R: Kernel Functions and Tools for Machine Learning Applications

# Kernel Functions and Tools for Machine Learning Applications

## Documentation for package ‘kerntools’ version 1.2.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

Acc | Accuracy
Acc_rnd | Accuracy of a random model
aggregate_imp | Aggregate importances
Aitchison | Compositional kernels
Boots_CI | Confidence Interval using Bootstrap
BrayCurtis | Kernels for count data
centerK | Centering a kernel matrix
centerX | Centering a squared matrix by row or column
Chi2 | Chi-squared kernel
cLinear | Compositional kernels
cosNorm | Cosine normalization of a kernel matrix
cosnormX | Cosine normalization of a matrix
desparsify | This function deletes those columns and/or rows in a matrix/data.frame that only contain 0s.
Dirac | Kernels for categorical variables
dummy_data | Convert categorical data to dummies.
dummy_var | Levels per factor variable
estimate_gamma | Gamma hyperparameter estimation (RBF kernel)
F1 | F1 score
Frobenius | Frobenius kernel
frobNorm | Frobenius normalization
heatK | Kernel matrix heatmap
histK | Kernel matrix histogram
Intersect | Kernels for sets
Jaccard | Kernels for sets
Kendall | Kendall's tau kernel
kPCA | Kernel PCA
kPCA_arrows | Plot the original variables' contribution to a PCA plot
kPCA_imp | Contributions of the variables to the Principal Components ("loadings")
KTA | Kernel-target alignment
Laplace | Laplacian kernel
Linear | Linear kernel
minmax | Minmax normalization
MKC | Multiple Kernel (Matrices) Combination
nmse | NMSE (Normalized Mean Squared Error)
Normal_CI | Confidence Interval using Normal Approximation
plotImp | Importance barplot
Prec | Precision or PPV
Procrustes | Procrustes Analysis
RBF | Gaussian RBF (Radial Basis Function) kernel
Rec | Recall or Sensitivity or TPR
Ruzicka | Kernels for count data
showdata | Showdata
simK | Kernel matrix similarity
soil | Soil microbiota (raw counts)
Spe | Specificity or TNR
Spectrum | Spectrum kernel
svm_imp | SVM feature importance
TSS | Total Sum Scaling
vonNeumann | Von Neumann entropy