Merge branch 'hotfix-outofbounds-b'

.Rbuildignore

@@ -7,4 +7,5 @@
 ^cran-comments\.md$
 ^desktop\.ini$
 gendat3*
-.vscode
+.vscode
+doc

.gitignore

@@ -65,4 +65,5 @@ Thumbs.db
 desktop.ini
 
 # Test files
-gendat3*
+gendat3*
+doc

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lsasim
 Title: Functions to Facilitate the Simulation of Large Scale Assessment Data
-Version: 2.0.1
+Version: 2.0.2
 Authors@R: c(
   person("Tyler", "Matta",
          email = "[email protected]", role = "aut"),
@@ -13,9 +13,9 @@ Authors@R: c(
   person("Kondwani Kajera", "Mughogho",
          email = "[email protected]", role = "ctb"),
   person("Waldir", "Leoncio",
-         email = "[email protected]", role = c("aut", "cre"))
+         email = "[email protected]", role = c("aut", "cre"))
   )
-Maintainer: Waldir Leoncio <[email protected]>
+Maintainer: Waldir Leoncio <[email protected]>
 BugReports: https://github.com/tmatta/lsasim/issues
 Description: Provides functions to simulate data from large-scale educational
   assessments, including background questionnaire data and cognitive item
@@ -26,5 +26,5 @@ Depends:
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.1
 Suggests: testthat, polycor

R/item_gen.R

@@ -130,44 +130,44 @@ item_gen <- function(b_bounds, a_bounds = NULL, c_bounds = NULL,
 
   }
 
-  b_mean <- mean(unlist(b_par))
-  b_center <- lapply(b_par, function(x) round(x - b_mean, 2))
-
-  #-- b_star is the average difficulty for each item
-  #-- For dicotomous items, b_star = b_center
-  b_star <- lapply(b_center, function(x) round(mean(x), 2))
-  b_par <- do.call("rbind", b_star)
-
-  d <- list()
-  for (p in 1:i) {
-    d_i <- list()
-
-    if (k[p] != 1) {
+  if (sum(thresholds) > 1) {
 
-      d_i[[p]] <- rep(0, max(k))
-      for(j in 1:k[p]) d_i[[p]][j] <- b_center[[p]][j] - b_star[[p]]
-
-    } else {
+    b_star <- lapply(b_par, mean)
 
-      d_i[[p]] <- rep(0, max(k))
+    d_i <- list()
 
-    }
+    for (pp in 1:i) {
 
-    d[[p]] <- unlist(d_i)
+      if (k[pp] != 1) {
 
-  }
+        d_i[[pp]] <- b_par[[pp]] - b_star[[pp]]
+
+      } else {
+
+        d_i[[pp]] <- rep(0, max(k))
+
+      }
+
+    }  
+
+    d_par <- round(do.call("rbind", d_i), 2)
+
+    dlabs <- paste0("d", 1:ncol(d_par))
 
-  d_par <- do.call("rbind", d)
-
-  dlabs <- paste0("d", 1:ncol(d_par))
+    b_star <- round(do.call("rbind", b_star), 2)
 
-  if (sum(thresholds) > 1){
-    item_parameters <- data.frame(item = item_no, b = b_par, d = d_par, a = a_par, c = c_par, k = k, p = item_type)
+    item_parameters <- data.frame(item = item_no, b = b_star, d = d_par, a = a_par, c = c_par, k = k, p = item_type)
     colnames(item_parameters) <- c("item", "b", dlabs, "a", "c", "k", "p")
+
   }
-  if (sum(thresholds) == 1){
+
+  if (sum(thresholds) == 1) {
+
+    b_par <- round(do.call("rbind", b_par), 2)
+
     item_parameters <- data.frame(item = item_no, b = b_par, a = a_par, c = c_par, k = k, p = item_type)
     colnames(item_parameters) <- c("item", "b", "a", "c", "k", "p")
+
   }
 
 

R/lsasim.R

@@ -1,4 +1,5 @@
-#' lsasim: A package for simulating large scale assessment data
+#' @title lsasim: A package for simulating large scale assessment data
+#' @description lsasim simulates data that mimics large-scale assessments (LSAs), including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design
 #'
 #' @section Core functions:
 #' \itemize{

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.org/tmatta/lsasim.svg?branch=master)](https://travis-ci.org/tmatta/lsasim)
+![Current CRAN release](https://www.r-pkg.org/badges/version/lsasim) [![Build Status](https://travis-ci.org/tmatta/lsasim.svg?branch=master)](https://travis-ci.org/tmatta/lsasim)
 
 # README