An Exact Test for a Change in Covariance (Dependence) Structure R package regstat version 0.1.0 An exact finite-sample test for whether two groups share a covariance matrix, the omnibus form of the differential-network question. Under the Gaussian null the likelihood-ratio statistic has a distribution given by the real Jacobi ensemble that is free of the unknown common covariance, so a single Monte-Carlo calibration at the identity serves every covariance with no estimate of the nuisance covariance; this is the property that survives the dimension barrier, where estimating the covariance is hardest. The max-type high-dimensional test of Cai, Liu and Xia (2013) <doi:10.1080/01621459.2012.758041> is provided for comparison. A pure-C back-end does the numerics and also backs the 'Python' package 'regstat'. Software Imports: stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0) M. Theodor Loots <theo.loots@gmail.com> Comprehensive R Archive Network (CRAN) M. Theodor Loots [aut, cre] (ORCID: <https://orcid.org/0000-0001-7722-3913>) GPL-3 2026-09-24 application/tgz https://CRAN.R-project.org/package=regstat doi:10.32614/CRAN.package.regstat