Package: gKRLS 1.0.4
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:
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')) |
Bug tracker:https://github.com/mgoplerud/gkrls/issues
Last updated 1 months agofrom:cfad3463ca. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 08 2024 |
R-4.5-linux-x86_64 | OK | Dec 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 page | Topics |
---|---|
Marginal Effects | calculate_effects calculate_interactions get_individual_effects print.gKRLS_mfx summary.gKRLS_mfx |
Generalized Kernel Regularized Least Squares | get_calibration_information gKRLS |
Machine Learning with gKRLS | add_bam_to_mlr3 ml_gKRLS predict.SL.mgcv SL.mgcv |