locpol, np.den and
np.svar use local polynomial kernel methods
to compute nonparametric estimates of a multidimensional
regression function, 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). To speed up computations, linear binning is
used to discretize the data. A full bandwidth matrix and
a multiplicative triweight kernel is used to compute the
weights. Main calculations are performed in FORTRAN using
the LAPACK library. 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 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. There are also functions for plotting data joint with a
legend representing a continuous color scale.
splot allows to combine a standard R plot
with a legend. spoints,
simage and spersp draw the
corresponding high-level plot with a legend strip for the
color scale. These functions are based on
image.plot of package fields. Among the other functions intended for direct access by
the user, the following could be emphasized:
binning, bin.den,
svar.bin, h.cv and
interp. There are also some functions which
can be used to interact with other packages. For
instance, as.variogram (geoR) or
as.vgm (gstat). Kriging is not yet implemented in this package. Users are
encouraged to use krige (or
krige.cv) utilities in gstat
package together with as.vgm.Fernandez-Casal R., Gonzalez-Manteiga W. and Febrero-Bande M. (2003) Flexible Spatio-Temporal Stationary Variogram Models, Statistics and Computing, 13, 127-136.
Rupert D. and Wand M.P. (1994) Multivariate locally weighted least squares regression. The Annals of Statistics, 22, 1346-1370.
Shapiro A. and Botha J.D. (1991) Variogram fitting with a general class of conditionally non-negative definite functions. Computational Statistics and Data Analysis, 11, 87-96.
Wand M.P. (1994) Fast Computation of Multivariate Kernel Estimators. Journal of Computational and Graphical Statistics, 3, 433-445.
Wand M.P. and Jones M.C. (1995) Kernel Smoothing. Chapman and Hall, London.