CRAN
dabestr 0.2.1
Data Analysis using Bootstrap-Coupled Estimation
Released Jun 26, 2019 by Joses W. Ho
Dependencies
tidyr 0.8.3 forcats 0.4.0 cowplot 0.9.4 ggforce 0.2.2 ggplot2 3.2.0 tibble 2.1.3 dplyr 0.8.2 magrittr 1.5 stringr 1.4.0 rlang 0.4.0 simpleboot 1.1-7 boot 1.3-22 ggbeeswarm 0.6.0
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.