Effect Size Targeted Bayesian Two-Sample t-Tests via Markov Chain Monte Carlo in Gaussian Mixture Models
Overview
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.
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Maintainer: ‘Riko Kelter <riko.kelter@uni-siegen.de>’
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The Description field contains
details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.- NOTE11 OK, 2 NOTE2026-09-08
- ERROR10 OK, 2 NOTE, 1 ERROR2026-09-07
- NOTE11 OK, 2 NOTE2026-06-09
- ERROR11 OK, 1 NOTE, 1 ERROR2026-06-08
- NOTE12 OK, 2 NOTE2026-03-10
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- RR 4.6.0 released · 2026-04-24
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- RR 4.4.0 released · 2024-04-24
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- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.42020-05-31 · diff ↗
- 1.32020-05-27 · diff ↗
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- RR 4.0.0 released · 2020-04-24
- 1.12019-12-13 · diff ↗
- 1.02019-08-02
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2019-08-02
- Total releases
- 6 / 7 yrs
- License
- GPL-3 OSI
- Download size
- 11 KB
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