Kernel-Based Regularized Least Squares R package KRLS version 1.7-0 Implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y = f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014, <doi:10.1093/pan/mpt019>). Software Imports: grDevices, graphics, stats Suggests: lattice, testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, generics Jens Hainmueller <jhain@stanford.edu> Comprehensive R Archive Network (CRAN) Jens Hainmueller [aut, cre], Chad Hazlett [aut] GPL (>= 2) 2026-06-05 application/tgz https://CRAN.R-project.org/package=KRLS doi:10.32614/CRAN.package.KRLS