hyppo: A Multivariate Hypothesis Testing Python Package

Abstract

We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. hyppo includes many state of the art multivariate testing procedures. The package is easy-to-use and is flexible enough to enable future extensions. The documentation and all releases are available at https://hyppo.neurodata.io.

References in corpus (9)

• Brownian distance covariance
• Kernel-based Conditional Independence Test and Application in Causal Discovery
• DISCO analysis: A nonparametric extension of analysis of variance
• The Chi-Square Test of Distance Correlation
• Fast Two-Sample Testing with Analytic Representations of Probability Measures
• Statistical Analysis of Data Repeatability Measures
• Universally Consistent K-Sample Tests via Dependence Measures
• Learning Interpretable Characteristic Kernels via Decision Forests
• High-Dimensional Independence Testing via Maximum and Average Distance Correlations

Cited by in corpus (3)

• Universally Consistent K-Sample Tests via Dependence Measures
• Multiscale Comparative Connectomics
• The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue -- I. A golden thread through time and space