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) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("dabestr")

0.2.0 by Joses W. Ho, 14 days ago


Browse source code at https://github.com/cran/dabestr


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