Package: MLModelSelection
Type: Package
Title: Model Selection in Multivariate Longitudinal Data Analysis
Version: 1.0
Date: 2020-03-13
Author: Kuo-Jung Lee
Maintainer: Kuo-Jung Lee <kuojunglee@mail.ncku.edu.tw>
Description: 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. 
URL: https://github.com/kuojunglee/
Depends: R(>= 3.5.0)
License: GPL-2
Imports: Rcpp (>= 1.0.1), MASS
Suggests: testthat
LinkingTo: Rcpp, RcppArmadillo, RcppDist
NeedsCompilation: yes
Packaged: 2020-03-20 14:30:29 UTC; kjlee
Repository: CRAN
Date/Publication: 2020-03-20 15:10:08 UTC
Built: R 4.3.0; x86_64-apple-darwin20; 2023-04-12 03:01:03 UTC; unix
Archs: MLModelSelection.so.dSYM
