Package: bayest 1.5
bayest: Effect Size Targeted Bayesian Two-Sample t-Tests via Markov Chain Monte Carlo in Gaussian Mixture Models
Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arxiv:1906.07524>.
Authors:
bayest_1.5.tar.gz
bayest_1.5.zip(r-4.5)bayest_1.5.zip(r-4.4)bayest_1.5.zip(r-4.3)
bayest_1.5.tgz(r-4.4-any)bayest_1.5.tgz(r-4.3-any)
bayest_1.5.tar.gz(r-4.5-noble)bayest_1.5.tar.gz(r-4.4-noble)
bayest_1.5.tgz(r-4.4-emscripten)bayest_1.5.tgz(r-4.3-emscripten)
bayest.pdf |bayest.html✨
bayest/json (API)
| # Install 'bayest' in R: |
| install.packages('bayest', repos = c('https://riko-k.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:45fd89f2cc. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | OK | Nov 02 2024 |
| R-4.5-linux | OK | Nov 02 2024 |
| R-4.4-win | OK | Nov 02 2024 |
| R-4.4-mac | OK | Nov 02 2024 |
| R-4.3-win | OK | Nov 02 2024 |
| R-4.3-mac | OK | Nov 02 2024 |
Exports:bayes.t.test
Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Effect Size Targeted Bayesian Two-Sample t-Tests via Markov Chain Monte Carlo in Gaussian Mixture Models | bayest-package bayest |
| bayesttest | bayes.t.test |
