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Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
Version: | 0.9-33 |
Depends: | R (≥ 2.10) |
Imports: | methods, stats, grDevices, graphics |
Published: | 2024-08-13 |
DOI: | 10.32614/CRAN.package.kernlab |
Author: | Alexandros Karatzoglou [aut, cre], Alex Smola [aut], Kurt Hornik [aut], National ICT Australia (NICTA) [cph], Michael A. Maniscalco [ctb, cph], Choon Hui Teo [ctb] |
Maintainer: | Alexandros Karatzoglou <alexandros.karatzoglou at gmail.com> |
License: | GPL-2 |
Copyright: | see file COPYRIGHTS |
NeedsCompilation: | yes |
Citation: | kernlab citation info |
In views: | Cluster, MachineLearning, NaturalLanguageProcessing, Optimization |
CRAN checks: | kernlab results |
Reference manual: | kernlab.pdf |
Vignettes: |
kernlab - An S4 Package for Kernel Methods in R (source, R code) |
Package source: | kernlab_0.9-33.tar.gz |
Windows binaries: | r-devel: kernlab_0.9-33.zip, r-release: kernlab_0.9-33.zip, r-oldrel: kernlab_0.9-33.zip |
macOS binaries: | r-release (arm64): kernlab_0.9-33.tgz, r-oldrel (arm64): kernlab_0.9-33.tgz, r-release (x86_64): kernlab_0.9-33.tgz, r-oldrel (x86_64): kernlab_0.9-33.tgz |
Old sources: | kernlab archive |
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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.