Package: KRLS 1.0-0

KRLS: Kernel-Based Regularized Least Squares

Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).

Authors:Jens Hainmueller Chad Hazlett

KRLS_1.0-0.tar.gz
KRLS_1.0-0.zip(r-4.5)KRLS_1.0-0.zip(r-4.4)KRLS_1.0-0.zip(r-4.3)
KRLS_1.0-0.tgz(r-4.4-any)KRLS_1.0-0.tgz(r-4.3-any)
KRLS_1.0-0.tar.gz(r-4.5-noble)KRLS_1.0-0.tar.gz(r-4.4-noble)
KRLS_1.0-0.tgz(r-4.4-emscripten)KRLS_1.0-0.tgz(r-4.3-emscripten)
KRLS.pdf |KRLS.html
KRLS/json (API)

# Install 'KRLS' in R:
install.packages('KRLS', repos = c('https://jankee2022.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.83 score 3 stars 45 scripts 284 downloads 5 mentions 7 exports 0 dependencies

Last updated 7 years agofrom:9ee4980f0f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 17 2024
R-4.5-winOKNov 17 2024
R-4.5-linuxOKNov 17 2024
R-4.4-winOKNov 17 2024
R-4.4-macOKNov 17 2024
R-4.3-winOKNov 17 2024
R-4.3-macOKNov 17 2024

Exports:gausskernelkrlsloolossplot.krlspredict.krlssolveforcsummary.krls

Dependencies: