A collection of functions for estimating centrographic statistcs and computational geometries from spatial point patterns
A collection of functions for computing centrographic satistics (e.g., standard distance, standard deviation ellipse), and minimum convex polygons (MCP)for observations taken at point locations.  A tool is also provided for converting geometric objects associated with the centrographic statistics, and MCPs into ESRI Shapefiles.

R (>= 2.0.1), adehabitat, ade4, gpclib, sp, maptools

A collection of functions for estimating centrographic statistcs
and computational geometries for spatial point patterns
Randy Bui, Ron N. Buliung, Tarmo K. Remmel
A collection of functions for computing centrographic
statistics (e.g., standard distance, standard deviation
ellipse, standard deviation box), and minimum convex polygons
(MCP) for observations taken at point locations.  A tool is
also provided for converting geometric objects associated with
the centrographic statistics, and MCPs into ESRI Shapefiles.
Separate plotting functions have been developed for each
measure.
R (>= 2.0.1), adehabitat, ade4, gpclib, sp, maptools, splancs,
Hmisc

A collection of functions for estimating centrographic
statistics and computational geometries for spatial point
patterns

R (>= 2.11.1), adehabitat, ade4, gpclib, sp, maptools,
splancs, Hmisc

A collection of functions for computing centrographic
statistics (e.g., standard distance, standard deviation
ellipse, standard deviation box) for observations taken at
point locations. Separate plotting functions have been
developed for each measure. Users interested in writing results
to ESRI shapefiles can do so by using results from aspace
functions as inputs to the convert.to.shapefile and
write.shapefile functions in the shapefiles library. The aspace
library was originally conceived to aid in the analysis of
spatial patterns of travel behaviour (see Buliung and Remmel,
2008). Major changes in the current version include (1) removal
of dependencies on several external libraries (e.g., gpclib,
maptools, sp), (2) the separation of plotting and estimation
capabilities, (3) reduction in the number of functions, and (4)
expansion of analytical capabilities with additional functions
for descriptive analysis and visualization (e.g., standard
deviation box, centre of minimum distance, central feature).
R (>= 2.12.2), splancs, Hmisc, shapefiles