Nonparametric methods for inference on both spatial trend and variogram functions:
np.svaruse local polynomial kernel smoothing to compute nonparametric estimates of a multidimensional regression function (e.g. a spatial trend), a probability density function or a semivariogram (or their first derivatives), respectively. Estimates of these functions can be constructed for any dimension (the amount of available memory is the only limitation).
np.svariso.corrcomputes a bias-corrected nonparametric semivariogram estimate using an iterative algorithm similar to that described in Fernandez-Casal and Francisco-Fernandez (2014). This procedure tries to correct the bias due to the direct use of residuals (obtained, in this case, from a nonparametric estimation of the trend function) in semivariogram estimation.
fitsvar.sb.isofits a ‘nonparametric’ isotropic Shapiro-Botha variogram model by WLS. Currently, only isotropic semivariogram estimation is supported.
Nonparametric residual kriging (sometimes called external drift kriging):
kriging.npcomputes residual kriging predictions
(and the corresponding simple kriging standard errors).
kriging.simplecomputes simple kriging predictions and standard errors.
Currently, only global (residual) simple kriging is implemented.
Users are encouraged to use
krige.cv) utilities in gstat package together with
as.vgmfor local kriging.