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dabestr is a package forDataAnalysis usingBootstrap-CoupledESTimation.

Estimationstatisticsis asimpleframeworkthat avoids thepitfallsof significance testing. It uses familiar statistical concepts: means,mean differences, and error bars. More importantly, it focuses on theeffect size of one’s experiment/intervention, as opposed to a falsedichotomy engendered byP values.

An estimation plot has two key features.

1. Itpresents all datapoints as a swarmplot, which orders eachpoint to display the underlying distribution.

2. It presents theeffect size as abootstrap 95% confidenceinterval on aseparate but aligned axes.

Thedabestr package powersestimationstats.com, allowing everyoneaccess to high-quality estimation plots.

# Install it from CRANinstall.packages("dabestr")# Or the development version from GitHub:# install.packages("devtools")devtools::install_github(repo="ACCLAB/dabestr",ref="dev")

library(dabestr)

data("non_proportional_data")dabest_obj.mean_diff<- load(data=non_proportional_data,x=Group,y=Measurement,idx= c("Control 1","Test 1")) %>%  mean_diff()dabest_plot(dabest_obj.mean_diff,TRUE)

Please refer to the officialtutorialfor more useful code snippets.

Moving beyond P values: Everyday data analysis with estimation plots

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, AdamClaridge-Chang

Nature Methods 2019, 1548-7105.10.1038/s41592-019-0470-3

Paywalled publishersite;Free-to-viewPDF

Please report any bugs on theGithub issuetracker.

All contributions are welcome; please read theGuidelines forcontributingfirst.

We also have aCode ofConductto foster an inclusive and productive space.

We would like to thank alpha testers from theClaridge-Changlab:SangyuXu,XianyuanZhang,FarhanMohammad, Jurga Mituzaitė, andStanislav Ott.

DABEST is also available in Python(DABEST-python)and Matlab(DABEST-Matlab).