Package: gKRLS
Type: Package
Title: Generalized Kernel Regularized Least Squares
Version: 1.0.4
Date: 2024-11-07
Encoding: UTF-8
Authors@R: c(
  person("Qing", "Chang", role = c("aut"), email = "qic108@pitt.edu"),
  person("Max", "Goplerud", role = c("aut", "cre"), email = "mgoplerud@austin.utexas.edu"))
License: GPL (>= 2)
Description: Kernel regularized least squares, also known as kernel
        ridge regression, is a flexible machine learning method. This
        package implements this method by providing a smooth term for
        use with 'mgcv' and uses random sketching to facilitate
        scalable estimation on large datasets. It provides additional
        functions for calculating marginal effects after estimation and
        for use with ensembles ('SuperLearning'), double/debiased
        machine learning ('DoubleML'), and robust/clustered standard
        errors ('sandwich'). Chang and Goplerud (2024)
        <doi:10.1017/pan.2023.27> provide further details.
LinkingTo: Rcpp, RcppEigen
Imports: Rcpp (>= 1.0.6), Matrix, mlr3, R6
Depends: mgcv, sandwich (>= 2.4.0)
Suggests: SuperLearner, mlr3misc, DoubleML, testthat
SystemRequirements: GNU make
RoxygenNote: 7.3.2
NeedsCompilation: yes
URL: https://github.com/mgoplerud/gKRLS
BugReports: https://github.com/mgoplerud/gKRLS/issues
Config/pak/sysreqs: make
Repository: https://mgoplerud.r-universe.dev
Date/Publication: 2024-11-07 22:04:39 UTC
RemoteUrl: https://github.com/mgoplerud/gkrls
RemoteRef: HEAD
RemoteSha: 3de6cac86fe8b72771146ed2ea9d5b05f30d0ccd
Packaged: 2026-07-25 09:42:32 UTC; root
Author: Qing Chang [aut],
  Max Goplerud [aut, cre]
Maintainer: Max Goplerud <mgoplerud@austin.utexas.edu>