Package: MultiDiscreteRNG 0.1.0

Chak Kwong (Tommy) Cheng

MultiDiscreteRNG: Generate Multivariate Discrete Data

Generate multivariate discrete data with generalized Poisson, negative binomial and binomial marginal distributions using user-specified distribution parameters and a target correlation matrix. The method is described in Cheng and Demirtas (2026) <doi:10.48550/arXiv.2602.07707>.

Authors:Chak Kwong Cheng [aut, cre, cph], Hakan Demirtas [aut]

MultiDiscreteRNG_0.1.0.tar.gz
MultiDiscreteRNG_0.1.0.zip(r-4.7)MultiDiscreteRNG_0.1.0.zip(r-4.6)MultiDiscreteRNG_0.1.0.zip(r-4.5)
MultiDiscreteRNG_0.1.0.tgz(r-4.6-any)MultiDiscreteRNG_0.1.0.tgz(r-4.5-any)
MultiDiscreteRNG_0.1.0.tar.gz(r-4.7-any)MultiDiscreteRNG_0.1.0.tar.gz(r-4.6-any)
MultiDiscreteRNG_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MultiDiscreteRNG/json (API)
NEWS

# Install 'MultiDiscreteRNG' in R:
install.packages('MultiDiscreteRNG', repos = c('https://ckchengtommy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ckchengtommy/multidiscreterng/issues

On CRAN:

Conda:

3.40 score 2 scripts 137 downloads 22 exports 17 dependencies

Last updated from:196b0c716f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK147
source / vignettesOK165
linux-release-x86_64OK169
macos-release-arm64OK138
macos-oldrel-arm64OK157
windows-develOK105
windows-releaseOK102
windows-oldrelOK112
wasm-releaseOK96

Exports:BinToBBinToGPDBinToMixBinToNBcalc.bin.prob.Bcalc.bin.prob.GPDcalc.bin.prob.NBdiscrete_contgenBgenerate.binaryVargenGPDgenMixgenNBGetGpoisPMFQuantileGpoissimBinaryCorr.BsimBinaryCorr.GPDsimBinaryCorr.MixsimBinaryCorr.NBvalidation.Bparametersvalidation.GPDparametersvalidation.NBparameters

Dependencies:bbmlebdsmatrixcorpcorcubatureGenOrdGPArotationlatticeMASSMatrixmatrixcalcmnormtMultiOrdmvtnormnlmenumDerivpsychRcpp

Readme and manuals

Help Manual

Help pageTopics
Convert multivariate binary data back to the original binomial scaleBinToB
Convert multivariate binary data back to the original generalized Poisson scaleBinToGPD
Convert multivariate binary data to mixed distribution outcomesBinToMix
Convert multivariate binary data back to the original negative binomial scaleBinToNB
Collapse binomial data outcomes to binary variablecalc.bin.prob.B
Collapse discrete generalized Poisson outcomes to binary variablescalc.bin.prob.GPD
Collapse discrete negative binomial outcomes to binary variablescalc.bin.prob.NB
Compute the tetrachoric correlation matrix for a multivariate standard normal distributiondiscrete_cont
Generate multivariate binomial datagenB
Generate multivariate Binary data using the Emrich and Piedmonte (1991) approach Approachgenerate.binaryVar
Generate multivariate generalized Poisson datagenGPD
Generate multivariate mixed discrete datagenMix
Generate multivariate negative binomial datagenNB
Get probability mass function of generalized Poisson distributionGetGpoisPMF
Compute the quantile function of the generalized Poisson distributionQuantileGpois
Compute intermediate binary correlations for multivariate binomial datasimBinaryCorr.B
Compute intermediate binary correlations for multivariate generalized Poisson datasimBinaryCorr.GPD
Calculate intermediate binary correlations for mixed datasimBinaryCorr.Mix
Compute intermediate binary correlations for multivariate negative binomial datasimBinaryCorr.NB
Validate binomial parameters are within feasible rangesvalidation.Bparameters
Validate generalized Poisson parameters are within feasible rangesvalidation.GPDparameters
Validate if the input negative binomial parameters are within feasible rangevalidation.NBparameters