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Effect Size Targeted Bayesian Two-Sample t-Tests via Markov Chain Monte Carlo in Gaussian Mixture Models

0packages depend
3Kdownloads / year
–test coverage
11/13flavors OK

Overview

About

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.

Install

Health

CRAN checks
2NOTE11OK
Check messages
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Riko Kelter <riko.kelter@uni-siegen.de>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Riko",
                    family = "Kelter",
                    role = c("aut", "cre"),
                    email = "riko.kelter@uni-siegen.de")
as necessary.

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>.

CRAN check page ↗

Slowest check: 53 s · r-oldrel-windows-x86_64
Status changes
  • 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
Advisories

none known

Documentation

In release 1.5
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
2includes a package overview page
Examples that run
100%1 run in a plain check
Exported functions with examples
100%
Documented parameters
not tracked
Return-value docs
not tracked
Help pages citing references
100%

Downloads

3K
CRAN downloads in the past year
Rank #11,433 · ~8/day · ~251/mo
Daily download trend is not available in this view yet.
35430 days
94590 days
3K1 year
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Also on87 r2u11 autocran

Dependencies

Declared dependencies
2 external dependencies (excludes base and recommended)

Installing bayest adds 6 packages (4 with compiled code, 3.7 MB of source tarballs). Heaviest: mcmc 1.5 MB, quantreg 903 KB, MCMCpack 718 KB.

Depends (0)
none
Imports (1)
LinkingTo (0)
none
Suggests (2)
codaMASS
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (from the free-text Author field) (1)

Maintainer since 1.0 (2019)

Releases (6)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 1.5Latest
    record
    2024-04-05 · current release · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • 1.4
    record
    2020-05-31 · diff ↗
  • 1.3
    record
    2020-05-27 · diff ↗
  • 1.2
    record
    2020-05-05 · diff ↗
  • R
    R 4.0.0 released · 2020-04-24
  • 1.1
    record
    2019-12-13 · diff ↗
  • 1.0
    record
    2019-08-02
  • R
    R 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

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("bayest")

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for bayest version 1.5 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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