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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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=HTLR
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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