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 (2018) .


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0.2.0 by Joses W. Ho, 14 days ago

Browse source code at

Authors: Joses W. Ho [cre, aut] , Tayfun Tumkaya [aut]

Documentation:   PDF Manual  

file LICENSE license

Imports cowplot, dplyr, ggplot2, forcats, ggforce, ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr

Depends on boot, magrittr

Suggests knitr, rmarkdown, tufte, testthat, vdiffr

See at CRAN