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-4.5)MvBinary_1.1.zip(r-4.4)MvBinary_1.1.zip(r-4.3)
MvBinary_1.1.tgz(r-4.5-any)MvBinary_1.1.tgz(r-4.4-any)MvBinary_1.1.tgz(r-4.3-any)
MvBinary_1.1.tar.gz(r-4.5-noble)MvBinary_1.1.tar.gz(r-4.4-noble)
MvBinary_1.1.tgz(r-4.4-emscripten)MvBinary_1.1.tgz(r-4.3-emscripten)
MvBinary.pdf |MvBinary.html✨
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:

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 177 downloads 6 exports 4 dependencies

Last updated 8 years agofrom:13230019ef. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 09 2025
R-4.5-winOKFeb 09 2025
R-4.5-macOKFeb 09 2025
R-4.5-linuxOKFeb 09 2025
R-4.4-winOKFeb 09 2025
R-4.4-macOKFeb 09 2025
R-4.3-winOKFeb 09 2025
R-4.3-macOKFeb 09 2025

Exports:ComputeEmpiricCramerComputeMvBinaryCramerMvBinaryEstimMvBinaryProbaPostprintsummary

Dependencies:latticeMatrixmgcvnlme