https://github.com/cran/spatstat
Revision c75a36fce807d4a1105403c1f8628a196a3e54da authored by Adrian Baddeley on 24 October 2008, 14:28:47 UTC, committed by cran-robot on 24 October 2008, 14:28:47 UTC
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Tip revision: c75a36fce807d4a1105403c1f8628a196a3e54da authored by Adrian Baddeley on 24 October 2008, 14:28:47 UTC
version 1.14-5
Tip revision: c75a36f
DESCRIPTION
Package: spatstat
Version: 1.14-5
Date: 23 October 2008
Title: Spatial Point Pattern analysis, model-fitting, simulation, tests
Author: Adrian Baddeley <adrian@maths.uwa.edu.au> and
	Rolf Turner <r.turner@auckland.ac.nz>, with substantial contributions
	of code by Marie-Colette van Lieshout, Rasmus Waagepetersen, Kasper 
	Klitgaard Berthelsen and Dominic Schuhmacher. Additional contributions 
	by C. Beale, B. Biggerstaff, R. Bivand, F. Bonneu, J.B. Chen, 
        Y.C. Chin, M. de la Cruz, P.J. Diggle, S. Eglen, A. Gault, M. Genton, 
	P. Grabarnik, C. Graf, J. Franklin, U. Hahn, M. Hering, M.B. Hansen, 
	M. Hazelton, J. Heikkinen, K. Hornik, R. Ihaka, R. John-Chandran, 
	D. Johnson, J. Laake, J. Mateu, P. McCullagh, X.C. Mi, J. Moller, 
	L.S. Nielsen, E. Parilov, J. Picka, M. Reiter, B.D. Ripley, 
	B. Rowlingson, J. Rudge, A. Sarkka, K. Schladitz, B.T. Scott, 
	I.-M. Sintorn, M. Spiess, M. Stevenson, P. Surovy, B. Turlach, 
	A. van Burgel, H. Wang and S. Wong.
Maintainer: Adrian Baddeley <adrian@maths.uwa.edu.au>
Depends: R (>= 2.6.0), stats, graphics, utils, mgcv, gpclib, deldir (>= 0.0-7)
Suggests: sm
Description: A package for analysing spatial data,
	mainly Spatial Point Patterns, including multitype/marked points
	and spatial covariates, in any two-dimensional spatial region.
	Contains functions for plotting spatial data, 
	exploratory data analysis, model-fitting, simulation, 
	spatial sampling, model diagnostics,
	and formal inference. Data types include point patterns,
	line segment patterns, spatial windows, and
	pixel images. Point process models can be
	fitted to point pattern data. Cluster type models are fitted
	by the method of minimum contrast. Very general Gibbs point
	process models can be fitted to point pattern data
	using a function ppm similar to lm or glm. Models may
	include dependence on covariates, interpoint interaction
	and dependence on marks. Fitted models can be simulated automatically.
	Also provides facilities for formal inference (such as chi-squared
	tests) and model diagnostics (including simulation
	envelopes, residuals, residual plots and Q-Q plots).
License: GPL (>= 2)
URL: http://www.spatstat.org
Packaged: Fri Oct 24 10:28:47 2008; adrian
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