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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, <arXiv:1405.3319>.
Browse package contents
Package details |
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| Author | Longhai Li [aut, cre] (<https://orcid.org/0000-0002-3074-8584>), Steven Liu [aut] |
| Maintainer | Longhai Li <longhai@math.usask.ca> |
| License | GPL-3 |
| Version | 0.4-4 |
| URL | https://longhaisk.github.io/HTLR/ |
| Package repository | View on CRAN |
| Installation |
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