Package: HTLR
Version: 0.4-4
Title: Bayesian Logistic Regression with Heavy-Tailed Priors
Authors@R: c(person(given = "Longhai", family = "Li", role = c("aut", "cre"), email = "longhai@math.usask.ca",
    comment=c(ORCID="0000-0002-3074-8584")), person(given = "Steven", family = "Liu", role = c("aut"), 
    email = "xil865@usask.ca"))
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, <arXiv:1405.3319>.
License: GPL-3
URL: https://longhaisk.github.io/HTLR/
BugReports: https://github.com/longhaiSK/HTLR/issues
Depends: R (>= 3.1.0)
Suggests: ggplot2, corrplot, testthat (>= 2.1.0), bayesplot, knitr,
        rmarkdown
Imports: Rcpp (>= 0.12.0), BCBCSF, glmnet, magrittr
LinkingTo: Rcpp (>= 0.12.0), RcppArmadillo
NeedsCompilation: yes
SystemRequirements: C++11
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.2.1
VignetteBuilder: knitr
Packaged: 2022-10-21 18:03:46 UTC; xil
Author: Longhai Li [aut, cre] (<https://orcid.org/0000-0002-3074-8584>),
  Steven Liu [aut]
Maintainer: Longhai Li <longhai@math.usask.ca>
Repository: CRAN
Date/Publication: 2022-10-22 12:47:53 UTC
Built: R 4.5.2; x86_64-w64-mingw32; 2025-12-03 04:53:23 UTC; windows
Archs: x64
