Installation | Example | 1. Working model: Bayesian MoE-Wishart model | 2. Working model: Bayesian Wishart mixture model | 3. Working model: MoE-Wishart model via EM algorithm | 4. Working model: Wishart mixture model via EM algorithm | Reference
Package: moewishart 1.2
moewishart: Mixture-of-Experts Wishart Models for Covariance Data
Methods for maximum likelihood and Bayesian estimation for the Wishart mixture model and the mixture-of-experts Wishart (MoE-Wishart) model. The package provides four inference algorithms for these models, each implemented using the expectation–maximization (EM) algorithm for maximum likelihood estimation and a fully Bayesian approach via Gibbs-within-Metropolis–Hastings sampling.
Authors:
moewishart_1.2.tar.gz
moewishart_1.2.zip(r-4.7)moewishart_1.2.zip(r-4.6)moewishart_1.2.zip(r-4.5)
moewishart_1.2.tgz(r-4.6-any)moewishart_1.2.tgz(r-4.5-any)
moewishart_1.2.tar.gz(r-4.7-any)moewishart_1.2.tar.gz(r-4.6-any)
moewishart_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
moewishart/json (API)
| # Install 'moewishart' in R: |
| install.packages('moewishart', repos = c('https://zhizuio.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zhizuio/moewishart/issues
Datasets:
- CTRP - CTRP drug response covariances data
Last updated from:405de4db6a. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 132 | ||
| source / vignettes | OK | 195 | ||
| linux-release-x86_64 | OK | 122 | ||
| macos-release-arm64 | OK | 157 | ||
| macos-oldrel-arm64 | OK | 148 | ||
| windows-devel | OK | 87 | ||
| windows-release | OK | 86 | ||
| windows-oldrel | OK | 80 | ||
| wasm-release | OK | 101 |
Exports:computeICdWishartlmvgammamixturewishartmoewishartplotMCMCrdirichletsampleIWsimData
Dependencies:abindbackportscheckmateclidistributionalgenericsgluelifecycleloomagrittrmatrixStatsnumDerivpillarpkgconfigposteriorrlangtensorAtibbleutf8vctrs
