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  [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 |
Documentation:
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