From 610fad440afd2a18a5c9afc100545fa40d293918 Mon Sep 17 00:00:00 2001 From: Maximilian Muecke Date: Thu, 27 Mar 2025 22:19:11 +0100 Subject: [PATCH] chore: remove old NEWS.Rd --- inst/NEWS.Rd | 337 --------------------------------------------------- 1 file changed, 337 deletions(-) delete mode 100644 inst/NEWS.Rd diff --git a/inst/NEWS.Rd b/inst/NEWS.Rd deleted file mode 100644 index 9727bcf..0000000 --- a/inst/NEWS.Rd +++ /dev/null @@ -1,337 +0,0 @@ -\name{NEWS} -\title{News for Package 'FDboost'} - -\section{Changes in FDboost version 1.1-0 (2022-07-12)}{ - \subsection{Miscellaneous}{ - \itemize{ - \item Anisotropic tensor-product operators \code{b1 \%A0\% b2} and \code{b1 \%Xa0\% b2} now - also working when \code{lambda} is specified for \code{b1} and \code{df} is specified for \code{b2} - (or vice versa). - } - } - \subsection{New feature}{ - \itemize{ - \item New function \code{clr} to compute the centered-log-ratio transform and its - inverse for density-on-scalar regression in Bayes spaces. - \item New dataset \code{birthDistribution}. - \item New vignette illustrating density-on-function regression on - the \code{birthDistribution} data. - \item Function \code{factorize} added for tensor-product factorization of - estimated effects or models. - } - } -} - -\section{Changes in FDboost version 0.3-4 (2020-08-31)}{ - \subsection{Bug-fixes}{ - \itemize{ - \item Fix predict() for bsignal with newdata and the functional covariate - given as numeric matrix, raised in - \href{https://github.com/boost-R/FDboost/issues/17}{#17} - \item Deprecated argument \code{LINPACK} in \code{solve} removed. - } - } -} - -\section{Changes in FDboost version 0.3-3 (2020-06-13)}{ -\subsection{New feature}{ -\itemize{ - \item Now it is possible to specify several time variabels as well as - factor time variabels in the timeformula. - This feature is needed for the manifoldboost package. - } -} -\subsection{Miscellaneous}{ -\itemize{ - \item The function stabsel.FDboost() now uses applyFolds() instead of validateFDboost() to do - cross-validation with recomputation of the smooth offset. This is only relevant for models with functional response. - This will change the results if the model contains base-learners like bbsc() or bolsc(), - as applyFolds() also recomputes the Z-matrix for those base-learners. - } -} - \subsection{Bug-fixes}{ - \itemize{ - \item Adapted functions \code{integrationWeights} and \code{integrationWeightsLeft} for unsorted time variables. - \item Change code in predict.FDboost() such that interaction effects of two functional - covariates such as \code{bsignal() \%X\% bsignal()} can be predicted with new data. - \item Adapt FDboost to R 4.0.1: explicitely use the first entry of dots$aggregate, - by setting dots$aggregate[1] != "sum", in predict.FDboost(); such that it also works with the default, - where aggregate is a vector of length 3 and later on the first argument is used, using match.arg() - } -} -} - -\section{Changes in FDboost version 0.3-2 (2018-08-04)}{ - \subsection{Bug-fixes}{ - \itemize{ - \item Deprecated argument \code{corrected} in \code{cvrisk} removed. - } - } -} - -\section{Changes in FDboost version 0.3-1 (2018-05-10)}{ - \subsection{Bug-fixes}{ - \itemize{ - \item \code{cvrisk} has per default adequate folds for a noncyclic fitted FDboostLSS model, - see issue \href{https://github.com/boost-R/FDboost/issues/14}{#14} - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item replace cBind which is deprecated with cbind - } - } -} - -\section{Changes in FDboost version 0.3-0 (2017-05-31)}{ - \subsection{User-visible changes}{ - \itemize{ - \item new function \code{bootstrapCI()} to compute bootstrapped coefficients - \item add the dataset 'emotion' containing EEG and EMG measures under different experimental conditions - \item with scalar response, \code{FDboost()} works with the response as - vector and not as matrix with one row; - thus, \code{fitted()} and \code{predict()} return a vector - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item \code{update.FDboost()} works now with scalar response - \item \code{FDboost()} works with family \code{Binomial(type = "glm")}, - see isssue \href{https://github.com/boost-R/FDboost/issues/1}{#1} - \item \code{applyFolds()} works for factor response, - see issue \href{https://github.com/boost-R/FDboost/issues/7}{#7} - \item \code{cvLong} and \code{cvMA} return a matrix for only one resampling - fold with \code{B = 1} (proposed by Almond Stoecker) - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item adapt \pkg{FDboost} to \pkg{mboost} 2.8-0 that allows for mstop = 0 - \item restructure FDboostLSS() such that it calls mboostLSS_fit() from \pkg{gamboostLSS} 2.0-0 - \item in \pkg{FDboost}, set \code{options("mboost_indexmin" = +Inf)} to disable the - internal use of ties in model fitting, as this breaks some methods for models with response - in long format and for models containing \code{bhistx}, - see issue \href{https://github.com/boost-R/FDboost/issues/10}{#10} - \item deprecate \code{validateFDboost()}, - use \code{applyFolds()} and \code{bootstrapCI()} instead - } - } -} - - -\section{Changes in FDboost version 0.2-0 (2016-05-26)}{ - \subsection{User-visible changes}{ - \itemize{ - \item add function applyFolds() to compute the optimal stopping iteration - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item allow for extrapolation in predict() with bbsc() - } - } -} - -\section{Changes in FDboost version 0.1-2 (2016-04-22)}{ - \subsection{Bug-fixes}{ - \itemize{ - \item bugfix in bolsc(): correctly use index in bolsc() / bbsc(), - before: for the computation of Z each observation was used only once - } - } - \subsection{User-visible changes}{ - \itemize{ - \item add function \%Xa0\% that computes a row-tensor product of two base-learners where - the penalty in one direction is zero - \item add function reweightData() that computes the data for Bootstrap or cross-falidation folds - \item add function stabsel.FDboost() that refits the smooth offset in each fold - \item add argument 'fun' to validateFDboost() - \item add update.FDboost() that overwrites update.mboost() - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item FDboost() works with family = Binomial() - } - } -} - - -\section{Changes in FDboost version 0.1-1 (2016-04-06)}{ - \subsection{Bug-fixes}{ - \itemize{ - \item fix oobpred in validateFDboost() for irregular response and resampling on the level of curves - and thus plot.validateFDboost() works for that case - \item fix scope of formula in FDboost(): now the formula given to mboost() within FDboost() uses the variables in the environment of the formula specified in FDboost() - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item plot.FDboost() works for more effects, especially for effects like bolsc() \%X\% bhistx() - } - } -} - - -\section{Changes in FDboost version 0.1-0 (2016-03-10)}{ - \subsection{User-visible changes}{ - \itemize{ - \item new operator \%A0\% for Kronecker product of two base-learners with - anisotropic penalty for the special case where lambda1 or lambda2 is zero - \item the base-learner bbsc() can be used with center = TRUE, derived by Almond Stoecker - \item in FDboostLSS() a list of one-sided formulas can be specified for timeformula - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item FDboostLSS works with families = GammaLSS() - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item operator \%A\% uses weights in model call; only works correctly for weights on level - of blg1 and blg2 (which is the same as weights on rows and columns of the response matrix) - \item call to internal functions of mboost is done using mboost_intern() - \item hyper_olsc() is based on hyper_ols() of mboost - } - } -} - -\section{Changes in FDboost version 0.0-17 (2016-02-25)}{ - \subsection{User-visible changes}{ - \itemize{ - \item changed the operator \%Xc\% for row tensor product of two scalar covariates. - The design matrix of the interaction effects is constrained such that the interaction is - centred around the intercept and around the two main effects of the scalar covariates (experimental!); - use e.g. as bols(x1) \%Xc\% bols(x2) - } - } -} - -\section{Changes in FDboost version 0.0-16 (2016-02-22)}{ - \subsection{User-visible changes}{ - \itemize{ - \item changed the operator \%Xc\% for row tensor product where the sum-to-zero constraint is applied to - the design matrix resulting from the row-tensor product (experimental!), - such that first a, intercept-column is added to the design-matrix and then the sum-to-zero constraint - is applied, use e.g. as bolsc(x1) \%Xc\% bolsc(x2) - \item use the functional index s as argsvals in the FPCA conducted within bfpc() - } - } -} - -\section{Changes in FDboost version 0.0-15 (2016-02-12)}{ - \subsection{User-visible changes}{ - \itemize{ - \item new operator \%A\% that implies anisotropic penalties for differently specified df in the two base-learners - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item do not penalize in direction of ONEx in smooth intercept specified implicitly by ~1, as bols(ONEx, intercept=FALSE, df=1) \%A\% bbs(time) - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item do not expand an effect that contains \%A\% or \%O\% with the timeformula, allowing for different effects over time for the - effects in the model - } - } -} - -\section{Changes in FDboost version 0.0-14 (2016-02-11)}{ - \subsection{User-visible changes}{ - \itemize{ - \item add the function FDboostLSS() to fit GAMLSS models with functional data - using R-package gamboostLSS - \item new operator \%Xc\% for row tensor product where the sum-to-zero constraint is applied to - the design matrix resulting from the row-tensor product (experimental!) - \item allow newdata to be a list in predict.FDboost() in combination with signal base-learners - \item expand coef.FDboost() such that it works for 3-dimensional tensor products - of with bhistx() the form bhistx() \%X\% bolsc() \%X\% bolsc() (with David Ruegamer) - \item add a new possibility for scalar-on-function regression: - for timeformula=NULL, no Kronecker-product with 1 is used, which - changes the penalty as otherwise in the direction of 1 is penalized as well. - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item new dependency on R-package gamboostLSS - \item remove dependency on R-package MASS - \item use the argument 'prediction' in the internal computation - of the base-learners (work in progress) - \item throw an error if 'timeLab' of the hmatrix-object in bhistx() is not - equal to the time-variable in 'timeformula'. - } - } -} - - -\section{Changes in FDboost version 0.0-13 (2015-11-17)}{ - \subsection{User-visible changes}{ - \itemize{ - \item in function FDboost() the offset is supplied differently, for a scalar offset, use offset = "scalar", the default is still the same offset=NULL - \item predict.FDboost() has new argument toFDboost (logical) - \item fitted.FDboost() has argument toFDboost explicitly and not only in ... - \item new base-learner bhistx() especially suited for effects with \%X\%, like bhistx \%X\% bolsc - \item coef.FDboost() and plot.FDboost() suited for effects like bhistx \%X\% bolsc - \item for predict.FDboost() with effects bhistx() and newdata the latest mboostPatch is necessary - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item check for necessity of smooth offset works for missings in regular response (spotted by Tore Erdmann) - } - } -} - -\section{Changes in FDboost version 0.0-12 (2015-09-15)}{ - \itemize{ - \item Internal experimental version. - } -} - -\section{Changes in FDboost version 0.0-11 (2015-06-01)}{ - \subsection{User-visible changes}{ - \itemize{ - \item integrationWeights() gives equal weights for regular grids - \item new base-learner bfpc() for a functional covariate where - functional covariate and the coeffcient are both expanded using fPCA (experimental feature!); - only works for regularly observed functional covariate. - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item the function coef.FDboost() only works for bhist() if the time variable is the same in the timeformula and in bhist() - \item predict.FDboost() has a check that for newdata only type="link" can be predicted - } - } -} - -\section{Changes in FDboost version 0.0-10 (2015-04-16)}{ - \subsection{User-visible changes}{ - \itemize{ - \item change the default in difference-penalties to first order difference penalty - differences=1, as then the effects are better identifiable - \item new method cvrisk.FDboost() that uses per default - sampling on the levels of curves, which is important for functional response - \item reorganize documentation of cvrisk() and validateFDboost() - \item in bhist(): effect can be standardized - } - } - \subsection{Miscellaneous}{ - \itemize{ - \item add a CITATION file - \item use mboost 2.4-2 as it exports all important functions - } - } - \subsection{Bug-fixes}{ - \itemize{ - \item main argument is always passed in plot.FDboost() - \item bhist() and bconcurrent() work for equal time and s - \item predict.FDboost() works with tensor-product base-learners bl1 \%X\% bl2 - } - } -} - -