Package: KRLS
Type: Package
Title: Kernel-Based Regularized Least Squares
Version: 1.7-0
Date: 2026-06-04
Authors@R: c(
    person("Jens", "Hainmueller", email = "jhain@stanford.edu", role = c("aut", "cre")),
    person("Chad", "Hazlett", role = "aut"))
Description: 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>).
License: GPL (>= 2)
Imports: grDevices, graphics, stats
Suggests: lattice, testthat (>= 3.0.0), knitr, rmarkdown, ggplot2,
        generics
Config/testthat/edition: 3
VignetteBuilder: knitr
URL: https://web.stanford.edu/~jhain/, https://github.com/j-hai/KRLS
BugReports: https://github.com/j-hai/KRLS/issues
Encoding: UTF-8
Repository: https://j-hai.r-universe.dev
Date/Publication: 2026-06-05 15:34:11 UTC
RemoteUrl: https://github.com/j-hai/krls
RemoteRef: HEAD
RemoteSha: bffb5c229fa1b81d9a26665511ef3aa8fce4abcc
NeedsCompilation: no
Packaged: 2026-06-05 19:54:04 UTC; root
Author: Jens Hainmueller [aut, cre],
  Chad Hazlett [aut]
Maintainer: Jens Hainmueller <jhain@stanford.edu>