Reference

All functions

nmf.sem.cv()

Cross-Validation for NMF-SEM

nmf.sem.DOT()

Generate a Graphviz DOT Diagram for an NMF-SEM Model

nmf.sem()

NMF-SEM Main Estimation Algorithm

nmf.sem.split()

Heuristic Variable Splitting for NMF-SEM

nmfkc.ar.degree.cv()

Optimize lag order for the autoregressive model

nmfkc.ar.DOT()

Generate a Graphviz DOT Diagram for NMF-AR / NMF-VAR Models

nmfkc.ar.predict()

Forecast future values for NMF-VAR model

nmfkc.ar()

Construct observation and covariate matrices for a vector autoregressive model

nmfkc.ar.stationarity()

Check stationarity of an NMF-VAR model

nmfkc.class()

Create a class (one-hot) matrix from a categorical vector

nmfkc.cv()

Perform k-fold cross-validation for NMF with kernel covariates

nmfkc.denormalize()

Denormalize a matrix from \([0,1]\) back to its original scale

nmfkc.DOT()

Generate Graphviz DOT Scripts for NMF or NMF-with-Covariates Models

nmfkc.ecv()

Perform Element-wise Cross-Validation (Wold's CV)

nmfkc.kernel.beta.cv()

Optimize beta of the Gaussian kernel function by cross-validation

nmfkc.kernel.beta.nearest.med()

Estimate Gaussian/RBF kernel parameter beta from covariates (supports landmarks)

nmfkc.kernel()

Create a kernel matrix from covariates

nmfkc.normalize()

Normalize a matrix to the range \([0,1]\)

nmfkc.rank()

Rank selection diagnostics with graphical output

nmfkc()

Optimize NMF with kernel covariates (Full Support for Missing Values)

nmfkc.residual.plot()

Plot Diagnostics: Original, Fitted, and Residual Matrices as Heatmaps

plot(<nmfkc>)

Plot method for objects of class nmfkc

predict(<nmfkc>)

Prediction method for objects of class nmfkc