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: 0.9-25
Depends: R (≥ 2.10)
Imports: methods, stats, grDevices, graphics
Published: 2016-10-03
Author: Alexandros Karatzoglou [aut, cre], Alex Smola [aut], Kurt Hornik [aut]
Maintainer: Alexandros Karatzoglou <alexandros.karatzoglou 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


Reference manual: kernlab.pdf
Vignettes: kernlab - An S4 Package for Kernel Methods in R
Package source: kernlab_0.9-25.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: kernlab_0.9-25.tgz, r-oldrel: kernlab_0.9-25.tgz
Old sources: kernlab archive

Reverse dependencies:

Reverse depends: CVST, DRR, DTRlearn, exprso, kappalab, kfda, pathClass, probsvm, RaPKod, svmadmm
Reverse imports: ABPS, BKPC, brainKCCA, CondIndTests, DeLorean, diceR, DynTxRegime, fpc, fPortfolio, funcy, gkmSVM, kernelFactory, kpcalg, KRMM, ks, nlcv, PCDimension, personalized, plsRcox, qrjoint, qrsvm, rminer, robCompositions, ROI.plugin.ipop, rres, RSSL, survivalsvm, SVMMaj, SwarmSVM, Synth, tsensembler, tsiR, VRPM
Reverse suggests: BiodiversityR, breakDown, bWGR, caret, caretEnsemble, colorspace, CompareCausalNetworks, dimRed, dismo, evtree, FactorsR, fscaret, gamclass, GAparsimony, loon, mistral, mlr, mlrMBO, MSCMT, pdp, pmml, preprocomb, rattle, recipes, RStoolbox, sand, ssc, SuperLearner, supervisedPRIM, vcd
Reverse enhances: clue, prediction


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