From a matrix of locations and covariance parameters of the form (variance, range, nugget), return the square matrix of all pairwise covariances.
exponential_isotropic(covparms, locs)d_exponential_isotropic(covparms, locs)
d_matern15_isotropic(covparms, locs)
d_matern25_isotropic(covparms, locs)
A matrix with n rows and n columns, with the i,j entry
containing the covariance between observations at locs[i,] and
locs[j,].
A vector with covariance parameters in the form (variance, range, nugget)
A matrix with n rows and d columns.
Each row of locs is a point in R^d.
d_exponential_isotropic(): Derivatives of isotropic exponential covariance
d_matern15_isotropic(): Derivatives of isotropic
matern covariance with smoothness 1.5
d_matern25_isotropic(): Derivatives of isotropic
matern covariance function with smoothness 2.5
The covariance parameter vector is (variance, range, nugget) = \((\sigma^2,\alpha,\tau^2)\), and the covariance function is parameterized as $$ M(x,y) = \sigma^2 exp( - || x - y ||/ \alpha )$$ The nugget value \( \sigma^2 \tau^2 \) is added to the diagonal of the covariance matrix. NOTE: the nugget is \( \sigma^2 \tau^2 \), not \( \tau^2 \).