Package: MLModelSelection 1.0
Kuo-Jung Lee
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.
Authors:
MLModelSelection_1.0.tar.gz
MLModelSelection_1.0.tar.gz(r-4.5-noble)MLModelSelection_1.0.tar.gz(r-4.4-noble)
MLModelSelection_1.0.tgz(r-4.4-emscripten)MLModelSelection_1.0.tgz(r-4.3-emscripten)
MLModelSelection.pdf |MLModelSelection.html✨
MLModelSelection/json (API)
| # Install 'MLModelSelection' in R: |
| install.packages('MLModelSelection', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- SimulatedData - Simulated data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:c619b68ac3. Checks:2 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 07 2025 |
| R-4.5-linux-x86_64 | OK | Jan 07 2025 |
Exports:MLModelSelectionMCMC
Dependencies:MASSRcppRcppArmadilloRcppDist
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Model estimation for multivariate longitudinal models. | MLModelSelectionMCMC |
| Simulated data | SimulatedData |
