Below is the detail information:
DEVELOPED  1999 
AUTHOR  R. Kelley Pace LREC Chair of Real Estate E.J. Ourso College of Business Administration Louisiana State University Baton Rouge, LA 70803 (225)3886256 FAX: (225)3341227 kelley@spatialstatistics.com Ronald Barry Associate Professor of Statistics Department of Mathematical Sciences University of Alaska Fairbanks, Alaska 997756660 (907)4747226 FAX: (907)4745394 FFRPB@uaf.edu 
PLATFORM  The toolbox requires Matlab 5.0 or later. Unfortunately, previous editions of Matlab did not contain the Delaunay command and others needed for the toolbox. The total installation takes around 15 Mb. The routines have been tested on PC compatibles ? the routines should run on other platforms, but have not been tested on nonPC compatibles. 
PURPOSE  The spatial statistics toolbox provides maximum likelihood estimation and likelihoodbased inference for a variety of models (with a heavy emphasis upon lattice models). The toolbox particularly excels at spatial estimation with large data sets. 
FUNCTIONS  Specifically, the software can estimate simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), mixed regressive spatially autoregressive estimates as well as other lattice models. Spatial weight matrix can be calculated for very large datasets (> 100 000 points) It can be based upon nearest neighbours (symmetric or asymmetric) and Delaunay triangles (symmetric). The Delaunay spatial weight matrix leads to a concentration matrix or a variancecovariance matrix that depends upon only oneparameter (a , the autoregressive parameter). In contrast, the nearest neighbour concentration matrices or variancecovariance matrices depend upon three parameters (a , the autoregressive parameter; m, the number of neighbours; and r , which governs the rate weights decline with the order of the neighbours with the closest neighbour given the highest weighting, the second closest given a lower weighting, and so forth). Computation of the logdeterminants for a grid of autoregressive parameters (prespecified by the routine) can also be done. Computing the logdeterminants is the slowest step but only needs to be done once for most problems (the same applies to creating the spatial weight matrix). 
CODES  Matlab codes available 
TIP  Papers and links to other related Matlab codes can be found on the web site of Kelley Pace 
HOMEPAGE  www.spatialstatistics.com/software_index.htm 

