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>.

Package details

AuthorLonghai Li [aut, cre] (<>), Steven Liu [aut]
MaintainerLonghai Li <>
Package repositoryView on CRAN
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HTLR documentation built on Sept. 13, 2020, 5:20 p.m.