Randomized Matrix Computations from Few Entries and Products R package matsketch version 0.1.0 Implements recent randomized algorithms that answer questions about a large positive-semidefinite matrix while touching only a small part of it. Randomly pivoted Cholesky builds a low-rank approximation from a few rows of a kernel matrix (Chen, Epperly, Tropp and Webber (2025) <doi:10.1002/cpa.22234>), with an accelerated variant based on rejection sampling (Epperly, Tropp and Webber (2025) <doi:10.1137/24m1699048>). The XTrace, XNysTrace and XDiag estimators recover the trace and diagonal of a matrix that is available only through matrix-vector products (Epperly, Tropp and Webber (2024) <doi:10.1137/23m1548323>), alongside the Hutch++ estimator of Meyer, Musco, Musco and Woodruff (2021) <doi:10.1137/1.9781611976496.16>. Randomized Nystrom preconditioning speeds up the conjugate gradient method for regularized linear systems (Frangella, Tropp and Udell (2023) <doi:10.1137/21m1466244>). These pieces are combined to fit restricted maximum likelihood variance-component models on genomic relationship matrices without forming or factorizing the covariance matrix. Software Depends: R (>= 4.1.0) Imports: graphics, grDevices, stats Suggests: knitr, Matrix, rmarkdown, testthat (>= 3.0.0) Muhammad Farooqi <mqfarooqi@gmail.com> Comprehensive R Archive Network (CRAN) Muhammad Farooqi [aut, cre] (ORCID: <https://orcid.org/0000-0003-4918-9791>) MIT + file LICENSE (https://CRAN.R-project.org/package=matsketch/LICENSE) 2026-09-21 application/tgz https://CRAN.R-project.org/package=matsketch doi:10.32614/CRAN.package.matsketch en-GB