Nothing
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>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Longhai Li [aut] (ORCID: <https://orcid.org/0000-0002-3074-8584>), Steven Liu [aut, cre] |
| Maintainer | Steven Liu <shinyu.lieu@gmail.com> |
| License | GPL-3 |
| Version | 1.0 |
| URL | https://longhaisk.github.io/HTLR/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the HTLR package in your browser
Any scripts or data that you put into this service are public.
