Package: kerntools 1.1.0

kerntools: Kernel Functions and Tools for Machine Learning Applications

Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features' importance for Support Vector Machines (SVMs), which expand other 'R' packages like 'kernlab'.

Authors:Elies Ramon [aut, cre, cph]

kerntools_1.1.0.tar.gz
kerntools_1.1.0.zip(r-4.5)kerntools_1.1.0.zip(r-4.4)kerntools_1.1.0.zip(r-4.3)
kerntools_1.1.0.tgz(r-4.4-any)kerntools_1.1.0.tgz(r-4.3-any)
kerntools_1.1.0.tar.gz(r-4.5-noble)kerntools_1.1.0.tar.gz(r-4.4-noble)
kerntools_1.1.0.tgz(r-4.4-emscripten)kerntools_1.1.0.tgz(r-4.3-emscripten)
kerntools.pdf |kerntools.html
kerntools/json (API)
NEWS

# Install 'kerntools' in R:
install.packages('kerntools', repos = c('https://elies-ramon.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/elies-ramon/kerntools/issues

Datasets:

On CRAN:

4.48 score 9 scripts 156 downloads 43 exports 37 dependencies

Last updated 24 days agofrom:2eb04a432f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winOKOct 25 2024
R-4.5-linuxOKOct 25 2024
R-4.4-winOKOct 25 2024
R-4.4-macOKOct 25 2024
R-4.3-winOKOct 25 2024
R-4.3-macOKOct 25 2024

Exports:AccAcc_rndBoots_CIBrayCurtiscenterKcenterXcosNormcosnormXdesparsifyDiracdummy_datadummy_varestimate_gammaF1FrobeniusfrobNormheatKhistKIntersectJaccardKendallkPCAkPCA_arrowskPCA_impKTALaplaceLinearminmaxMKCnmseNormal_CIplotImpPrecProcrustesRBFRecRuzickasimKSpeSpectrumsvm_impTSSvonNeumann

Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandkernlablabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppreshape2rlangscalesstringistringrtibbletidyselectutf8vctrsviridisLitewithr

Kernel functions

Rendered fromKernel-functions.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-10-25
Started: 2024-10-25

Kernel PCA and Coinertia

Rendered fromKernel-PCA-and-CIA.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-10-25
Started: 2024-10-25

kerntools: R tools for kernel methods

Rendered fromkerntools.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-09-11
Started: 2024-09-11

Readme and manuals

Help Manual

Help pageTopics
AccuracyAcc
Accuracy of a random modelAcc_rnd
Confidence Interval using BootstrapBoots_CI
Kernels for count dataBrayCurtis Ruzicka
Centering a kernel matrixcenterK
Centering a squared matrix by row or columncenterX
Cosine normalization of a kernel matrixcosNorm
Cosine normalization of a matrixcosnormX
This function deletes those columns and/or rows in a matrix/data.frame that only contain 0s.desparsify
Kernels for categorical variablesDirac
Convert categorical data to dummies.dummy_data
Levels per factor variabledummy_var
Gamma hyperparameter estimation (RBF kernel)estimate_gamma
F1 scoreF1
Frobenius kernelFrobenius
Frobenius normalizationfrobNorm
Kernel matrix heatmapheatK
Kernel matrix histogramhistK
Kernels for setsIntersect Jaccard
Kendall's tau kernelKendall
Kernel PCAkPCA
Plot the original variables' contribution to a PCA plotkPCA_arrows
Contributions of the variables to the Principal Components ("loadings")kPCA_imp
Kernel-target alignmentKTA
Laplacian kernelLaplace
Linear kernelLinear
Minmax normalizationminmax
Multiple Kernel (Matrices) CombinationMKC
NMSE (Normalized Mean Squared Error)nmse
Confidence Interval using Normal ApproximationNormal_CI
Importance barplotplotImp
Precision or PPVPrec
Procrustes AnalysisProcrustes
Gaussian RBF (Radial Basis Function) kernelRBF
Recall or Sensitivity or TPRRec
Showdatashowdata
Kernel matrix similaritysimK
Soil microbiota (raw counts)soil
Specificity or TNRSpe
Spectrum kernelSpectrum
SVM feature importancesvm_imp
Total Sum ScalingTSS
Von Neumann entropyvonNeumann