post2[, 1:15] ), 3 )
 # columns 16 to 30 refer to class 2
 
-# }
                                                                                          
+}
                                                                                          
   
   
 
 
+
       
                                                                                          
       
                                                                                          
   
                                                                                          Developed by Alexander Robitzsch.
                                                                                          
 
                                                                                          
 
 
                                                                                          
-  
                                                                                          Site built with pkgdown 1.3.0.
                                                                                          
+  
                                                                                          Site built with pkgdown 1.4.1.
                                                                                          
 
                                                                                          
+
       
                                                                                          
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tamaanify.html b/docs/reference/tamaanify.html
index fea0d85..01cd5ca 100644
--- a/docs/reference/tamaanify.html
+++ b/docs/reference/tamaanify.html
@@ -8,21 +8,25 @@
 
 Function for Parsing <span class="pkg">TAM</span> Input — tamaanify • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-TAM Input — tamaanify" />
 
+TAM Input — tamaanify" />
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
                                                                                            
         
                                                                                          • 
   
-    
+    
      
   
 
                                                                                            • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
                                                                                            
@@ -108,16 +114,14 @@ 
                                                                                            Function for Parsing TAM Input
                                                                                            
     
                                                                                            
 
     
                                                                                            
-    
     
                                                                                            This function parses a so called tammodel which is a
 string used for model estimation in TAM.
 The function is based on the lavaan syntax and operates
 at the extension lavaanify.IRT.
                                                                                            
-    
     
                                                                                            
 
     
                                                                                            tamaanify(tammodel, resp, tam.method=NULL, doparse=TRUE )
                                                                                            
-    
+
     
                                                                                            Arguments
                                                                                            
     
@@ -141,7 +145,7 @@ 
                                                                                            Arg
 should be parsed for DO statements.
                                                                                            
     
     
                                                                                            
     
                                                                                            
-    
+
     
                                                                                            Details
                                                                                            
 
     
                                                                                            The model syntax tammodel consists of several sections.
@@ -175,13 +179,16 @@ 
                                                                                            Details
 Fourier functions with decreasing periods.
 See Example 2, Model 7 of the tamaan
 function. 
                                                                                            
-    
                                                                                            PRIOR: 
                                                                                            
+
+
+
                                                                                            PRIOR: 
                                                                                            
 Possible prior distributions: Normal distribution N(mu,sd),
 truncated normal distribution TN(mu,sd,low,upp) and
-Beta distribution Beta(a,b).
+Beta distribution Beta(a,b).
 Parameter labels and prior specification must be separated
 by ~.
                                                                                            
-    
+
+
     
                                                                                            Value
                                                                                            
 
     
                                                                                            A list with following (optional) entries
@@ -224,7 +231,6 @@ 
                                                                                            Value
                                                                                            
 
                                                                                            xsi.fixed
                                                                                            Fixed \(\xi\) parameter
                                                                                            
 
                                                                                            xsi.prior
                                                                                            Prior distributions for \(\xi\) parameters
                                                                                            
 
-    
     
                                                                                            References
                                                                                            
 
     
                                                                                            Bartolucci, F. (2007). A class of multidimensional IRT models for testing
@@ -246,22 +252,19 @@ 
                                                                                            R
 DNC Research Note 07-12.
                                                                                            
 
                                                                                            von Davier, M. (2007). Mixture distribution diagnostic models.
 ETS Research Report ETS RR-07-32. Princeton, ETS.
                                                                                            
-    
     
                                                                                            See also
                                                                                            
 
     
                                                                                            See tamaan for more examples. Other examples
 are included in tam.mml and tam.mml.3pl.
                                                                                            
 
                                                                                            lavaanify.IRT
                                                                                            
-    
 
     
                                                                                            Examples
                                                                                            
-    
                                                                                            # NOT RUN {
-#############################################################################
+    
                                                                                            #############################################################################
 # EXAMPLE 1: Examples dichotomous data data.read
 #############################################################################
 
-library(sirt)
-data(data.read,package="sirt")
+library(sirt)
+data(data.read,package="sirt")
 dat <- data.read
 
 #*********************************************************************
@@ -286,7 +289,7 @@ 
                                                                                            Examp
   B1__C4 (Rasch) ;
     "
 # process model
-out <- TAM::tamaanify( tammodel, resp=dat)
+out <- TAM::tamaanify( tammodel, resp=dat)
 # inspect some output
 out$method          # used TAM function
 out$lavpartable     # lavaan parameter table
@@ -302,10 +305,10 @@ 
                                                                                            Examp
   F=~ A1__C4
     "
 # process syntax
-out <- TAM::tamaanify( tammodel, resp=dat)
-str(out$E)     # E design matrix for estimation with tam.mml.3pl function
+out <- TAM::tamaanify( tammodel, resp=dat)
+str(out$E)     # E design matrix for estimation with tam.mml.3pl function
 
-# }# NOT RUN {
+if (FALSE) {
 #*********************************************************************
 #*** Model 3: Linear constraints for item intercepts and item loadings
 tammodel <- "
@@ -335,16 +338,17 @@ 
                                                                                            Examp
   lam9==lam0
     "
 # parse syntax
-mod1 <- TAM::tamaanify( tammodel, resp=dat)
+mod1 <- TAM::tamaanify( tammodel, resp=dat)
 mod1$A          # design matrix A for intercepts
 mod1$L[,1,]     # design matrix L for loadings
-# }# NOT RUN {
+}
+
 #############################################################################
 # EXAMPLE 2: Examples polytomous data data.Students
 #############################################################################
 
-library(CDM)
-data( data.Students, package="CDM")
+library(CDM)
+data( data.Students, package="CDM")
 dat <- data.Students[,3:13]
 
 #*********************************************************************
@@ -359,7 +363,7 @@ 
                                                                                            Examp
   act1__act3 | t1
   sc2 | t2
     "
-out <- TAM::tamaanify( tammodel, resp=dat)
+out <- TAM::tamaanify( tammodel, resp=dat)
 out$A    # design matrix for item intercepts
 out$Q    # loading matrix for items
 
@@ -382,43 +386,41 @@ 
                                                                                            Examp
                 + (-0.2)*z1
   "
 # tamaanify model
-res <- TAM::tamaanify( tammodel, dat )
+res <- TAM::tamaanify( tammodel, dat )
 res$MODELCONSTRAINT.dfr
-res$modelconstraint.loading
-# }
                                                                                            
+res$modelconstraint.loading
                                                                                            
   
   
 
 
+
       
                                                                                            
       
                                                                                            
   
                                                                                            Developed by Alexander Robitzsch.
                                                                                            
 
                                                                                            
 
 
                                                                                            
-  
                                                                                            Site built with pkgdown 1.3.0.
                                                                                            
+  
                                                                                            Site built with pkgdown 1.4.1.
                                                                                            
 
                                                                                            
+
       
                                                                                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/tampv2datalist.html b/docs/reference/tampv2datalist.html
index b39c273..1ee3ee8 100644
--- a/docs/reference/tampv2datalist.html
+++ b/docs/reference/tampv2datalist.html
@@ -8,21 +8,25 @@
 
 Conversion of Plausible Value Object into Datalist — tampv2datalist • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
                                                                                              
         
                                                                                            • 
   
-    
+    
      
   
 
                                                                                              • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
                                                                                              
@@ -108,17 +114,15 @@ 
                                                                                              Conversion of Plausible Value Object into Datalist
                                                                                              
     
                                                                                              
 
     
                                                                                              
-    
     
                                                                                              Converts a tam.pv object and a matrix of covariates
 into a list of multiply imputed datasets. This list can be conveniently
 analyzed by R packages such as semTools, Zelig, mice
 or BIFIEsurvey.
                                                                                              
-    
     
                                                                                              
 
     
                                                                                              tampv2datalist(tam.pv.object, pvnames=NULL, Y=NULL, Y.pid="pid",
       as_mids=FALSE, stringsAsFactors=FALSE)
                                                                                              
-    
+
     
                                                                                              Arguments
                                                                                              
     
@@ -145,7 +149,7 @@ 
                                                                                              Arg
       
                                                                                              
+  the function miceadds::datalist2mids.
                                                                                              
@@ -153,43 +157,43 @@ 
                                                                                              Arg
 should be converted into factors
                                                                                              
     
                                                                                              
     
                                                                                              Logical indicating whether the datalist
   should be converted into an object of class mids for
   analysis in the mice package. This functionality uses the
-  the function miceadds::datalist2mids.
                                                                                              		
     
                                                                                              
     
                                                                                              
       stringsAsFactors
                                                                                              
     
                                                                                              
-    
+
     
                                                                                              Value
                                                                                              
 
     
                                                                                              List of multiply imputed datasets or an mids object
                                                                                              
-    
     
                                                                                              See also
                                                                                              
 
     
                                                                                              For examples see tam.pv.
                                                                                              
-    
 
   
   
                                                                                            
 
   
 
 
+
       
                                                                                            
       
                                                                                            
   
                                                                                            Developed by Alexander Robitzsch.
                                                                                            
 
                                                                                            
 
 
                                                                                            
-  
                                                                                            Site built with pkgdown 1.3.0.
                                                                                            
+  
                                                                                            Site built with pkgdown 1.4.1.
                                                                                            
 
                                                                                            
+
       
                                                                                            
    
 
   
 
+
   
 
 
+
diff --git a/docs/reference/weighted_Stats.html b/docs/reference/weighted_Stats.html
index 7ebb1f6..7cfb792 100644
--- a/docs/reference/weighted_Stats.html
+++ b/docs/reference/weighted_Stats.html
@@ -8,21 +8,25 @@
 
 Descriptive Statistics for Weighted Data — weighted_Stats • TAM
 
+
 
 
 
 
 
+
 
 
 
-
+
+
 
 
 
 
-
-
+
+
+
 
 
 
@@ -30,8 +34,8 @@
 
 
 
-
 
+
 
 
@@ -50,6 +55,7 @@
 
 
 
+
   
 
   
@@ -74,7 +80,7 @@
       
-      
       
                                                                                              
         
                                                                                            • 
   
-    
+    
      
   
 
                                                                                              • 
@@ -97,6 +102,7 @@
 
 
       
+
       
 
 
                                                                                              
@@ -108,28 +114,26 @@ 
                                                                                              Descriptive Statistics for Weighted Data
                                                                                              
     
                                                                                              
 
     
                                                                                              
-    
     
                                                                                              Some descriptive statistics for weighted data: variance,
 standard deviation, means, skewness, excess kurtosis, quantiles and
 frequency tables. Missing values are automatically removed from
 the data.
                                                                                              
-    
     
                                                                                              
 
-    
                                                                                              weighted_mean(x, w=rep(1, length(x)), select=NULL  )
+    
                                                                                              weighted_mean(x, w=rep(1, length(x)), select=NULL  )
 
-weighted_var(x, w=rep(1, length(x)), method="unbiased", select=NULL )
+weighted_var(x, w=rep(1, length(x)), method="unbiased", select=NULL )
 
-weighted_sd(x, w=rep(1, length(x)), method="unbiased", select=NULL )
+weighted_sd(x, w=rep(1, length(x)), method="unbiased", select=NULL )
 
-weighted_skewness( x, w=rep(1,length(x)), select=NULL  )
+weighted_skewness( x, w=rep(1,length(x)), select=NULL  )
 
-weighted_kurtosis( x, w=rep(1,length(x)), select=NULL  )
+weighted_kurtosis( x, w=rep(1,length(x)), select=NULL  )
 
-weighted_quantile( x, w=rep(1,length(x)), probs=seq(0,1,.25), type=NULL, select=NULL )
+weighted_quantile( x, w=rep(1,length(x)), probs=seq(0,1,.25), type=NULL, select=NULL )
 
 weighted_table( x, w=NULL, props=FALSE )
                                                                                              
-    
+
     
                                                                                              Arguments
                                                                                              
     
@@ -149,7 +153,7 @@ 
                                                                                              Arg
     
                                                                                              
+stats::cov.wt
                                                                                              
@@ -159,9 +163,9 @@ 
                                                                                              Arg
       
                                                                                              
+is used (see Hmisc::wtd.quantile)).
                                                                                              
@@ -169,144 +173,51 @@ 
                                                                                              Arg
 frequencies should be calculated.
                                                                                              
     
                                                                                              
     
                                                                                              
       method
       
                                                                                              Computation method (can be "unbiased" or "ML")), see
-stats::cov.wt
                                                                                              		
     
                                                                                              
     
                                                                                              
       probstype
       
                                                                                              Quantile type. For unweighted data, quantile types 6 and
 7 can be used (see
-stats::quantile).
+stats::quantile).
 For weighted data, the quantile type "i/n"
-is used (see Hmisc::wtd.quantile)).
                                                                                              		
     
                                                                                              
     
                                                                                              
       props
                                                                                              
     
                                                                                              
-    
+
     
                                                                                              Value
                                                                                              
 
     
                                                                                              Numeric value
                                                                                              
-    
     
                                                                                              See also
                                                                                              
 
-    
                                                                                              See stats::weighted.mean for
+    
                                                                                              See stats::weighted.mean for
 computing a weighted mean.
                                                                                              
-
                                                                                              See stats::var for computing
+
                                                                                              See stats::var for computing
 unweighted variances.
                                                                                              
-
                                                                                              See stats::quantile and
-Hmisc::wtd.quantile) for quantiles.
                                                                                              
-    
+
                                                                                              See stats::quantile and
+Hmisc::wtd.quantile) for quantiles.
                                                                                              
 
     
                                                                                              Examples
                                                                                              
-    
                                                                                              # NOT RUN {
-#############################################################################
-# EXAMPLE 1: Toy example for weighted_var function
-#############################################################################
-
-set.seed(9897)
-# simulate data
-N <- 10
-x <- stats::rnorm(N)
-w <- stats::runif(N)
-
-#---- variance
-
-# use weighted_var
-weighted_var( x=x, w=w )
-# use cov.wt
-stats::cov.wt( data.frame(x=x), w=w )$cov[1,1]
-# }# NOT RUN {
-# use wtd.var from Hmisc package
-Hmisc::wtd.var(x=x, weights=w, normwt=TRUE, method="unbiased")
-
-#---- standard deviation
-weighted_sd( x=x, w=w )
-
-#---- mean
-weighted_mean( x=x, w=w )
-stats::weighted.mean( x=x, w=w )
-
-#---- weighted quantiles for unweighted data
-pvec <- c(.23, .53, .78, .99 )  # choose probabilities
-type <- 7
-
-# quantiles for unweighted data
-weighted_quantile( x, probs=pvec, type=type)
-quantile( x, probs=pvec, type=type)
-Hmisc::wtd.quantile(x,probs=pvec, type=type)
-
-# quantiles for weighted data
-pvec <- c(.23, .53, .78, .99 )  # probabilities
-weighted_quantile( x, w=w, probs=pvec)
-Hmisc::wtd.quantile(x, weights=w, probs=pvec)
-
-#--- weighted skewness and kurtosis
-weighted_skewness(x=x, w=w)
-weighted_kurtosis(x=x, w=w)
-
-#############################################################################
-# EXAMPLE 2: Descriptive statistics normally distributed data
-#############################################################################
-
-# simulate some normally distributed data
-set.seed(7768)
-x <- stats::rnorm( 10000, mean=1.7, sd=1.2)
-# some statistics
-weighted_mean(x=x)
-weighted_sd(x=x)
-weighted_skewness(x=x)
-weighted_kurtosis(x=x)
-
-#############################################################################
-# EXAMPLE 3: Frequency tables
-#############################################################################
-
-#*****
-# simulate data for weighted frequency tables
-y <- scan()
-   1 0  1 1   1 2   1 3    1 4
-   2 0  2 1   2 2   2 3    2 4
-
-y <- matrix( y, ncol=2, byrow=TRUE)
-# define probabilities
-set.seed(976)
-pr <- stats::runif(10)
-pr <- pr / sum(pr)
-# sample data
-N <- 300
-x <- y[ sample( 1:10, size=300, prob=pr, replace=TRUE ), ]
-w <- stats::runif( N, 0.5, 1.5 )
-
-# frequency table unweighted data
-weighted_table(x[,2] )
-table( x[,2] )
-
-# weighted data and proportions
-weighted_table(x[,2], w=w, props=TRUE)
-
-#*** contingency table
-table( x[,1], x[,2] )
-weighted_table( x )
-# table using weights
-weighted_table( x, w=w )
-# }
-
                                                                                              
+    
                                                                                              
   
   
 
 
+
       
                                                                                              
       
                                                                                              
   
                                                                                              Developed by Alexander Robitzsch.
                                                                                              
 
                                                                                              
 
 
                                                                                              
-  
                                                                                              Site built with pkgdown 1.3.0.
                                                                                              
+  
                                                                                              Site built with pkgdown 1.4.1.
                                                                                              
 
                                                                                              
+
       
                                                                                              
    
 
   
 
+
   
 
 
+
diff --git a/inst/NEWS b/inst/NEWS
index 40c8f80..3c7f47b 100644
--- a/inst/NEWS
+++ b/inst/NEWS
@@ -24,7 +24,7 @@ A. Robitzsch, T. Kiefer, & M. Wu
 mailto:
 -------
 Alexander Robitzsch
-robitzsch@leibniz-ipn.de
+robitzsch@ipn.uni-kiel.de>
 
 For reporting a bug, please always provide a reproducible R script 
 and (if necessary) a corresponding dataset.
@@ -42,7 +42,7 @@ http://www.edmeasurementsurveys.com/TAM/Tutorials/
 
 
 -------------------------------------------------------------
-VERSIONS TAM 3.4  |  2019-10-24  |  Last: TAM 3.4-5
+VERSIONS TAM 3.4  |  2020-02-14  |  Last: TAM 3.4-15
 -------------------------------------------------------------
 
 NOTE  * corrected output 'resp' in tam.jml() in case of missing
@@ -50,10 +50,17 @@ NOTE  * corrected output 'resp' in tam.jml() in case of missing
 FIXED * fixed incorrect number of estimated parameters for 
         multidimensional polytomous data in tam.fa() and
         tam.mml.2pl() (thanks to Kuan-Yu Jin)
+FIXED * fixed incorrect number of estimated parameters if
+        a fixed B matrix is used (thanks to Timo Gnmabs)
+ADDED * include output in plot.tam() function 
+        (requested by Anne Pinot de Moira)
+ADDED * included function tam_downcode()
+        (suggested by Marcus Kubsch)
 
 
 DATA  * included/modified datasets: ---
-EXAMP * included/modified examples: tam.fa (2)
+EXAMP * included/modified examples: tam.fa (2), tam.mml (6),
+        plot.tam (1), tam_downcode()
 
 
 
diff --git a/man/TAM-package.Rd b/man/TAM-package.Rd
index 7e281e0..1e9d072 100644
--- a/man/TAM-package.Rd
+++ b/man/TAM-package.Rd
@@ -1,5 +1,5 @@
 %% File Name: TAM-package.Rd
-%% File Version: 2.64
+%% File Version: 2.653
 
 \name{TAM-package}
 \alias{TAM-package}
@@ -31,23 +31,29 @@ tutorials of the \pkg{TAM} package.
 \references{
 Adams, R. J., Wilson, M., & Wang, W. C. (1997). The multidimensional random coefficients
 multinomial logit model. \emph{Applied Psychological Measurement, 21}(1), 1-23.
+\doi{10.1177/0146621697211001}
 
 Adams, R. J., Wilson, M., & Wu, M. (1997).
 Multilevel item response models: An approach to errors in
 variables regression. \emph{Journal of Educational and Behavioral
-Statistics, 22}, 47-76.
+Statistics, 22}(1), 47-76. \doi{10.3102/10769986022001047}
 
 Adams, R. J., & Wu, M. L. (2007). The mixed-coefficients multinomial logit model.
 A generalized form of the Rasch model. In M. von Davier & C. H. Carstensen (Eds.):
 \emph{Multivariate and mixture distribution Rasch models: Extensions and applications}
 (pp. 55-76). New York: Springer.
+\doi{10.1007/978-0-387-49839-3_4}
 
 Formann, A. K. (1982). Linear logistic latent class analysis.
 \emph{Biometrical Journal, 24}(2), 171-190.
+\doi{10.1002/bimj.4710240209}
 
 Formann, A. K. (1992). Linear logistic latent class analysis for polytomous data.
-\emph{Journal of the American Statistical Association, 87}, 476-486.
+\emph{Journal of the American Statistical Association, 87}(418), 476-486.
+\doi{10.1080/01621459.1992.10475229}
 }
+
+
 \keyword{package}
 
 
diff --git a/man/plot.tam.Rd b/man/plot.tam.Rd
index dc13efd..5ac30bc 100644
--- a/man/plot.tam.Rd
+++ b/man/plot.tam.Rd
@@ -1,5 +1,5 @@
 %% File Name: plot.tam.Rd
-%% File Version: 3.39
+%% File Version: 3.416
 
 \name{plot.tam}
 \alias{plot.tam}
@@ -20,17 +20,17 @@ or \code{tam.mml}.
 \method{plot}{tam}(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
                    groups_by_item=FALSE, wle=NULL, export=TRUE, export.type="png",
                    export.args=list(), observed=TRUE, overlay=FALSE,
-                   ask=FALSE, package="lattice", fix.devices=TRUE, ...)
+                   ask=FALSE, package="lattice", fix.devices=TRUE, nnodes=100, ...)
 
 \method{plot}{tam.mml}(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
                        groups_by_item=FALSE, wle=NULL, export=TRUE, export.type="png",
                        export.args=list(), observed=TRUE, overlay=FALSE,
-                       ask=FALSE,  package="lattice",  fix.devices=TRUE, ...)
+                       ask=FALSE,  package="lattice",  fix.devices=TRUE, nnodes=100, ...)
 
 \method{plot}{tam.jml}(x, items=1:x$nitems, type="expected", low=-3, high=3, ngroups=6,
                        groups_by_item=FALSE, wle=NULL, export=TRUE, export.type="png",
                        export.args=list(), observed=TRUE, overlay=FALSE,
-                       ask=FALSE,  package="lattice", fix.devices=TRUE, ...)
+                       ask=FALSE,  package="lattice", fix.devices=TRUE, nnodes=100, ...)
 }
 %- maybe also 'usage' for other objects documented here.
 
@@ -91,6 +91,8 @@ of the working directory.
 \code{"graphics"}.}
 \item{fix.devices}{Optional logical indicating whether old graphics devices should
      be saved.}
+\item{nnodes}{Number of \eqn{\theta} points at which item response functions
+are evaluated}
 \item{\dots}{
   Further arguments to be passed
 }
@@ -101,13 +103,10 @@ This plot method does not work for multidimensional item response
 models.
 }
 
-%\value{
-%%  ~Describe the value returned
-%%  If it is a LIST, use
-%%  \item{comp1 }{Description of 'comp1'}
-%%  \item{comp2 }{Description of 'comp2'}
-%% ...
-%}
+\value{
+A plot and list of computed values for \code{plot} (if saved as an object)
+}
+
 %\references{
 %% ~put references to the literature/web site here ~
 %}
@@ -136,6 +135,8 @@ data(data.sim.rasch)
 mod <- TAM::tam.mml(data.sim.rasch)
 # expected response curves
 plot(mod, items=1:5, export=FALSE)
+# export computed values
+out <- plot(mod, items=1:5, export=FALSE)
 # item response curves
 plot(mod, items=1:5, type="items", export=FALSE)
 # plot with graphics package
diff --git a/man/tam.mml.3pl.Rd b/man/tam.mml.3pl.Rd
index 592fe5f..c3926ea 100644
--- a/man/tam.mml.3pl.Rd
+++ b/man/tam.mml.3pl.Rd
@@ -1,5 +1,5 @@
 %% File Name: tam.mml.3pl.Rd
-%% File Version: 2.683
+%% File Version: 2.684
 
 \name{tam.mml.3pl}
 \alias{tam.mml.3pl}
@@ -180,7 +180,7 @@ Fixed or initial guessing parameters
 }
   \item{guess.prior}{
      Item-specific prior distribution for guessing parameters \eqn{c_i}. It is
-     assumed that \eqn{c_i \sim N( a_i, b_i)}. The first column
+     assumed that \eqn{c_i \sim Beta(a_i, b_i)}. The first column
      in \code{gammaslope.prior} is \eqn{a_i}, the second is \eqn{b_i}.
 }
 \item{max.guess}{Upper bound for guessing parameters}
diff --git a/man/tam.mml.Rd b/man/tam.mml.Rd
index c59e041..f33bb9f 100644
--- a/man/tam.mml.Rd
+++ b/man/tam.mml.Rd
@@ -1,5 +1,5 @@
 %% File Name: tam.mml.Rd
-%% File Version: 2.9861
+%% File Version: 2.9862
 
 \name{tam.mml}
 
@@ -1037,7 +1037,8 @@ mod6_1$B    # for every category a separate slope parameter is estimated
 
 # reestimate the model with fixed item parameters
 mod6_1a <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL",
-       xsi.fixed=mod6_1$xsi.fixed.estimated,  B.fixed=mod6_1$B.fixed.estimated )
+       xsi.fixed=mod6_1$xsi.fixed.estimated,  B.fixed=mod6_1$B.fixed.estimated,
+       est.variance=TRUE )
 
 # estimate the model with initial item parameters from mod6_1
 mod6_1b <- TAM::tam.mml.2pl( resp=data.gpcm, irtmodel="2PL",
diff --git a/man/tam_downcode.Rd b/man/tam_downcode.Rd
new file mode 100644
index 0000000..77d36ec
--- /dev/null
+++ b/man/tam_downcode.Rd
@@ -0,0 +1,78 @@
+%% File Name: tam_downcode.Rd
+%% File Version: 0.04
+
+\name{tam_downcode}
+\alias{tam_downcode}
+%- Also NEED an '\alias' for EACH other topic documented here.
+
+\title{
+Downcoding an Item Response Dataset
+}
+
+\description{
+Recodes item categories in a data frame such that each item has values
+\eqn{0,1,\ldots,K_i}.
+}
+
+\usage{
+tam_downcode(dat)
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+\item{dat}{
+Data frame containing item responses
+}
+}
+
+%\details{
+%%  ~~ If necessary, more details than the description above ~~
+%}
+
+
+\value{
+List with following entries
+\item{dat}{Recoded dataset}
+\item{rec}{Recoding table}
+}
+
+
+%\references{
+%% ~put references to the literature/web site here ~
+%}
+%\author{
+%%  ~~who you are~~
+%}
+%\note{
+%%  ~~further notes~~
+%}
+
+%% ~Make other sections like Warning with \section{Warning }{....} ~
+
+
+
+\examples{
+#############################################################################
+# EXAMPLE 1: Downcoding in a toy example
+#############################################################################
+
+#-- simulate some toy data
+set.seed(989)
+# values to be sampled
+vals <- c(NA, 0:6)
+# number of persons and items
+N <- 10; I <- 5
+dat <- as.data.frame(matrix(NA, nrow=N, ncol=I))
+colnames(dat) <- paste0("I",1:I)
+for (ii in 1L:I){
+    dat[,ii] <- sample(vals, size=N, replace=TRUE)
+}
+
+#-- apply downcoding
+res <- TAM::tam_downcode(dat)
+dat <- res$dat   # extract downcoded dataset
+rec <- res$rec   # extract recoded table
+}
+% Add one or more standard keywords, see file 'KEYWORDS' in the
+% R documentation directory.
+%% \keyword{Confirmatory factor analysis}
+%% \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index ac3cf6d..d1abb60 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -1,10 +1,11 @@
+//// File Name: RcppExports.cpp
+//// File Version: 3.004015
 // Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include 
-#include 
 
-using namespace Rcpp;
+using namespace Rcpp; using namespace arma;
 
 // tam_rcpp_calc_exp
 Rcpp::List tam_rcpp_calc_exp(int NP, Rcpp::NumericVector rprobs, Rcpp::NumericVector A, Rcpp::NumericMatrix INDEXIPNO, Rcpp::NumericVector INDEXIPLIST2, Rcpp::NumericVector ESTXSIINDEX, int C, Rcpp::NumericMatrix ITEMWT, int NR, int TP);
@@ -583,3 +584,4 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
+
diff --git a/src/init.c b/src/init.c
index 17d3f3e..853ac4f 100644
--- a/src/init.c
+++ b/src/init.c
@@ -1,5 +1,5 @@
 //// File Name: init.c
-//// File Version: 3.004005
+//// File Version: 3.004015
 #include 
 #include 
 #include  // for NULL