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: | 0.9-29 |
Depends: | R (≥ 2.10) |
Imports: | methods, stats, grDevices, graphics |
Published: | 2019-11-12 |
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 |
SystemRequirements: | C++11 |
Citation: | kernlab citation info |
In views: | Cluster, MachineLearning, Multivariate, NaturalLanguageProcessing, Optimization |
CRAN checks: | kernlab results |
Reference manual: | kernlab.pdf |
Vignettes: |
kernlab - An S4 Package for Kernel Methods in R |
Package source: | kernlab_0.9-29.tar.gz |
Windows binaries: | r-devel: kernlab_0.9-29.zip, r-release: kernlab_0.9-29.zip, r-oldrel: kernlab_0.9-29.zip |
macOS binaries: | r-release: kernlab_0.9-29.tgz, r-oldrel: kernlab_0.9-29.tgz |
Old sources: | kernlab archive |
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