2021/06/19 by Puritz, Connor, Ness-Cohn, Elan, Braun, Rosemary
#Computation (stat.CO) #Data Analysis #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Methodology (stat.ME) #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2106.10539
The Kolmogorov-Smirnov (KS) test is a nonparametric statistical test used to test for differences between univariate probability distributions. The versatility of the KS test has made it a cornerstone of statistical analysis across many scientific disciplines. However, the test proposed by Kolmogorov and Smirnov does not easily extend to multidimensional distributions. Here we present the fasano.franceschini.test package, an R implementation of a multidimensional two-sample KS test described by Fasano and Franceschini (1987). The fasano.franceschini.test package provides a test that is computationally efficient, applicable to data of any dimension and type (continuous, discrete, or mixed), and that performs competitively with similar R packages.