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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 |
| 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 |
| Reference manual: | MLModelSelection.pdf |
| 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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