npsp: Nonparametric spatial (geo)statistics

Version 0.7.13

This package implements nonparametric methods for inference on multidimensional spatial (or spatio-temporal) processes, which may be (especially) useful in (automatic) geostatistical modeling and interpolation.

Main functions

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

Nonparametric residual kriging (sometimes called external drift kriging):

Other functions

Among the other functions intended for direct access by the user, the following (methods for multidimensional linear binning, local polynomial kernel regression, density or variogram estimation) could be emphasized: binning(), bin.den(), svar.bin(), and interp(). There are functions for plotting data joint with a legend representing a continuous color scale (based on fields::image.plot()):

There are also some functions which can be used to interact with other packages. For instance, as.variogram() (geoR) or as.vgm() (gstat).

See the Reference for the complete list of functions.


npsp is available from CRAN, but you can install the development version from github with:

# install.packages("devtools")

Note also that, as this package requires compilation, Windows users need to have previously installed the appropriate version of Rtools, and OS X users need to have installed Xcode.

Alternatively, Windows users may install the corresponding file in the releases section of the github repository.

For R versions 4.2.x under Windows:

                 repos = NULL)


Ruben Fernandez-Casal (Dep. Mathematics, University of A Coruña, Spain). Please send comments, error reports or suggestions to


Important suggestions and contributions to some techniques included here were made by Sergio Castillo-Páez (Universidad de las Fuerzas Armadas ESPE, Ecuador) and Tomas Cotos-Yañez (Dep. Statistics, University of Vigo, Spain).

This research has been supported by MINECO grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF.