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.
- Version0.4-4
- R version≥ 3.1.0
- LicenseGPL-3
- Needs compilation?Yes
- HTLR citation info
- Last release10/22/2022
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Team
Longhai Li
Steven Liu
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- Imports4 packages
- Suggests6 packages
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