dabestr (0.2.2)

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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. . The free-to-view PDF is located at .

Maintainer: Joses W. Ho
Author(s): Joses W. Ho [cre, aut], Tayfun Tumkaya [aut]

License: file LICENSE

Uses: boot, cowplot, dplyr, ellipsis, forcats, ggbeeswarm, ggforce, ggplot2, magrittr, rlang, simpleboot, stringr, tibble, tidyr, testthat, knitr, rmarkdown, tufte, vdiffr

Released 4 months ago.

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