Statsmodels: statistical modeling and econometrics in Python
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Updated
May 30, 2024 - Python
Statsmodels: statistical modeling and econometrics in Python
A light-weight, flexible, and expressive statistical data testing library
Python package for multivariate hypothesis testing
Learning kernels to maximize the power of MMD tests
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
Multiple hypothesis testing in Python
Enabling easy statistical significance testing for deep neural networks.
This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
Hypothesis and statistical testing in Python
Python tools for working with the IceCube public data.
Generic goodness of fit tests for random plain old data
Grammars suitable for lark parser and Hypothesis
Test the phenomenon of Stroop Effect
pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets
[TNNLS 2022] Significance tests of feature relevance for a black-box learner
One-Dimensional Random Field Theory in Python
NFT Markertplace written in Vyper
Statistical Counterexample Detector for Differentially Private Mechanisms
A web application to design and evaluate the results of A/B tests.
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