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:
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-4.4-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')) |
- MvBinaryExample - Simulated binary data: MvBinaryExample
- plants - Real binary data: Plants
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 years agofrom:13230019ef. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 11 2024 |
| R-4.5-win | OK | Nov 11 2024 |
| R-4.5-linux | OK | Nov 11 2024 |
| R-4.4-win | OK | Nov 11 2024 |
| R-4.4-mac | OK | Nov 11 2024 |
| R-4.3-win | OK | Nov 11 2024 |
| R-4.3-mac | OK | Nov 11 2024 |
Exports:ComputeEmpiricCramerComputeMvBinaryCramerMvBinaryEstimMvBinaryProbaPostprintsummary
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| MvBinary a package for Multivariate Binary data | MvBinary-package MvBinary |
| Computation of the Empiric Cramer'v. | ComputeEmpiricCramer |
| Computation of the model Cramer'v. | ComputeMvBinaryCramer |
| Create an instance of the ['MvBinaryResult'] class | MvBinaryEstim |
| Simulated binary data: MvBinaryExample | MvBinaryExample |
| Computation of the model Cramer'v. | MvBinaryProbaPost |
| Constructor of ['MvBinaryResult'] class | MvBinaryResult-class |
| Real binary data: Plants | plants |
| Summary function. | print print,MvBinaryResult-method |
| Summary function. | summary summary,MvBinaryResult-method |
