HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors (original) (raw)
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 |
| 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 |
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