# Estimation of a nonseparable and/or nonstationary covariance structure (NSGPR model) Estimate the covariance structure of a zero-mean Gaussian Process with Q-dimensional input coordinates (covariates). Multiple realisations for the response variable can be used, provided they are observed on the same grid of dimension n_1 x n_2 x ... x n_Q. Let n = n_1 x n_2 x ... x n_Q and let nSamples be the number of realisations. ```r nsgpr( response, input, corrModel = "pow.ex", gamma = 2, nu = 1.5, whichTau = NULL, nBasis = 5, cyclic = NULL, unitSignalVariance = F, zeroNoiseVariance = F, sepCov = F, nInitCandidates = 300, absBounds = 6, inputSubsetIdx = NULL ) ``` ## Arguments - `response`: Response variable. This should be a (n x nSamples) matrix where each column is a realisation - `input`: List of Q input variables (see Details). - `corrModel`: Correlation function specification used for g(.). It can be either "pow.ex" or "matern". - `gamma`: Power parameter used in powered exponential kernel function. It must be 0.

nsgpr function