Package index
-
Acc()
- Accuracy
-
Acc_rnd()
- Accuracy of a random model
-
Boots_CI()
- Confidence Interval using Bootstrap
-
BrayCurtis()
Ruzicka()
- Kernels for count data
-
Dirac()
- Kernels for categorical variables
-
F1()
- F1 score
-
Frobenius()
- Frobenius kernel
-
Jaccard()
Intersect()
- Kernels for sets
-
KTA()
- Kernel-target alignment
-
Kendall()
- Kendall's tau kernel
-
Laplace()
- Laplacian kernel
-
Linear()
- Linear kernel
-
MKC()
- Multiple Kernel (Matrices) Combination
-
Normal_CI()
- Confidence Interval using Normal Approximation
-
Prec()
- Precision or PPV
-
Procrustes()
- Procrustes Analysis
-
RBF()
- Gaussian RBF (Radial Basis Function) kernel
-
Rec()
- Recall or Sensitivity or TPR
-
Spe()
- Specificity or TNR
-
Spectrum()
- Spectrum kernel
-
TSS()
- Total Sum Scaling
-
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.
-
dummy_data()
- Convert categorical data to dummies.
-
dummy_var()
- Levels per factor variable
-
estimate_gamma()
- Gamma hyperparameter estimation (RBF kernel)
-
frobNorm()
- Frobenius normalization
-
heatK()
- Kernel matrix heatmap
-
histK()
- Kernel matrix histogram
-
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")
-
minmax()
- Minmax normalization
-
nmse()
- NMSE (Normalized Mean Squared Error)
-
plotImp()
- Importance barplot
-
showdata
- Showdata
-
simK()
- Kernel matrix similarity
-
soil
- Soil microbiota (raw counts)
-
svm_imp()
- SVM feature importance
-
vonNeumann()
- Von Neumann entropy