kernlab: Kernel-based Machine Learning Lab

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: 0.9-19
Depends: R (≥ 2.10), methods
Published: 2013-11-03
Author: Alexandros Karatzoglou [aut, cre], Alex Smola [aut], Kurt Hornik [aut]
Maintainer: Alexandros Karatzoglou <alexis at ci.tuwien.ac.at>
License: GPL-2
Copyright: see file COPYRIGHTS
NeedsCompilation: yes
Citation: kernlab citation info
In views: Cluster, MachineLearning, Multivariate, NaturalLanguageProcessing, Optimization
CRAN checks: kernlab results

Downloads:

Reference manual: kernlab.pdf
Vignettes: kernlab - An S4 Package for Kernel Methods in R
Package source: kernlab_0.9-19.tar.gz
Windows binaries: r-devel: kernlab_0.9-19.zip, r-release: kernlab_0.9-19.zip, r-oldrel: kernlab_0.9-19.zip
OS X Snow Leopard binaries: r-release: kernlab_0.9-19.tgz, r-oldrel: kernlab_0.9-19.tgz
OS X Mavericks binaries: r-release: kernlab_0.9-19.tgz
Old sources: kernlab archive

Reverse dependencies:

Reverse depends: CVST, kappalab, MVpower, netClass, pathClass, probsvm, SVMMaj
Reverse imports: bmrm, COPASutils, kernelFactory, LinearizedSVR, mistral, pi0, plsRcox, rminer, Synth
Reverse suggests: apcluster, BiodiversityR, caret, colorspace, dismo, evtree, fpc, fscaret, gamclass, mlr, pmml, rattle, sand, SPOT, vcd
Reverse enhances: clue