A Python package for the statistical analysis of A/B tests.
-
Updated
Sep 15, 2024 - Python
A Python package for the statistical analysis of A/B tests.
A collection of bias correction techniques written in Python - for climate sciences.
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++
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
Uncertainty or Standard Error of Mean propagation using Delta and Monte Carlo methods
Educational projects on online experiments
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.
Sample-Size Calculations for A/B Testing using Delta method
Add a description, image, and links to the delta-method topic page so that developers can more easily learn about it.
To associate your repository with the delta-method topic, visit your repo's landing page and select "manage topics."