Package: HTLR
Version: 0.4-1
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), JSCS, 88:14, 2827-2851, <arXiv:1405.3319>.
License: GPL-2
URL: https://github.com/longhaiSK/HTLR
BugReports: https://github.com/longhaiSK/HTLR/issues
Depends: R (>= 3.1.0)
Suggests: rda, ggplot2, corrplot, testthat (>= 2.0.0), 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: 6.1.1
VignetteBuilder: knitr
Packaged: 2019-10-08 19:56:38 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: 2019-10-08 22:50:02 UTC
