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.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4