Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R

Junhao Gao; Ery Arias-Castro

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arXiv · 2608.26450

Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R

Junhao Gao·Ery Arias-Castro

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Abstract

Stein's method constructs computable discrepancies between a target distribution and a candidate distribution without requiring the target distribution's normalizing constant. These discrepancies support goodness-of-fit tests for model assessment as well as sampling tools for empirical approximation. The R package steinsampling provides the first unified R workflow for applying score-based Stein methods to kernel goodness-of-fit testing of independent or serially dependent observations, point transport, greedy point construction, and sample compression. High-level functions carry out each task in a single call, while the kernel, calibration, optimization, and transition components are provided separately so that users can replace any one of them. A single score and kernel setup can therefore be reused across sampling and testing, making these methods easier to reproduce, compare, and extend.

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Junhao Gao, Ery Arias-Castro. 2026-08-26. Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R. https://arxiv.org/abs/2608.26450

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