pass CRAN submission

DESCRIPTION

@@ -4,20 +4,21 @@ Version: 0.11.1
 Authors@R:
     c(person(given = "Alex",
              family = "Knudson",
-             role = c("aut", "cre"),
+             role = "aut",
              email = "[email protected]"),
       person(given = "Grant",
              family = "Schissler",
-             role = "aut"))
-Maintainer: Alex Knudson <aknudson@nevada.unr.edu>
+             role = c("aut", "cre"),
+             email = "aschissler@unr.edu"))
 Description: Simulate multivariate data with arbitrary marginal distributions.
+  'bigsimr' is an package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. 
+  It utilizes a Julia package named 'Bigsimr.jl' for its core routines.
 URL: https://github.com/SchisslerGroup/r-bigsimr
 BugReports: https://github.com/SchisslerGroup/Bigsimr.jl/issues
 Depends: 
     R (>= 3.6.0)
-License: MIT + file LICENSE
+License: GPL-3
 Encoding: UTF-8
-LazyData: true
 SystemRequirements: Julia (>= 1.5), Bigsimr.jl, Distributions.jl
 RoxygenNote: 7.1.1
 Imports:

R/bigsimr.R

@@ -8,6 +8,23 @@
 #' @param pkg_check logical, check for Bigsimr.jl package and install if necessary
 #' @param ... Parameters are passed down to JuliaCall::julia_setup
 #'
+#' @return Return the imported wrapper of Bigsimr.jl Julia package
+#' @examples
+#' \donttest{
+#' library(bigsimr)
+#' bs   <- bigsimr::bigsimr_setup()
+#' dist <- bigsimr::distributions_setup()
+#'
+#' JuliaCall::julia_eval('using Random; Random.seed!(1);')
+#' # Generate random target correlation matrix
+#' target_corr <- bs$cor_randPD(3)
+#' # Set the margins of variables
+#' margins <- c(dist$Binomial(20, 0.2), dist$Beta(2, 3), dist$LogNormal(3, 1))
+#' # Adjust target correlation matrix using Pearson matching
+#' adjusted_corr <- bs$pearson_match(target_corr, margins)
+#' # Generate random vectors
+#' x <- bs$rvec(10000, adjusted_corr, margins)
+#' }
 #' @export
 bigsimr_setup <- function (pkg_check = TRUE, ...){
   julia <- JuliaCall::julia_setup(installJulia = TRUE, ...)

R/distributions.R

@@ -3,6 +3,23 @@
 #' This function initializes the Distributions package that many of the bigsimr
 #' functions work with.
 #'
+#' @return Return the imported wrapper of Distributions.jl Julia package
+#' @examples
+#' \donttest{
+#' library(bigsimr)
+#' bs   <- bigsimr::bigsimr_setup()
+#' dist <- bigsimr::distributions_setup()
+#'
+#' JuliaCall::julia_eval('using Random; Random.seed!(1);')
+#' # Generate random target correlation matrix
+#' target_corr <- bs$cor_randPD(3)
+#' # Set the margins of variables
+#' margins <- c(dist$Binomial(20, 0.2), dist$Beta(2, 3), dist$LogNormal(3, 1))
+#' # Adjust target correlation matrix using Pearson matching
+#' adjusted_corr <- bs$pearson_match(target_corr, margins)
+#' # Generate random vectors
+#' x <- bs$rvec(10000, adjusted_corr, margins)
+#' }
 #' @export
 distributions_setup <- function() {
   JuliaCall::julia_install_package_if_needed("Distributions")

inst/COPYRIGHTS

@@ -0,0 +1,21 @@
+Overall license
+===============
+
+bigsimr: Fast Generation of High-Dimensional Random Vectors
+Copyright (C) 2021-2021  Grant Schissler
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+The years in the copyright notice above are to be understood as a
+range, including the years on both ends.

tests/testthat/test-bigsimr.R

@@ -4,3 +4,35 @@ test_that("Bigsimr.jl loads", {
   bs   <- bigsimr::bigsimr_setup()
   dist <- bigsimr::distributions_setup()
 })
+
+
+
+test_that("Pearson matching is working", {
+  skip_on_cran()
+
+  bs   <- bigsimr::bigsimr_setup()
+  dist <- bigsimr::distributions_setup()
+
+  JuliaCall::julia_eval('using Random; Random.seed!(1);')
+  target_corr <- bs$cor_randPD(3)
+  margins <- c(dist$Binomial(20, 0.2), dist$Beta(2, 3), dist$LogNormal(3, 1))
+  adjusted_corr <- bs$pearson_match(target_corr, margins)
+  x <- bs$rvec(100000, adjusted_corr, margins)
+
+  expect_equal(mean(bs$cor(x, bs$Pearson)), mean(target_corr), tolerance = 0.05)
+})
+
+test_that("Spearman matching is working", {
+  skip_on_cran()
+
+  bs   <- bigsimr::bigsimr_setup()
+  dist <- bigsimr::distributions_setup()
+
+  JuliaCall::julia_eval('using Random; Random.seed!(1);')
+  spearman_corr <- bs$cor_randPD(3)
+  margins <- c(dist$Binomial(20, 0.2), dist$Beta(2, 3), dist$LogNormal(3, 1))
+  adjusted_corr <- bs$cor_convert(spearman_corr, bs$Spearman, bs$Pearson)
+  x <- bs$rvec(100000, adjusted_corr, margins)
+
+  expect_equal(mean(bs$cor(x, bs$Spearman)), mean(spearman_corr), tolerance = 0.05)
+})