HTLR (0.4-2)

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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, .

Maintainer: Longhai Li
Author(s): Longhai Li [aut, cre] (<>), Steven Liu [aut]

License: GPL-3

Uses: BCBCSF, glmnet, magrittr, Rcpp, ggplot2, testthat, corrplot, knitr, rmarkdown, bayesplot

Released 2 days ago.

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