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: 0.9-24
Depends: R (≥ 2.10)
Imports: methods, stats, grDevices, graphics
Published: 2016-03-29
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_0.9-24.tar.gz
Windows binaries: r-devel: kernlab_0.9-24.zip, r-release: kernlab_0.9-24.zip, r-oldrel: kernlab_0.9-24.zip
OS X Mavericks binaries: r-release: kernlab_0.9-24.tgz, r-oldrel: kernlab_0.9-24.tgz
Old sources: kernlab archive

Reverse dependencies:

Reverse depends: CVST, DTRlearn, kappalab, netClass, pathClass, probsvm, svmadmm, SVMMaj
Reverse imports: BKPC, DeLorean, fpc, fPortfolio, funcy, gkmSVM, kernelFactory, LinearizedSVR, pi0, plsRcox, qrjoint, rminer, SwarmSVM, Synth
Reverse suggests: BiodiversityR, caret, caretEnsemble, colorspace, CompareCausalNetworks, conformal, dismo, evtree, fscaret, gamclass, mistral, mlr, pmml, preprocomb, rattle, RStoolbox, sand, SPOT, vcd
Reverse enhances: clue