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 <doi:10.31234/osf.io/2z6pd>.
| Version: |
0.1.2 |
| Depends: |
R (≥ 3.6.0) |
| 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), igraph (≥ 1.2.4.2), testthat (≥ 2.1.0), cowplot (≥ 1.0.0), covr |
| Published: |
2021-06-01 |
| 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 |
| Materials: |
README NEWS |
| CRAN checks: |
multifear results |
Documentation:
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