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://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

Version:	2023.9.12
Depends:	R (≥ 2.10)
Imports:	ggplot2, cowplot, tidyr, dplyr, tibble, rlang, magrittr, ggbeeswarm, effsize, grid, scales, ggsci, cli, boot, stats, stringr, brunnermunzel, methods
Suggests:	testthat (≥ 3.0.0), vdiffr, knitr, rmarkdown, kableExtra
Published:	2023-10-13
Author:	Joses W. Ho [aut], Kah Seng Lian [aut], Zhuoyu Wang [aut], Jun Yang Liao [aut], Felicia Low [aut], Tayfun Tumkaya [aut], Yishan Mai [cre, ctb], Sangyu Xu [ctb], Hyungwon Choi [ctb], Adam Claridge-Chang [ctb], ACCLAB [cph, fnd]
Maintainer:	Yishan Mai <maiyishan at u.duke.nus.edu>
License:	Apache License (≥ 2)
URL:	https://github.com/ACCLAB/dabestr, https://acclab.github.io/dabestr/
NeedsCompilation:	no
Citation:	dabestr citation info
Materials:	README NEWS
CRAN checks:	dabestr results

Documentation:

Reference manual:	dabestr.pdf
Vignettes:	Sample Datasets Controlling Plot Aesthetics Tutorial: Basics Tutorial: Delta-Delta Tutorial: Mini-Meta Delta Tutorial: Proportion Plots Tutorial: Repeated Measures

Downloads:

Package source:	dabestr_2023.9.12.tar.gz
Windows binaries:	r-devel: dabestr_2023.9.12.zip, r-release: dabestr_2023.9.12.zip, r-oldrel: dabestr_2023.9.12.zip
macOS binaries:	r-release (arm64): dabestr_2023.9.12.tgz, r-oldrel (arm64): dabestr_2023.9.12.tgz, r-release (x86_64): dabestr_2023.9.12.tgz, r-oldrel (x86_64): dabestr_2023.9.12.tgz
Old sources:	dabestr archive

Reverse dependencies:

Reverse imports:

permubiome

Linking:

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