The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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
DOI: 10.32614/CRAN.package.dabestr
Author: Joses W. Ho ORCID iD [aut], Kah Seng Lian [aut], Zhuoyu Wang [aut], Jun Yang Liao [aut], Felicia Low [aut], Tayfun Tumkaya ORCID iD [aut], Yishan Mai ORCID iD [cre, ctb], Sangyu Xu ORCID iD [ctb], Hyungwon Choi ORCID iD [ctb], Adam Claridge-Chang ORCID iD [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:

Please use the canonical form https://CRAN.R-project.org/package=dabestr to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.