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HTLR  

Bayesian Logistic Regression with Heavy-Tailed Priors
View on CRAN: Click here


Download and install HTLR package within the R console
Install from CRAN:
install.packages("HTLR")

Install from Github:
library("remotes")
install_github("cran/HTLR")

Install by package version:
library("remotes")
install_version("HTLR", "1.0")



Attach the package and use:
library("HTLR")
Maintained by
Longhai Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-06
Latest Update: 2022-10-22
Description:
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, .
How to cite:
Longhai Li (2019). HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors. R package version 1.0, https://cran.r-project.org/web/packages/HTLR. Accessed 26 Oct. 2026.
Previous versions and publish date:
0.4-1 (2019-10-09 00:50), 0.4-2 (2020-01-17 08:50), 0.4-3 (2020-09-09 06:20), 0.4-4 (2022-10-22 14:47), 0.4 (2019-10-06 12:10), (2026-07-09 08:06)
Other packages that cited HTLR R package
View HTLR citation profile
Other R packages that HTLR depends, imports, suggests or enhances
Complete documentation for HTLR
Functions, R codes and Examples using the HTLR R package
Some associated functions: HTLR-package . as.matrix.htlr.fit . bcbcsf_deltas . colon . diabetes392 . evaluate_pred . gendata_FAM . gendata_MLR . htlr . htlr_fit . htlr_predict . htlr_prior . lasso_deltas . nzero_idx . order_ftest . order_kruskal . order_plain . pipe . predict.htlr.fit . split_data . std . summary.htlr.fit . 
Some associated R codes: HTLR-package.R . RcppExports.R . data.R . htlr.R . initial-state.R . predict.R . std.R .  Full HTLR package functions and examples
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