A collection of bias correction techniques written in Python - for climate sciences.

A Python package for the statistical analysis of A/B tests.

This module includes Item Statistics, Model fit, Differential Item Functioning, Wright Map, Expected Scores Curve,and Item Characteristic Curve for DIF using MML estimation of the Rasch measurement model. Furthermore you can analyze DIF, Distractor analysis and Many facet Rasch model.

Bias correction command-line tool for climatic research written in C++

Educational projects on online experiments

pydisagg is a Python package for disaggregating estimated count observations across groups under generalized proportionality assumptions.

Compares several ways for calculating confidence interval (CI) of the relative difference of two sample means

Performmance/execution time test of the bias correction tool BiasAdjustCXX v1.8 in comparison to python-cmethods v0.6.1 and xclim v0.40.0.

Calculates standard errors and confidence intervals for effects in continuous-time mediation models.

Uncertainty or Standard Error of Mean propagation using Delta and Monte Carlo methods

Sample-Size Calculations for A/B Testing using Delta method

Illustrate the delta-method