multifear: Multiverse Analyses for Conditioning Data
A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) <doi:10.1016/j.brat.2022.104072>.
Version: |
0.1.3 |
Depends: |
R (≥ 3.6.3) |
Imports: |
dplyr (≥ 0.8.4), purrr (≥ 0.3.3), stats (≥ 3.6.2), ez (≥
4.4.0), stringr (≥ 1.4.0), reshape2 (≥ 1.4.3), tibble (≥
2.1.3), ggplot2 (≥ 3.2.1), effsize (≥ 0.7.8), nlme (≥
3.1.144), BayesFactor (≥ 0.9.12.4.2), bayestestR (≥ 0.10.0), broom (≥ 0.5.5), effectsize (≥ 0.4.1), esc (≥ 0.5.1), forestplot (≥ 1.10), bootstrap (≥ 2019.6) |
Suggests: |
gridExtra (≥ 2.3), fastDummies (≥ 1.6.1), vctrs (≥ 0.3.1), tidyselect (≥ 1.0.0), tidyr (≥ 1.0.2), plyr (≥ 1.8.6), ggraph (≥ 2.0.1), testthat (≥ 2.1.0), cowplot (≥ 1.0.0), covr, knitr, rmarkdown |
Published: |
2023-09-23 |
DOI: |
10.32614/CRAN.package.multifear |
Author: |
Angelos-Miltiadis Krypotos [aut, cre, cph] |
Maintainer: |
Angelos-Miltiadis Krypotos <amkrypotos at gmail.com> |
BugReports: |
https://github.com/AngelosPsy/multifear/issues |
License: |
GPL-3 |
URL: |
https://github.com/AngelosPsy/multifear |
NeedsCompilation: |
no |
Citation: |
multifear citation info |
Materials: |
README NEWS |
CRAN checks: |
multifear results |
Documentation:
Downloads:
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