Data Analysis using Bootstrap-Coupled ESTimation.
Estimation statistics is a simple framework that avoids the pitfalls of
significance testing. It uses familiar statistical concepts: means,
mean differences, and error bars. More importantly, it focuses on the
effect size of one's experiment/intervention, as opposed to a false
dichotomy engendered by P values.
An estimation plot has two key features:
1. It presents all datapoints as a swarmplot, which orders each point to
display the underlying distribution.
2. It presents the effect size as a bootstrap 95% confidence interval on a
separate but aligned axes.
Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.
<doi:10.1038/s41592-019-0470-3>.
The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.
| Version: |
0.3.0 |
| Depends: |
R (≥ 3.5.0), magrittr, stats, utils |
| Imports: |
boot, cowplot, dplyr, effsize, ellipsis, ggplot2 (≥ 3.2), forcats, ggforce, ggbeeswarm, plyr, RColorBrewer, rlang, simpleboot, stringr, tibble, tidyr |
| Suggests: |
knitr, rmarkdown, tufte, testthat, vdiffr |
| Published: |
2020-07-13 |
| Author: |
Joses W. Ho [cre, aut],
Tayfun Tumkaya [aut] |
| Maintainer: |
Joses W. Ho <joseshowh at gmail.com> |
| BugReports: |
https://github.com/ACCLAB/dabestr/issues |
| License: |
file LICENSE |
| URL: |
https://github.com/ACCLAB/dabestr |
| NeedsCompilation: |
no |
| Citation: |
dabestr citation info |
| Materials: |
README NEWS |
| CRAN checks: |
dabestr results |