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MvBinary
View on CRAN: Click
here
Download and install MvBinary package within the R console
Install from CRAN:
install.packages("MvBinary")
Install from Github:
library("remotes")
install_github("cran/MvBinary")
Install by package version:
library("remotes")
install_version("MvBinary", "1.1")
Attach the package and use:
library("MvBinary")
Maintained by
Mohammed Sedki
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-11-17
Latest Update: 2016-12-15
Description:
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.
How to cite:
Mohammed Sedki (2015). MvBinary: Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. R package version 1.1, https://cran.r-project.org/web/packages/MvBinary
Previous versions and publish date:
1.0 (2015-11-17 14:40)
Other packages that cited MvBinary R package
View MvBinary citation profile
Other R packages that MvBinary depends,
imports, suggests or enhances
Functions, R codes and Examples using
the MvBinary R package
Some associated functions: ComputeEmpiricCramer . ComputeMvBinaryCramer . MvBinary-package . MvBinaryEstim . MvBinaryExample . MvBinaryProbaPost . MvBinaryResult-class . plants . print-methods . summary-methods .
Some associated R codes: Classes.R . Metropolis-Hastings.R . MvBinaryEstim.R . ProbaPost.R . Summary.R . Tools.R . XEM.R . Full MvBinary package functions and examples
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