PyRKHSstats 1.2.0

A Python package for kernel methods in Statistics/ML.

PyRKHSstats

A Python package implementing a variety of statistical/machine learning methods that rely on kernels (e.g. HSIC for independence testing).

Implemented

• Independence testing with HSIC (Hilbert-Schmidt Independence Criterion) using the Gamma approximation, as introduced in A Kernel Statistical Test of Independence, A. Gretton, K. Fukumizu, C. Hui Teo, L. Song, B. Scholkopf, and A. J. Smola (NIPS 2007).
• Measurement of conditional independence with HSCIC (Hilbert-Schmidt Conditional Independence Criterion), as introduced in A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings, J. Park and K. Muandet (NeurIPS 2020).
• The Kernel-based Conditional Independence Test (KCIT), as introduced in Kernel-based Conditional Independence Test and Application in Causal Discovery, K. Zhang, J. Peters, D. Janzing, B. Scholkopf (UAI 2011).

Resource	Description	Numpy based available	PyTorch based available
HSIC	For independence testing	Yes	No
HSCIC	For the measurement of conditional independence	Yes	Yes
KCIT	For conditional independence testing	Yes	No

HSIC

Implementations provided :

• Gamma approximation based.

KCIT

Implementations provided :

• Gamma approximation based,
• Monte Carlo simulation based.

In development

• Two-sample testing with MMD.
• Goodness-of-fit testing.
• Methods for time series models.
• Bayesian statistical kernel methods.