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 (2024)
<doi:10.1017/pan.2023.27> provide further details.
| Version: |
1.0.4 |
| Depends: |
mgcv, sandwich (≥ 2.4.0) |
| Imports: |
Rcpp (≥ 1.0.6), Matrix, mlr3, R6 |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
SuperLearner, mlr3misc, DoubleML, testthat |
| Published: |
2024-11-07 |
| DOI: |
10.32614/CRAN.package.gKRLS |
| Author: |
Qing Chang [aut],
Max Goplerud [aut, cre] |
| Maintainer: |
Max Goplerud <mgoplerud at austin.utexas.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 |
| In views: |
MachineLearning |
| CRAN checks: |
gKRLS results |
Documentation:
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Reverse dependencies:
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