dabestr is a package for 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.

Your version of R must be 3.5.0 or higher.

install.packages("dabestr")

# To install the latest development version on Github,
# use the line below.
devtools::install_github("ACCLAB/dabestr")

library(dabestr)

# Performing unpaired (two independent groups) analysis.
unpaired_mean_diff <- dabest(iris, Species, Petal.Width,
                             idx = c("setosa", "versicolor", "virginica"),
                             paired = FALSE)

# Display the results in a user-friendly format.
unpaired_mean_diff
#> DABEST (Data Analysis with Bootstrap Estimation) v0.2.2
#> =======================================================
#> 
#> Variable: Petal.Width 
#> 
#> Unpaired mean difference of versicolor (n=50) minus setosa (n=50)
#>  1.08 [95CI  1.01; 1.14]
#> 
#> Unpaired mean difference of virginica (n=50) minus setosa (n=50)
#>  1.78 [95CI  1.69; 1.85]
#> 
#> 
#> 5000 bootstrap resamples.
#> All confidence intervals are bias-corrected and accelerated.

# Produce a Cumming estimation plot.
plot(unpaired_mean_diff)

You will find more useful code snippets in this vignette.

Moving beyond P values: Everyday data analysis with estimation plots

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

Nature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3

Paywalled publisher site; Free-to-view PDF

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dabestr is also available in Python and Matlab.