[aut,
cre],
Steven Liu [aut]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>.
| Version: | 0.4-2 |
| 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: | 2020-01-17 |
| 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 |
| Reference manual: | HTLR.pdf |
| Vignettes: |
intro |
| Package source: | HTLR_0.4-2.tar.gz |
| Windows binaries: | r-devel: HTLR_0.4-2.zip, r-release: HTLR_0.4-2.zip, r-oldrel: HTLR_0.4-2.zip |
| macOS binaries: | r-release: HTLR_0.4-2.tgz, r-oldrel: HTLR_0.4-2.tgz |
| Old sources: | HTLR archive |
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