Package: wishartinference 0.1.0

Hanqi Shi

wishartinference: Bayesian Inference for the Wishart Distribution Parameters

Posterior inference for the shape parameter alpha and mean matrix mu in the model X_i ~ Wishart_p(2*alpha, Sigma), under both an improper prior and a proper Gamma/inverse-Wishart prior. The posterior mode is found via a Newton-within-EM algorithm and joint samples are drawn via rejection sampling.

Authors:Philip Everson [aut], Hanqi Shi [aut, cre]

wishartinference_0.1.0.tar.gz
wishartinference_0.1.0.zip(r-4.7-x86_64)wishartinference_0.1.0.zip(r-4.7-arm64)wishartinference_0.1.0.zip(r-4.6-x86_64)wishartinference_0.1.0.zip(r-4.6-arm64)
wishartinference_0.1.0.tar.gz(r-4.7-arm64)wishartinference_0.1.0.tar.gz(r-4.7-x86_64)wishartinference_0.1.0.tar.gz(r-4.6-arm64)wishartinference_0.1.0.tar.gz(r-4.6-x86_64)
wishartinference_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
wishartinference/json (API)

# Install 'wishartinference' in R:
install.packages('wishartinference', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.70 score 231 downloads 14 exports 3 dependencies

Last updated from:3ce3164515. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK111
linux-devel-x86_64OK124
source / vignettesOK190
linux-release-arm64OK132
linux-release-x86_64OK132
windows-devel-arm64OK287
windows-devel-x86_64OK177
windows-release-arm64OK192
windows-release-x86_64OK112
wasm-releaseOK122

Exports:gamma_inferenceldetlfafun_gammalfafun_improperlfafun_properlgammap_exportmode_alphaEMmode_alphaEM_gammarejection_samplerrejection_sampler_gammarwishartwishart_hellowishart_inferencewishart_stats

Dependencies:BHRcppRcppArmadillo