Enhancing {ggplot2} plots with statistical analysis
Statistical package in Python based on Pandas
This repository contains analysis of churn in telephone service company (using IV and WOE), comparison of effect size and information value and quick tutorial how to use information value module (created for this analysis).
The Scott-Knott Effect Size Difference (ESD) test
Collection of Matlab functions for the computation of measures of effect size
A Python package for computing effect sizes
An R package for visualizing comparison between two distributions.
Calculating robust effect sizes using bootstrap (resampling) technique in R.
Effect size measures
This repo is no longer being maintained. See this repo instead: https://github.com/hauselin/esconvert
Estimation Approach to Statistical Inference
Interpret effects and visualise uncertainty
Identifying and avoiding common misinterpretations in using statistics
Two sample data analysis method that tests for negligible and meaningful effect sizes (through difference in means)
This code is an implementation of the A statistic, otherwise known as the probability of superiority, in SAS. The A statistic is a non-parametric form of the common language effect-size. Both it and its counterpart, RProbSup, are available at the website linked below.
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