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, <arXiv:1405.3319>.

Version: 0.4-2
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: 2020-01-17
Author: Longhai Li ORCID iD [aut, cre], Steven Liu [aut]
Maintainer: Longhai Li <longhai at math.usask.ca>
BugReports: https://github.com/longhaiSK/HTLR/issues
License: GPL-3
URL: https://longhaisk.github.io/HTLR/
NeedsCompilation: yes
SystemRequirements: C++11
Citation: HTLR citation info
Materials: README NEWS
CRAN checks: HTLR results

Downloads:

Reference manual: HTLR.pdf
Vignettes: intro
Package source: HTLR_0.4-2.tar.gz
Windows binaries: r-devel: HTLR_0.4-1.zip, r-devel-gcc8: HTLR_0.4-1.zip, r-release: HTLR_0.4-1.zip, r-oldrel: HTLR_0.4-2.zip
OS X binaries: r-release: HTLR_0.4-2.tgz, r-oldrel: HTLR_0.4-2.tgz
Old sources: HTLR archive

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