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