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KRIG

Implements different methods for spatial statistics, in particular focused in Kriging based models. We count with different implemented models, simple, ordinary and universal forms of Kriging, co-Kriging and regression Kriging models. Includes, multivariate sensitivity analysis under an approximation designed over Reproducing Kernel Hilbert Spaces and computation of Sobol indexes under this framework.

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install.packages('KRIG')

Monthly Downloads

6

Version

0.1.0

License

LGPL-3

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Maintainer

Pedro Guarderas

Last Published

January 30th, 2018

Functions in KRIG (0.1.0)

Krigvar

Combinatorial variance computation.
Krig

Kriging computation.
Copper

Copper mining data
nat_cubic_spline_kernel

Natural cubic spline kernel.
Krigidx

Combinatorial variance computation.
KRIG-package

Spatial Statistics with Kriging
thin_plate_kernel

Thin plate kernel.
polynomial_kernel

Polynomial kernel
spherical_kernel

Spherical kernel.
square_kernel

Square kernel.
Kanova

KANOVA, kernel anova under RKHS approximations.
triangular_kernel

Triangular kernel.
Kov

Spatial covariance matrix.
list_integrate_kernel

Integrals of a list of kernels.
linear_kernel

Linear kernel
matern_kernel

Mat<U+00E9>rn kernel.
multilog_kernel

Multilog kernel.
weight_pow_dist

Generic weighted p-distance
variogram

Computes the variogram.
vector_integrate_kernel

One coordinate kernel integral.
gaussian_kernel

Gaussian kernel.
exp_kernel

Exponential kernel.
integrate_kernel

Complete kernel integral.