This is the formal representation of Gaussian process models within the funGp package. Gaussian process models are useful statistical tools in the modeling of complex input-output relationships.

  • Main methods
    fgpm: creation of funGp regression models
    predict: output estimation at new input points based on a funGp model
    simulate: random sampling from a funGp Gaussian process model
    update: modification of data and hyperparameters of a funGp model

  • Plotters
    plotLOO: leave-one-out diagnostic plot for a funGp model
    plotPreds: plot for predictions of a funGp model
    plotSims: plot for simulations of a funGp model



Object of class "modelCall". User call reminder.


Object of class "character". Type of model based on type of inputs. To be set from "scalar", "functional", "hybrid".


Object of class "numeric". Number of scalar inputs.


Object of class "numeric". Number of functional inputs.


Object of class "numeric". An array with the original dimension of each functional input.


Object of class "matrix". The scalar input points. Variables are arranged by columns and coordinates by rows.


Object of class "list". The functional input points. Each element of the list contains a functional input in the form of a matrix. In each matrix, curves representing functional coordinates are arranged by rows.


Object of class "matrix". The scalar output values at the coordinates specified by sIn and/or fIn.


Object of class "integer". Number of observed points used to compute the training-training and training-prediction covariance matrices.

Object of class "integer". Among all the points loaded in the model, the amount used for training.


Object of class "fgpProj". Data structures related to the projection of functional inputs. Check fgpProj for more details.


Object of class "fgpKern". Data structures related to the kernel of the Gaussian process model. Check fgpKern for more details.


Object of class "numeric". Variance parameter standing for the homogeneous nugget effect.


Object of class "list". L and LInvY matrices pre-computed for prediction. L is a lower diagonal matrix such that \(L'L\) equals the training auto-covariance matrix \(\). On the other hand, \(LInvY = L^(-1) * sOut\).

Useful material