Kernel Functions and Tools for Machine Learning Applications

Documentation for package ‘kerntools’ version 1.0.1

Acc	Accuracy
Acc_rnd	Accuracy of a random model
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
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
Spe	Specificity or TNR
Spectrum	Spectrum kernel
svm_imp	SVM feature importance
TSS	Total Sum Scaling
vonNeumann	Von Neumann entropy