The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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) <doi:10.48550/arXiv.2209.14355> provide further details.
| Version: | 1.0.2 |
| Depends: | mgcv, sandwich (≥ 2.4.0) |
| Imports: | Rcpp (≥ 1.0.6), Matrix, mlr3, R6 |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | SuperLearner, mlr3misc, DoubleML, testthat |
| Published: | 2023-04-20 |
| Author: | Qing Chang [aut], Max Goplerud [aut, cre] |
| Maintainer: | Max Goplerud <mgoplerud at pitt.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 |
| CRAN checks: | gKRLS results |
| Reference manual: | gKRLS.pdf |
| Package source: | gKRLS_1.0.2.tar.gz |
| Windows binaries: | r-devel: gKRLS_1.0.2.zip, r-release: gKRLS_1.0.2.zip, r-oldrel: gKRLS_1.0.2.zip |
| macOS binaries: | r-release (arm64): gKRLS_1.0.2.tgz, r-oldrel (arm64): gKRLS_1.0.2.tgz, r-release (x86_64): gKRLS_1.0.2.tgz, r-oldrel (x86_64): gKRLS_1.0.2.tgz |
| Old sources: | gKRLS archive |
Please use the canonical form https://CRAN.R-project.org/package=gKRLS to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.