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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:9ee4980f0f. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 17 2024 |
R-4.5-win | OK | Nov 17 2024 |
R-4.5-linux | OK | Nov 17 2024 |
R-4.4-win | OK | Nov 17 2024 |
R-4.4-mac | OK | Nov 17 2024 |
R-4.3-win | OK | Nov 17 2024 |
R-4.3-mac | OK | Nov 17 2024 |
Exports:gausskernelkrlsloolossplot.krlspredict.krlssolveforcsummary.krls
Dependencies: