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MLModelSelection  

Model Selection in Multivariate Longitudinal Data Analysis
View on CRAN: Click here


Download and install MLModelSelection package within the R console
Install from CRAN:
install.packages("MLModelSelection")

Install from Github:
library("remotes")
install_github("cran/MLModelSelection")

Install by package version:
library("remotes")
install_version("MLModelSelection", "1.0")



Attach the package and use:
library("MLModelSelection")
Maintained by
Kuo-Jung Lee
[Scholar Profile | Author Map]
First Published: 2020-03-20
Latest Update: 2020-03-20
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.
How to cite:
Kuo-Jung Lee (2020). MLModelSelection: Model Selection in Multivariate Longitudinal Data Analysis. R package version 1.0, https://cran.r-project.org/web/packages/MLModelSelection. Accessed 04 Apr. 2025.
Previous versions and publish date:
No previous versions
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Complete documentation for MLModelSelection
Functions, R codes and Examples using the MLModelSelection R package
Some associated functions: MLModelSelectionMCMC . SimulatedData . 
Some associated R codes: RcppExports.R .  Full MLModelSelection package functions and examples
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