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 (2018) <doi:10.1101/377978>.

Version: 0.2.0
Depends: R (≥ 3.5.0), boot, magrittr
Imports: cowplot, dplyr, ggplot2 (≥ 3.0), forcats, ggforce, ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr
Suggests: knitr, rmarkdown, tufte, testthat, vdiffr
Published: 2019-01-07
Author: Joses W. Ho [cre, aut], Tayfun Tumkaya [aut]
Maintainer: Joses W. Ho <joseshowh at gmail.com>
License: file LICENSE
NeedsCompilation: no
Citation: dabestr citation info
Materials: README
CRAN checks: dabestr results


Reference manual: dabestr.pdf
Vignettes: Bootstrap Confidence Intervals
Robust and Beautiful Statistical Visualization
Using dabestr
Package source: dabestr_0.2.0.tar.gz
Windows binaries: r-devel: dabestr_0.2.0.zip, r-release: dabestr_0.2.0.zip, r-oldrel: not available
OS X binaries: r-release: dabestr_0.2.0.tgz, r-oldrel: not available
Old sources: dabestr archive


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