## Main functions

Nonparametric methods for inference on both spatial trend and variogram functions:

`np.fitgeo`

(automatically) fits an isotropic nonparametric geostatistical model by estimating the trend and the variogram (using a bias-corrected estimator) iteratively (by calling`h.cv`

,`locpol`

,`np.svariso.corr`

and`fitsvar.sb.iso`

at each iteration)`locpol`

,`np.den`

and`np.svar`

use 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 (depending on the amount of available memory).`np.svariso.corr`

computes 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.iso`

fits 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.np`

computes residual kriging predictions

(and the corresponding simple kriging standard errors).`kriging.simple`

computes simple kriging predictions and standard errors.Currently, only global (residual) simple kriging is implemented.

Users are encouraged to use`krige`

(or`krige.cv`

) utilities in gstat package together with`as.vgm`

for local kriging.