Package: HTLR 1.0

Steven Liu

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>.

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

HTLR_1.0.tar.gz
HTLR_1.0.zip(r-4.7)HTLR_1.0.zip(r-4.6)HTLR_1.0.zip(r-4.5)
HTLR_1.0.tgz(r-4.6-x86_64)HTLR_1.0.tgz(r-4.6-arm64)HTLR_1.0.tgz(r-4.5-x86_64)HTLR_1.0.tgz(r-4.5-arm64)
HTLR_1.0.tar.gz(r-4.7-arm64)HTLR_1.0.tar.gz(r-4.7-x86_64)HTLR_1.0.tar.gz(r-4.6-arm64)HTLR_1.0.tar.gz(r-4.6-x86_64)
HTLR_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
HTLR/json (API)

# 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

Pkgdown/docs site:https://longhaisk.github.io

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 openblas cpp openmp

5.04 score 10 stars 11 scripts 263 downloads 16 exports 14 dependencies

Last updated from:0adb6f05e4. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	200
linux-devel-x86_64	OK	183
source / vignettes	OK	401
linux-release-arm64	OK	163
linux-release-x86_64	OK	191
macos-release-arm64	OK	136
macos-release-x86_64	OK	329
macos-oldrel-arm64	OK	155
macos-oldrel-x86_64	OK	306
windows-devel	OK	160
windows-release	OK	164
windows-oldrel	OK	144
wasm-release	OK	134

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

Data Generation | Model Fitting | Model Inspection | Make Predictions

Rendered fromsimu.Rmdusingknitr::rmarkdown

Last update: 2024-11-14
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