Package: HTLR 0.4-4

Longhai Li

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>.

Authors:Longhai Li [aut, cre], Steven Liu [aut]

HTLR_0.4-4.tar.gz
HTLR_0.4-4.tar.gz(r-4.5-noble)HTLR_0.4-4.tar.gz(r-4.4-noble)
HTLR_0.4-4.tgz(r-4.4-emscripten)HTLR_0.4-4.tgz(r-4.3-emscripten)
HTLR.pdf |HTLR.html✨
HTLR/json (API)
NEWS

# Install 'HTLR' in R:

install.packages('HTLR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/longhaisk/htlr/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

colon - Colon Tissues
diabetes392 - Pima Indians Diabetes

2.70 score 7 scripts 285 downloads 16 exports 14 dependencies

Last updated 2 years agofrom:4aef542fd6. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Sep 30 2024
R-4.5-linux-x86_64	NOTE	Sep 30 2024

Exports:%>%bcbcsf_deltas evaluate_pred gendata_FAM gendata_MLR htlr htlr_fit htlr_predict htlr_prior lasso_deltas nzero_idx order_ftest order_kruskal order_plain split_data std

Dependencies:abind BCBCSF codetools foreach glmnet iterators lattice magrittr Matrix Rcpp RcppArmadillo RcppEigen shape survival

Multinomial Logistic Regression with Heavy-Tailed Priors

Longhai Li and Steven Liu

Rendered fromsimu.Rmdusingknitr::rmarkdownon Sep 30 2024.

Last update: 2020-09-09
Started: 2019-10-06

Citation

Readme and manuals

Help Manual

Help page	Topics
Bayesian Logistic Regression with Heavy-Tailed Priors	HTLR-package
Create a Matrix of Markov Chain Samples	as.matrix.htlr.fit
Colon Tissues	colon
Pima Indians Diabetes	diabetes392
Evaluate Prediction Results	evaluate_pred
Generate Simulated Data with Factor Analysis Model	gendata_FAM
Generate Simulated Data with Multinomial Logistic Regression Model	gendata_MLR
Fit a HTLR Model	htlr
Generate Prior Configuration	htlr_prior
Get Indices of Non-Zero Coefficients	nzero_idx
Make Prediction on New Data	predict.htlr.fit
Split Data into Train and Test Partitions	split_data
Standardizes a Design Matrix	std
Posterior Summaries	summary.htlr.fit