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: dabestr_0.2.0.zip |
OS X binaries: | r-release: dabestr_0.2.0.tgz, r-oldrel: dabestr_0.2.0.tgz |
Old sources: | dabestr archive |
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