Package: MvBinary 1.1

MvBinary: Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution

Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.

Authors:Matthieu Marbac and Mohammed Sedki

MvBinary_1.1.tar.gz
MvBinary_1.1.zip(r-4.7)MvBinary_1.1.zip(r-4.6)MvBinary_1.1.zip(r-4.5)
MvBinary_1.1.tgz(r-4.6-any)MvBinary_1.1.tgz(r-4.5-any)
MvBinary_1.1.tar.gz(r-4.7-any)MvBinary_1.1.tar.gz(r-4.6-any)
MvBinary_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
MvBinary/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 5 scripts 163 downloads 6 exports 4 dependencies

Last updated from:13230019ef. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK131
source / vignettesOK142
linux-release-x86_64OK121
macos-release-arm64OK165
macos-oldrel-arm64OK173
windows-develOK87
windows-releaseOK74
windows-oldrelOK80
wasm-releaseOK93

Exports:ComputeEmpiricCramerComputeMvBinaryCramerMvBinaryEstimMvBinaryProbaPostprintsummary

Dependencies:latticeMatrixmgcvnlme