Package: gKRLS 1.0.3

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.

Authors:Qing Chang [aut], Max Goplerud [aut, cre]

gKRLS_1.0.3.tar.gz
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gKRLS.pdf |gKRLS.html✨
gKRLS/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/mgoplerud/gkrls/issues

On CRAN:

10 exports 7 stars 1.33 score 29 dependencies 4 scripts 214 downloads

Last updated 14 days agofrom:76c176b280. Checks:OK: 9. Indexed: yes.

Exports:add_bam_to_mlr3calculate_effectscalculate_interactionsget_calibration_informationget_individual_effectsgKRLSLearnerClassifBamLearnerRegrBamlegacy_marginal_effectSL.mgcv

Dependencies:backportscheckmatecodetoolsdata.tabledigestevaluatefuturefuture.applyglobalslatticelgrlistenvMatrixmgcvmlbenchmlr3mlr3measuresmlr3miscnlmepalmerpenguinsparadoxparallellyPRROCR6RcppRcppEigensandwichuuidzoo