Package: HTLR
Version: 1.0
Title: Bayesian Logistic Regression with Heavy-Tailed Priors
Authors@R: c(person(given = "Longhai", family = "Li", role = c("aut"), email = "longhai@math.usask.ca",
    comment=c(ORCID="0000-0002-3074-8584")), person(given = "Steven", family = "Liu", role = c("aut", "cre"), 
    email = "shinyu.lieu@gmail.com"))
Description: 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>.
License: GPL-3
URL: https://longhaisk.github.io/HTLR/
BugReports: https://github.com/longhaiSK/HTLR/issues
Depends: R (>= 3.6.2)
Suggests: ggplot2, corrplot, testthat, bayesplot, knitr, rmarkdown
Imports: Rcpp (>= 1.0.0), BCBCSF, glmnet, magrittr
LinkingTo: Rcpp (>= 1.0.0), RcppArmadillo
NeedsCompilation: yes
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Packaged: 2025-12-14 07:06:47 UTC; xil
Author: Longhai Li [aut] (ORCID: <https://orcid.org/0000-0002-3074-8584>),
  Steven Liu [aut, cre]
Maintainer: Steven Liu <shinyu.lieu@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-15 08:10:08 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2025-12-17 00:52:04 UTC; windows
Archs: x64
