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DESCRIPTION
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DESCRIPTION
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Package: gKRLS
Type: Package
Title: Generalized Kernel Regularized Least Squares
Version: 1.0.2
Date: 2023-4-17
Encoding: UTF-8
Authors@R: c(
person("Qing", "Chang", role = c("aut"), email = "qic47@pitt.edu"),
person("Max", "Goplerud", role = c("aut", "cre"), email = "mgoplerud@pitt.edu"))
License: GPL (>= 2)
Description: 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)
<arXiv:2209.14355> provide further details.
LinkingTo: Rcpp, RcppEigen
Imports:
Rcpp (>= 1.0.6),
Matrix,
mlr3,
R6
Depends:
mgcv, sandwich (>= 2.4.0)
Suggests:
SuperLearner,
mlr3misc,
DoubleML,
testthat
SystemRequirements: GNU make
RoxygenNote: 7.2.3
NeedsCompilation: yes
URL: https://github.com/mgoplerud/gKRLS
BugReports: https://github.com/mgoplerud/gKRLS/issues