CRAN 2.1

DESCRIPTION

@@ -2,12 +2,12 @@ Package: TruncatedNormal
 Type: Package
 Title: Truncated Multivariate Normal and Student Distributions
 Version: 2.1
-Date: 2019-08-28
+Date: 2019-08-30
 Authors@R: c(person(given="Zdravko", family="Botev", role = "aut", email = "botev@unsw.edu.au", comment = c(ORCID = "0000-0001-9054-3452")), person(given="Leo", family="Belzile", role = c("aut", "cre"), email = "belzilel@gmail.com", comment = c(ORCID = "0000-0002-9135-014X")))
 Description: A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
 License: GPL-3
 BugReports: https://github.com/lbelzile/TruncatedNormal/issues
-Imports: nleqslv, qrng, Rcpp (>= 0.12.16)
+Imports: nleqslv, randtoolbox, Rcpp (>= 0.12.16)
 LinkingTo: Rcpp, RcppArmadillo
 RoxygenNote: 6.1.1
 VignetteBuilder: knitr

NAMESPACE

@@ -28,7 +28,7 @@ export(trandn)
 export(tregress)
 importFrom(Rcpp,evalCpp)
 importFrom(nleqslv,nleqslv)
-importFrom(qrng,sobol)
+importFrom(randtoolbox,sobol)
 importFrom(stats,"dnorm")
 importFrom(stats,"pnorm")
 importFrom(stats,"pt")

NEWS

@@ -3,6 +3,8 @@ TruncatedNormal changelog
 version 2.1
 
 * bug fix: Cholesky decomposition with permutation now checks the arguments to ensure lb less than ub, to avoid segfault errors. In case of degenerate bounds, the conditional distribution is used with fixed components for the degenerate variables (issue #2)
+* Moved back probit example in documentation
+* change back to randtoolbox package
 
 version 2.0
 
@@ -14,7 +16,7 @@ version 2.0
 * Selected code rewritten in Rcpp
 * New vignette
 * Removed random seed
-* Change to the Sobol sequence because previously used package is unmaintained (no more scrambling for QMC)
+* Change to qrng package for QRN generation of the Sobol s (randtoolbox package unmaintained) - the sequence is not scrambled
 * Throw error if NaN in bounds
 * Handle univariate cases in mvrandt and mvrandn
 * Use nleq solver to solve convex program (also check KKT conditions)

R/mvTcdf.R

@@ -42,7 +42,7 @@
 #' @export
 #' @keywords internal
 #' @author \code{Matlab} code by Zdravko Botev, \code{R} port by Leo Belzile
-#' @importFrom qrng sobol
+#' @importFrom randtoolbox sobol
 mvTcdf <- function(l, u, Sig, df, n = 1e5){
   d <- length(l)
   if (length(u) != d | d != sqrt(length(Sig)) | any(l > u)) {

R/mvnprqmc.R

@@ -6,10 +6,12 @@ mvnprqmc <- function(n, L, l, u, mu){
     d <- length(l) # Initialization
     Z <- matrix(0, d, n) # create array for variables
     # QMC pointset - 1 is Owen's scrambling
-    # x <- randtoolbox::sobol(n, dim = d-1, init =TRUE, scrambling = 1, seed=ceiling(1e6*runif(1)))
-    ## ORPHANED PACKAGE at current, allows for scrambling (qrng does not)
+    if(n*(d-1) > 2e7){
+      warning("High memory requirements for storage of QMC sequence\nConsider reducing n")
+    }
+    x <- as.matrix(randtoolbox::sobol(n, dim = d-1, init =TRUE, scrambling = 1, seed=ceiling(1e6*runif(1))))
     ## Similar option in fOptions package. Problem: sobol sequence can overflow (values above 1).
-    x <- qrng::sobol(n = n, d = d - 1, randomize = TRUE)
+    # x <- qrng::sobol(n = n, d = d - 1, randomize = TRUE)
     p <- 0
     for (k in 1:(d-1)){
       # compute matrix multiplication L*Z

R/mvtprqmc.R

@@ -7,9 +7,11 @@ mvtprqmc <- function(n, L, l, u, nu, mu){
   eta <- mu[d];
   Z <- matrix(0, nrow = d, ncol = n); # create array for variables
   # QMC pointset
-  #x <- randtoolbox::sobol(n, dim = d - 1, init = TRUE, scrambling = 1,
-  #         seed = ceiling(1e6 * runif(1)))
-  x <- as.matrix(qrng::sobol(n = n, d = d - 1, randomize = TRUE))
+  if(n*(d-1) > 2e7){
+    warning("High memory requirements for storage of QMC sequence\nConsider reducing n")
+  }
+  x <- as.matrix(randtoolbox::sobol(n, dim = d - 1, init = TRUE, scrambling = 1, seed = ceiling(1e6 * runif(1))))
+  # x <- as.matrix(qrng::sobol(n = n, d = d - 1, randomize = TRUE))
   #Fixed 21.03.2018 to ensure that if d=2, no error returned
   # Monte Carlo uses 'n' samples;
   # precompute constants