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-any)MvBinary_1.1.zip(r-4.6-any)MvBinary_1.1.zip(r-4.5-any)
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✨
DESCRIPTION
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 207 downloads 6 exports 4 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK109
source / vignettesOK122
linux-releaseOK106
macos-releaseOK195
macos-oldrelOK190
windows-develOK64
windows-releaseOK73
windows-oldrelOK87
wasm-releaseOK72

Exports:ComputeEmpiricCramerComputeMvBinaryCramerMvBinaryEstimMvBinaryProbaPostprintsummary

Dependencies:latticeMatrixmgcvnlme