Package: gKRLS 1.0.4

Max Goplerud

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_1.0.4.tar.gz
gKRLS_1.0.4.tar.gz(r-4.5-noble)gKRLS_1.0.4.tar.gz(r-4.4-noble)
gKRLS_1.0.4.tgz(r-4.4-emscripten)gKRLS_1.0.4.tgz(r-4.3-emscripten)
gKRLS.pdf |gKRLS.html
gKRLS/json (API)
NEWS

# Install 'gKRLS' in R:
install.packages('gKRLS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

Uses libs:
  • c++– GNU Standard C++ Library v3

cpp

2.00 score 4 scripts 664 downloads 10 exports 29 dependencies

Last updated 1 months agofrom:cfad3463ca. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 08 2024
R-4.5-linux-x86_64OKDec 08 2024

Exports:add_bam_to_mlr3calculate_effectscalculate_interactionsget_calibration_informationget_individual_effectsgKRLSLearnerClassifBamLearnerRegrBamlegacy_marginal_effectSL.mgcv

Dependencies:backportscheckmatecodetoolsdata.tabledigestevaluatefuturefuture.applyglobalslatticelgrlistenvMatrixmgcvmlbenchmlr3mlr3measuresmlr3miscnlmepalmerpenguinsparadoxparallellyPRROCR6RcppRcppEigensandwichuuidzoo

Readme and manuals

Help Manual

Help pageTopics
Marginal Effectscalculate_effects calculate_interactions get_individual_effects print.gKRLS_mfx summary.gKRLS_mfx
Generalized Kernel Regularized Least Squaresget_calibration_information gKRLS
Machine Learning with gKRLSadd_bam_to_mlr3 ml_gKRLS predict.SL.mgcv SL.mgcv