Bayesian Logistic Regression with Heavy-Tailed Priors R package HTLR version 1.0 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), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <doi:10.48550/arXiv.1405.3319>. Software Depends: R (>= 3.6.2) Imports: Rcpp (>= 1.0.0), BCBCSF, glmnet, magrittr LinkingTo: Rcpp (>= 1.0.0), RcppArmadillo Suggests: ggplot2, corrplot, testthat, bayesplot, knitr, rmarkdown Steven Liu <shinyu.lieu@gmail.com> Comprehensive R Archive Network (CRAN) Longhai Li [aut] (ORCID: <https://orcid.org/0000-0002-3074-8584>), Steven Liu [aut, cre] GPL-3 2025-12-15 application/tgz https://CRAN.R-project.org/package=HTLR doi:10.32614/CRAN.package.HTLR