Simulation of Correlated Data with Multiple Variable Types
Overview
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.
1. Fixed error in formation of intermediate matrices with findintercorr2.
2. Fixed check for repeated continuous distribution when using method = "Fleishman".
3. Updated citations.
4. Fixed function/package links in documentation.
5. Added automated testing with 'testthat' package.
From NEWS.md in the release
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Allison Cynthia Fialkowski <allijazz@uab.edu>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: person(given = c("Allison", "Cynthia"),
family = "Fialkowski",
role = c("aut", "cre"),
email = "allijazz@uab.edu")
as necessary.
Found the following \keyword or \concept entries
which likely give several index terms:
File ‘calc_fisherk.Rd’:
\keyword{cumulants,}
File ‘calc_lower_skurt.Rd’:
\keyword{Fleishman,}
\keyword{boundary,}
\keyword{kurtosis,}
File ‘calc_moments.Rd’:
\keyword{cumulants,}
File ‘calc_theory.Rd’:
\keyword{cumulants,}
File ‘cdf_prob.Rd’:
\keyword{cumulative,}
\keyword{statistics,}
\keyword{theoretical,}
File ‘chat_nb.Rd’:
\keyword{Binomial,}
\keyword{correlation,}
File ‘chat_pois.Rd’:
\keyword{Poisson,}
\keyword{correlation,}
File ‘denom_corr_cat.Rd’:
\keyword{intercorrelation,}
File ‘error_loop.Rd’:
...[truncated]...
File ‘rcorrvar.Rd’:
\keyword{Binomial,}
\keyword{Fleishman,}
\keyword{Headrick,}
\keyword{Poisson,}
\keyword{continuous,}
\keyword{ordinal,}
\keyword{simulation,}
File ‘rcorrvar2.Rd’:
\keyword{Binomial,}
\keyword{Fleishman,}
\keyword{Headrick,}
\keyword{Poisson,}
\keyword{continuous,}
\keyword{ordinal,}
\keyword{simulation,}
File ‘sim_cdf_prob.Rd’:
\keyword{cumulative,}
\keyword{empirical,}
\keyword{simulated,}
\keyword{statistics,}
File ‘stats_pdf.Rd’:
\keyword{theoretical,}
File ‘valid_corr.Rd’:
\keyword{Binomial,}
\keyword{Fleishman,}
\keyword{Headrick,}
\keyword{Poisson,}
\keyword{bounds,}
\keyword{continuous,}
\keyword{correlation,}
\keyword{ordinal,}
File ‘valid_corr2.Rd’:
\keyword{Binomial,}
\keyword{Fleishman,}
\keyword{Headrick,}
\keyword{Poisson,}
\keyword{bounds,}
\keyword{continuous,}
\keyword{correlation,}
\keyword{ordinal,}- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 56includes a package overview page
- Examples that run
- 64%18 run in a plain check · 10 never run
- Exported functions with examples
- 51%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 95%
- Benefits of SimMultiCorrData and Comparison to Other Packages HTML
- Comparison of Correlation Method 1 and Correlation Method 2 HTML
- Comparison of Simulated Distribution to Theoretical Distribution or Empirical Data HTML
- Functions by Topic HTML
- Overall Workflow for Data Simulation HTML
- Overview of Error Loop HTML
- Using the Sixth Cumulant Correction to Find Valid Power Method Pdfs HTML
- Variable Types HTML
Downloads
Repository
Stars over time
Forks over time
Issues over time
4 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/afialkowski/simmulticorrdata on 2026-10-18.
Dependencies
Installing SimMultiCorrData adds 32 packages (15 with compiled code, 25 MB of source tarballs). Heaviest: ggplot2 6.0 MB, cubature 3.2 MB, Rcpp 1.8 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.2.2Latest
- RR 3.5.0 released · 2018-04-23
- 0.2.12017-11-09 · diff ↗
- 0.2.02017-10-25 · diff ↗
- 0.1.02017-06-30
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-06-30
- Total releases
- 4 / 9 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.3.0
- Bundled data
- 2.3 KB / 2 files
- Download size
- 900 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("SimMultiCorrData")Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.