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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++
A Python package for statistical analysis of A/B tests.
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
Calculates standard errors and confidence intervals for effects in continuous time mediation models.
Sample-Size Calculations for A/B Testing using Delta method
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.