dabestr: Data Analysis using Bootstrap-Coupled Estimation

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: 0.2.2
Depends: R (≥ 3.5.0), boot, magrittr
Imports: cowplot, dplyr, ellipsis, ggplot2 (≥ 3.0), forcats, ggforce, ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr
Suggests: knitr, rmarkdown, tufte, testthat, vdiffr
Published: 2019-07-04
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

Downloads:

Reference manual: dabestr.pdf
Vignettes: Bootstrap Confidence Intervals
Robust and Beautiful Statistical Visualization
Using dabestr
Package source: dabestr_0.2.2.tar.gz
Windows binaries: r-devel: dabestr_0.2.2.zip, r-release: dabestr_0.2.2.zip, r-oldrel: dabestr_0.2.1.zip
OS X binaries: r-release: dabestr_0.2.2.tgz, r-oldrel: dabestr_0.2.1.tgz
Old sources: dabestr archive

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