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]

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HTLR.pdf |HTLR.html✨
HTLR/json (API)
NEWS

# Install 'HTLR' in R:

install.packages('HTLR', repos = c('https://longhaisk.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

On CRAN:

bayesian classification high-dimensional-data machine-learning mcmc

16 exports 9 stars 1.54 score 14 dependencies 7 scripts 354 downloads

Last updated 1 months agofrom:d755294146. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 14 2024
R-4.5-win-x86_64	NOTE	Sep 14 2024
R-4.5-linux-x86_64	NOTE	Sep 14 2024
R-4.4-win-x86_64	NOTE	Sep 14 2024
R-4.4-mac-x86_64	NOTE	Sep 14 2024
R-4.4-mac-aarch64	NOTE	Sep 14 2024
R-4.3-win-x86_64	NOTE	Sep 14 2024
R-4.3-mac-x86_64	NOTE	Sep 14 2024
R-4.3-mac-aarch64	NOTE	Sep 14 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 14 2024.

Last update: 2024-08-15
Started: 2019-09-09

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
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