<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Data Analysis using Bootstrap-Coupled Estimation</dc:title>
  <dc:title>R package dabestr version 2025.3.15</dc:title>
  <dc:description>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.  &lt;doi:10.1038/s41592-019-0470-3&gt;.  The
    free-to-view PDF is located at
    &lt;https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 2.10)</dc:relation>
  <dc:relation>Imports: boot, brunnermunzel, cli, cowplot, dplyr, effsize, ggbeeswarm,
ggplot2 (&gt;= 3.5.2), ggsci, grid, magrittr, RColorBrewer, rlang,
scales, stats, stringr, tibble, tidyr, viridisLite</dc:relation>
  <dc:relation>Suggests: kableExtra, knitr, rmarkdown, testthat (&gt;= 3.0.0), vdiffr</dc:relation>
  <dc:creator>Yishan Mai &lt;maiyishan@u.duke.nus.edu&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Joses W. Ho [aut] (ORCID: &lt;https://orcid.org/0000-0002-9186-6322&gt;),
  Kah Seng Lian [aut],
  Ana Rosa Castillo [aut],
  Zhuoyu Wang [aut],
  Jun Yang Liao [aut],
  Felicia Low [aut],
  Tayfun Tumkaya [aut] (ORCID: &lt;https://orcid.org/0000-0001-8425-3360&gt;),
  Jonathan Anns [ctb] (ORCID: &lt;https://orcid.org/0009-0005-8349-4986&gt;),
  Yishan Mai [cre, ctb] (ORCID: &lt;https://orcid.org/0000-0002-7199-380X&gt;),
  Sangyu Xu [ctb] (ORCID: &lt;https://orcid.org/0000-0002-4927-9204&gt;),
  Zinan Lu [ctb],
  Hyungwon Choi [ctb] (ORCID: &lt;https://orcid.org/0000-0002-6687-3088&gt;),
  Adam Claridge-Chang [ctb] (ORCID:
    &lt;https://orcid.org/0000-0002-4583-3650&gt;),
  ACCLAB [cph, fnd]</dc:contributor>
  <dc:rights>Apache License (&gt;= 2)</dc:rights>
  <dc:date>2025-10-30</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=dabestr</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.dabestr</dc:identifier>
</oai_dc:dc>
