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:Phil Everson [aut], Hanqi Shi [aut, cre]

wishartinference_0.1.0.tar.gz
wishartinference_0.1.0.zip(r-4.7)wishartinference_0.1.0.zip(r-4.6)wishartinference_0.1.0.zip(r-4.5)
wishartinference_0.1.0.tgz(r-4.6-x86_64)wishartinference_0.1.0.tgz(r-4.6-arm64)wishartinference_0.1.0.tgz(r-4.5-x86_64)wishartinference_0.1.0.tgz(r-4.5-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://sunnyhq-shi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sunnyhq-shi/wishart-rejection-sampling/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

2.00 score 10 exports 3 dependencies

Last updated from:98cd0d0c47. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK143
linux-devel-x86_64OK164
source / vignettesOK183
linux-release-arm64OK150
linux-release-x86_64OK143
macos-release-arm64OK76
macos-release-x86_64OK197
macos-oldrel-arm64OK93
macos-oldrel-x86_64OK154
windows-develOK124
windows-releaseOK109
windows-oldrelOK117
wasm-releaseOK122

Exports:ldetlfafun_improperlfafun_properlgammap_exportmode_alphaEMrejection_samplerrwisharttestwishart_inferencewishart_stats

Dependencies:BHRcppRcppArmadillo