gKRLS: Generalized Kernel Regularized Least Squares

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 (2023) <doi:10.48550/arXiv.2209.14355> provide further details.

Version: 1.0.2
Depends: mgcv, sandwich (≥ 2.4.0)
Imports: Rcpp (≥ 1.0.6), Matrix, mlr3, R6
LinkingTo: Rcpp, RcppEigen
Suggests: SuperLearner, mlr3misc, DoubleML, testthat
Published: 2023-04-20
Author: Qing Chang [aut], Max Goplerud [aut, cre]
Maintainer: Max Goplerud <mgoplerud at pitt.edu>
BugReports: https://github.com/mgoplerud/gKRLS/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/mgoplerud/gKRLS
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: gKRLS results

Documentation:

Reference manual: gKRLS.pdf

Downloads:

Package source: gKRLS_1.0.2.tar.gz
Windows binaries: r-devel: gKRLS_1.0.2.zip, r-release: gKRLS_1.0.2.zip, r-oldrel: gKRLS_1.0.2.zip
macOS binaries: r-release (arm64): gKRLS_1.0.2.tgz, r-oldrel (arm64): gKRLS_1.0.2.tgz, r-release (x86_64): gKRLS_1.0.2.tgz
Old sources: gKRLS archive

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