Evaluates the covariance over the upper triangle of a distance matrix 
  rather than over the entire matrix to reduce computation time.

covariance

CovarianceUpper

For curve, surface and function
 fitting with an emphasis on splines, spatial data and spatial
 statistics. The major methods include cubic, and thin plate splines,
 Kriging and compact covariances for large data sets. The splines and
 Kriging methods are supporting by functions that can determine the
 smoothing parameter (nugget and sill variance) by cross validation
 and also by restricted maximum likelihood. For Kriging there is
 an easy to function that also estimates the correlation scale (range).
 A major feature is that
 any covariance function implemented in R and following a simple fields
 format can be used for spatial prediction. There are also many useful
 functions for plotting and
 working with spatial data as images. This package also contains an
 implementation of sparse matrix methods for large spatial data sets and
 currently requires the sparse matrix (spam) package.
 Use help(fields) to get started and for an
 overview.  The fields source code is deliberately commented and
 provides useful explanations of numerical details in addition to the
 manual pages. The commented source code can be viewed by expanding the
 source code .tar.gz file and looking in R subdirectory. 

CovarianceUpper: Evaluate covariance over upper triangle of distance matrix

Description

Usage

Arguments

Value

See Also