Bayesian Logistic Regression with Heavy-Tailed Priors

Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), JSCS, 88:14, 2827-2851, .


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install.packages("HTLR")

0.4-1 by Longhai Li, 2 months ago


https://github.com/longhaiSK/HTLR


Report a bug at https://github.com/longhaiSK/HTLR/issues


Browse source code at https://github.com/cran/HTLR


Authors: Longhai Li [aut, cre] , Steven Liu [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp, BCBCSF, glmnet, magrittr

Suggests rda, ggplot2, corrplot, testthat, bayesplot, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

System requirements: C++11


See at CRAN