http://arxiv.org/abs/2106.10539
The univariate Kolmogorov-Smirnov (KS) test is a non-parametric statistical test designed to assess whether a set of data is consistent with a given probability distribution (or, in the two-sample case, whether the two samples come from the same underlying distribution). The versatility of the KS test has made it a cornerstone of statistical analysis and is commonly used across the scientific disciplines. However, the test proposed by Kolmogorov and Smirnov does not naturally extend to multidimensional distributions. Here, we present the fasano.franceschini.test package, an R implementation of the 2-D KS two-sample test as defined by Fasano and Franceschini (Fasano and Franceschini 1987). The fasano.franceschini.test package provides three improvements over the current 2-D KS test on the Comprehensive R Archive Network (CRAN): (i) the Fasano and Franceschini test has been shown to run in $O(n^2)$ versus the Peacock implementation which runs in $O(n^3)$; (ii) the package implements a procedure for handling ties in the data; and (iii) the package implements a parallelized bootstrapping procedure for improved significance testing. Ultimately, the fasano.franceschini.test package presents a robust statistical test for analyzing random samples defined in 2-dimensions.
E. Ness-Cohn and R. Braun
Tue, 22 Jun 21
71/71
Comments: 8 pages, 4 figures