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), JSCS, 88:14, 2827-2851, <arXiv:1405.3319>.

Package details

AuthorLonghai Li [aut, cre] (<>), Steven Liu [aut]
MaintainerLonghai Li <[email protected]>
Package repositoryView on CRAN
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HTLR documentation built on Oct. 9, 2019, 1:05 a.m.