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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 |
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_0.4-4.tar.gz |
Windows binaries: | r-devel: HTLR_0.4-4.zip, r-release: HTLR_0.4-4.zip, r-oldrel: HTLR_0.4-4.zip |
macOS binaries: | r-release (arm64): HTLR_0.4-4.tgz, r-oldrel (arm64): HTLR_0.4-4.tgz, r-release (x86_64): HTLR_0.4-4.tgz, r-oldrel (x86_64): HTLR_0.4-4.tgz |
Old sources: | HTLR archive |
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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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