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
Packaged: 2024-11-07 22:05:11 UTC; Max
Author: Qing Chang [aut],
  Max Goplerud [aut, cre]
Maintainer: Max Goplerud <mgoplerud@austin.utexas.edu>
Repository: CRAN
Date/Publication: 2024-11-07 22:30:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 07:47:31 UTC; unix
Archs: gKRLS.so.dSYM
