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'))

Peer review:

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

Pkgdown:https://longhaisk.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

2.70 score 7 scripts 240 downloads 16 exports 14 dependencies

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

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-linux-x86_64NOTEOct 30 2024

Exports:%>%bcbcsf_deltasevaluate_predgendata_FAMgendata_MLRhtlrhtlr_fithtlr_predicthtlr_priorlasso_deltasnzero_idxorder_ftestorder_kruskalorder_plainsplit_datastd

Dependencies:abindBCBCSFcodetoolsforeachglmnetiteratorslatticemagrittrMatrixRcppRcppArmadilloRcppEigenshapesurvival

Multinomial Logistic Regression with Heavy-Tailed Priors

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