## KriSp: a Package for Interpolation of Large Datasets Using Covariance Tapering

### Reinhard Furrer, GSP post doc

Interpolation of a spatially correlated random process is used in many
areas. The best unbiased linear predictor, often called kriging in
geostatistical science, requires the solution of a large linear system
based on the covariance matrix of the observations. Tapering the
correct covariance matrix with an appropriate compactly supported
covariance function reduces the computational burden significantly and
still has an asymptotic optimal mean squared error. The effect of
tapering is to create a sparse approximate linear system that can then
be solved using sparse matrix algorithms. Further, the manageable
size of the observed and predicted fields can be far bigger than with
classical approaches. The net result is the ability to analyze spatial
data sets that are several orders of magnitude larger than past work
in a high level interactive environment such as R. The talk
summarizes briefly the theoretical background and then presents the R
package 'KriSp' which supports the taper approach.