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cff-version: 1.2.0
message: 'To cite package "MLModelSelection" in publications use:'
type: software
license: GPL-2.0-only
title: 'MLModelSelection: Model Selection in Multivariate Longitudinal Data Analysis'
version: '1.0'
doi: 10.32614/CRAN.package.MLModelSelection
abstract: 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.
authors:
- family-names: Lee
  given-names: Kuo-Jung
  email: kuojunglee@mail.ncku.edu.tw
repository: https://kuojunglee.r-universe.dev
repository-code: https://github.com/kuojunglee/
commit: c619b68ac3a0230abf8eb6fb1f70191c03c8497d
url: https://github.com/kuojunglee/
date-released: '2020-03-13'
contact:
- family-names: Lee
  given-names: Kuo-Jung
  email: kuojunglee@mail.ncku.edu.tw