HTLR: 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), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <doi:10.48550/arXiv.1405.3319>.

Version: 0.4-4
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.12.0), BCBCSF, glmnet, magrittr
LinkingTo: Rcpp (≥ 0.12.0), RcppArmadillo
Suggests: ggplot2, corrplot, testthat (≥ 2.1.0), bayesplot, knitr, rmarkdown
Published: 2022-10-22
DOI: 10.32614/CRAN.package.HTLR
Author: Longhai Li ORCID iD [aut, cre], Steven Liu [aut]
Maintainer: Longhai Li <longhai at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++11
Citation: HTLR citation info
Materials: README NEWS
CRAN checks: HTLR results


Reference manual: HTLR.pdf
Vignettes: intro


Package source: HTLR_0.4-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): HTLR_0.4-4.tgz, r-oldrel (arm64): HTLR_0.4-4.tgz, r-release (x86_64): HTLR_0.4-4.tgz, r-oldrel (x86_64): HTLR_0.4-4.tgz
Old sources: HTLR archive


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