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.
| Reverse depends: |
CVST, DRR, DTRlearn2, Iscores, kappalab, kebabs, kfda, KPC, PPInfer, svmpath |
| Reverse imports: |
ABPS, ADImpute, ampir, aweSOM, BKPC, BPRMeth, brainKCCA, branchpointer, calibrateBinary, CIDER, classmap, clusterExperiment, CondIndTests, CondiS, DA, DeLorean, DMTL, DynTxRegime, Ecume, finnts, fmf, fpc, fPortfolio, GeneGeneInteR, GeneralisedCovarianceMeasure, gkmSVM, GreedyExperimentalDesign, ITRLearn, kernelFactory, kernelPSI, KnowSeq, kpcalg, KRMM, ks, LDLcalc, MachineShop, microsynth, mikropml, mixtools, nlcv, oddstream, PCDimension, personalized, PLORN, plsRcox, PredCRG, pRoloc, qrjoint, QuESTr, REMP, RISCA, Rmagpie, rminer, robCompositions, ROI.plugin.ipop, rres, RSSL, scAnnotatR, scClassifR, scPCA, scRecover, soilassessment, STGS, survivalsvm, SVMMaj, SwarmSVM, Synth, tboot, tidysynth, tsensembler, TSGS, tsiR, wearables |
| Reverse suggests: |
BiodiversityR, breakDown, butcher, caret, caretEnsemble, colorspace, CompareCausalNetworks, condvis2, dials, diceR, dimRed, dismo, evclust, evtree, FactorsR, FCPS, fscaret, gamclass, GAparsimony, HPiP, iForecast, isotree, loon, microbiomeMarker, mistral, MLInterfaces, mlr, mlr3cluster, mlr3pipelines, mlrMBO, MLSeq, modeltime, MSCMT, parsnip, pdp, pmml, rattle, recipes, RLSeq, RStoolbox, sand, sdmApp, Semblance, shipunov, spectralGraphTopology, ssc, SSLR, stacks, SuperLearner, superMICE, supervisedPRIM, swag, tune, vcd |
| Reverse enhances: |
clue, prediction |