23.2.9.1

Fix CRAN issues

.Rbuildignore

@@ -12,3 +12,4 @@
 ^docs$
 ^pkgdown$
 ^cran-comments\.md$
+^CRAN-SUBMISSION$

DESCRIPTION

@@ -10,12 +10,12 @@ Authors@R: c(
   )
 Description: A toolbox for constructing potential landscapes for Ising
     networks. The parameters of the networks can be directly supplied by
-    users or estimated by the `IsingFit` package
-    <https://CRAN.R-project.org/package=IsingFit> from empirical data. The
-    Ising model's Boltzmann distribution is preserved for the potential
-    landscape function. The landscape functions can be used for
-    quantifying and visualizing the stability of network states, as well
-    as visualizing the simulation process.
+    users or estimated by the 'IsingFit' package by van Borkulo and
+    Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from
+    empirical data. The Ising model's Boltzmann distribution is preserved
+    for the potential landscape function. The landscape functions can be
+    used for quantifying and visualizing the stability of network states,
+    as well as visualizing the simulation process.
 License: GPL (>= 3)
 URL: https://sciurus365.github.io/Isinglandr/,
     https://github.com/Sciurus365/Isinglandr

R/Ising_grid.R

@@ -142,6 +142,8 @@ generator_control_seq <- function(type) {
 #' and the original threshold value(s) or the original weight(ed
 #' adjacency matrix)? `*` by default, which means the values supplied
 #' in `seq` will be multiplied to the original value, vector, or matrix.
+#'
+#' @return An `ctrl_*` object specifying the varying parameters.
 #' @name make_Ising_grid-control-functions
 NULL
 

R/imported_generic_function.R

@@ -1,19 +1,7 @@
-#' Autolayer generic function
-#'
-#' See [ggplot2::autolayer()], [autolayer.resilience()] for details.
-#'
-#' @name autolayer
-#' @keywords internal
 #' @export
 #' @importFrom ggplot2 autolayer
-NULL
+ggplot2::autolayer
 
-#' Barrier calculation function
-#'
-#' See [calculate_barrier.Isingland()] for details.
-#'
-#' @name calculate_barrier
-#' @keywords internal
 #' @export
 #' @importFrom simlandr calculate_barrier
-NULL
+simlandr::calculate_barrier

R/landscape.R

@@ -106,7 +106,17 @@ make_2d_Isingland <- function(thresholds, weiadj, beta = 1, transform = FALSE) {
 #'   distribution and the potential values for different states.
 #'   \item `Nvar` The number of variables (nodes) in the Ising network.
 #' }
-#'
+#' @examples
+#' Nvar <- 10
+#' m <- rep(0, Nvar)
+#' w <- matrix(0.1, Nvar, Nvar)
+#' diag(w) <- 0
+#' result4 <- make_Ising_grid(
+#' all_thresholds(seq(-0.1, 0.1, 0.1), .f = `+`),
+#' whole_weiadj(seq(0.5, 1.5, 0.5)),
+#' m, w
+#' ) %>% make_2d_Isingland_matrix()
+#' plot(result4)
 #' @export
 make_2d_Isingland_matrix <- function(Ising_grid, transform = FALSE) {
   dist_raw <- Ising_grid %>%
@@ -184,6 +194,14 @@ plot.2d_Isingland_matrix <- function(x, ...) {
 #'
 #' @seealso [make_2d_Isingland()] for the algorithm.
 #'
+#' @return A `3d_Isingland` object that contains the following components:
+#' \itemize{
+#'   \item `dist_raw`,`dist` Two tibbles containing the probability
+#'   distribution and the potential values for different states.
+#'   \item `thresholds`,`weiadj`,`beta` The parameters supplied to the function.
+#'   \item `Nvar` The number of variables (nodes) in the Ising network.
+#' }
+#'
 #' @export
 make_3d_Isingland <- function(thresholds, weiadj, x, y, beta = 1, transform = FALSE) {
   l_2d <- make_2d_Isingland(thresholds, weiadj, beta, transform)

R/resilience.R

@@ -87,6 +87,8 @@ calculate_resilience.2d_Isingland <- function(l, split_value = 0.5*l$Nvar, ...)
 #' @param ... Not in use.
 #'
 #' @export
+#'
+#' @inherit ggplot2::autolayer return
 #' @name autolayer.resilience
 autolayer.resilience_2d_Isingland <- function(object, point = TRUE, line = TRUE, split_value = TRUE, interval = TRUE, resilience_value = TRUE, ...) {
   result <- list()

R/shiny.R

@@ -106,10 +106,11 @@ shiny_ui <- function() {
 
 #' A shiny app that shows the landscape for
 #' the Ising network of major depressive disorder
+#' @param ... Not in use.
 #'
 #' @export
-#'
-shiny_Isingland_MDD <- function() {
+#' @return This function opens a Shiny app session without a return value.
+shiny_Isingland_MDD <- function(...) {
   app <- shiny::shinyApp(shiny_ui, shiny_server)
   shiny::runApp(app)
 }

R/simulation.R

@@ -49,6 +49,18 @@ U <- function(l, x) {
 #'  \item `landscape` The landscape object supplied to this function.
 #'  \item `mode` A character representing the mode of the simulation.
 #' }
+#'
+#' @examplesIf interactive()
+#' Nvar <- 10
+#' m <- rep(0, Nvar)
+#' w <- matrix(0.1, Nvar, Nvar)
+#' diag(w) <- 0
+#' result1 <- make_2d_Isingland(m, w)
+#' plot(result1)
+#'
+#' set.seed(1614)
+#' sim1 <- simulate_Isingland(result1, initial = 5)
+#' plot(sim1)
 #' @export
 simulate_Isingland <- function(l, ...) {
   UseMethod("simulate_Isingland", l)

README.Rmd

@@ -22,7 +22,14 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/Sciurus365/Isinglandr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Sciurus365/Isinglandr/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-A toolbox for constructing potential landscapes for Ising networks. The parameters of the networks can be directly supplied by users or estimated by the `IsingFit` package <https://CRAN.R-project.org/package=IsingFit> from empirical data. The Ising model's Boltzmann distribution is preserved for the potential landscape function. The landscape functions can be used for quantifying and visualizing the stability of network states, as well as visualizing the simulation process.
+A toolbox for constructing potential landscapes for Ising
+    networks. The parameters of the networks can be directly supplied by
+    users or estimated by the `IsingFit` package by van Borkulo and
+    Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from
+    empirical data. The Ising model's Boltzmann distribution is preserved
+    for the potential landscape function. The landscape functions can be
+    used for quantifying and visualizing the stability of network states,
+    as well as visualizing the simulation process.
 
 ## Installation
 

README.md

@@ -14,7 +14,7 @@ status](https://www.r-pkg.org/badges/version/Isinglandr)](https://CRAN.R-project
 
 A toolbox for constructing potential landscapes for Ising networks. The
 parameters of the networks can be directly supplied by users or
-estimated by the `IsingFit` package
+estimated by the `IsingFit` package by van Borkulo and Epskamp (2016)
 <https://CRAN.R-project.org/package=IsingFit> from empirical data. The
 Ising model’s Boltzmann distribution is preserved for the potential
 landscape function. The landscape functions can be used for quantifying

cran-comments.md

@@ -1,3 +1,9 @@
+- This is a re-submission, in which the following issues were solved:
+	- Used only undirected quotation marks in the description text.
+	- Added reference to the 'IsingFit' package.
+	- Added \value and \arguments fields to some functions.
+	- Added several examples.
+
 ## Test environments
 
 - local R installation in Windows R 4.2.2