Package index • nmfkc

All functions

nmfkc.ar.degree.cv(): Optimize lag order for the autoregressive model

nmfkc.ar.DOT(): Generate DOT language scripts for vector autoregressive 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 DOT language scripts for NMF 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 kernel parameter beta from covariates

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