SimMultiCorrData

0.2.2

Simulation of Correlated Data with Multiple Variable Types

3packages depend

9Kdownloads / year

–test coverage

11/13checks pass

Overview

About

Maintained by Allison Cynthia FialkowskiFirst published 2017-06-304 releasesCRAN page ↗GitHub ↗

Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order (DOI:10.1007/BF02293811) or Headrick's fifth-order (DOI:10.1016/S0167-9473(02)00072-5) polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from 'GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, DOI:10.1002/asmb.901). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, DOI:10.1002/asmb.2072). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.

Install

R (CRAN)pak (dev)Ubuntu (r2u)openSUSE (OBS)

install.packages("SimMultiCorrData", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("AFialkowski/SimMultiCorrData")

Development version from AFialkowski/SimMultiCorrData. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-simmulticorrdata

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-SimMultiCorrData

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

2NOTE11OK

Failing flavors

NOTE r-devel-linux-x86_64-debian-clang
NOTE r-devel-linux-x86_64-debian-gcc

Slowest check: 11.0 min · r-devel-linux-x86_64-fedora-clang

Check history

NOTE2026-03-10
12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation signals are not tracked yet for this package.

Downloads

CRAN downloads in the past year

Rank #5,603 · ~25/day · ~754/mo

SimMultiCorrData Releases R versions

Daily download trend is not available in this view yet.

51730 days

1.7K90 days

9K1 year

Compare downloads with other packages →

Also on150 r2u27 autocran

Repository

GitHub ↗ afialkowski/simmulticorrdata

12Stars

3Forks

1Open issues

0Open PRs

0Releases

23Commits

1Contributors

23 commits · Last activity 2018-06-28

Repository practices

Upstream repositoryBeta

4 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/afialkowski/simmulticorrdata on 2026-07-19.

CRAN release process (2)

cran-comments.mdrevdep/

Docs source (1)

README.Rmd

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

11 external dependencies (excludes base and recommended)

Depends (1)

R >= 3.3.0

Imports (11)

BB nleqslv GenOrd psychMatrixVGAM triangle ggplot2gridstatsutils

LinkingTo (0)

none

Suggests (4)

knitr rmarkdown printr testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

3direct

8indirect

SimCorrMix oeli stenR

Code & Tests

Datasets

People & History

People (0)

Author records are not tracked yet for this package.

Listed in earlier versions (1)

Allison Cynthia Fialkowski

no longer listed · 0.1.0 to 0.2.2

Package Timeline

4 releases. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
R
R 4.2.0 released · 2022-04-22
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
R
R 3.6.0 released · 2019-04-26
0.2.2Latest
2018-06-28 · current release · diff ↗
R
R 3.5.0 released · 2018-04-23
0.2.1
2017-11-09 · diff ↗
0.2.0
2017-10-25 · diff ↗
0.1.0
2017-06-30
R
R 3.4.0 released · 2017-04-21

Package metadata

First published: 2017-06-30
Total releases: 4 / 9 yrs
License: GPL-2 OSI
Download size: 900 KB
Installed size: not tracked yet
With dependencies: not tracked yet

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