MLModelSelection: Model Selection in Multivariate Longitudinal Data Analysis

An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection.

Version:	1.0
Depends:	R (≥ 3.5.0)
Imports:	Rcpp (≥ 1.0.1), MASS
LinkingTo:	Rcpp, RcppArmadillo, RcppDist
Suggests:	testthat
Published:	2020-03-20
DOI:	10.32614/CRAN.package.MLModelSelection
Author:	Kuo-Jung Lee
Maintainer:	Kuo-Jung Lee <kuojunglee at mail.ncku.edu.tw>
License:	GPL-2
URL:	https://github.com/kuojunglee/
NeedsCompilation:	yes
CRAN checks:	MLModelSelection results

Documentation:

Reference manual:

MLModelSelection.pdf

Downloads:

Package source:	MLModelSelection_1.0.tar.gz
Windows binaries:	r-devel: MLModelSelection_1.0.zip, r-release: MLModelSelection_1.0.zip, r-oldrel: MLModelSelection_1.0.zip
macOS binaries:	r-release (arm64): MLModelSelection_1.0.tgz, r-oldrel (arm64): MLModelSelection_1.0.tgz, r-release (x86_64): MLModelSelection_1.0.tgz, r-oldrel (x86_64): MLModelSelection_1.0.tgz

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