https://github.com/cran/spatstat
Revision 97116a19ab5e4323b8c5566f016d2dc6d77b217b authored by Adrian Baddeley on 06 November 2009, 10:18:11 UTC, committed by cran-robot on 06 November 2009, 10:18:11 UTC
1 parent de378e6
Tip revision: 97116a19ab5e4323b8c5566f016d2dc6d77b217b authored by Adrian Baddeley on 06 November 2009, 10:18:11 UTC
version 1.17-1
version 1.17-1
Tip revision: 97116a1
DESCRIPTION
Package: spatstat
Version: 1.17-1
Date: 04 november 2009
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 Ang Qi Wei, C. Beale, R. Bernhardt, B.
Biggerstaff, R. Bivand, F. Bonneu, J. Burgos, J.B. Chen, Y.C.
Chin, B. Christensen, 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, R. Mark, J.
Mateu, P. McCullagh, S. Meyer, X.C. Mi, J. Moller, L.S.
Nielsen, F. Nunes, E. Parilov, J. Picka, M. Reiter, B.D.
Ripley, B. Rowlingson, E. Rubak, J. Rudge, A. Sarkka, K.
Schladitz, B.T. Scott, I.-M. Sintorn, M. Spiess, M. Stevenson,
P. Surovy, B. Turlach, A. van Burgel, T. Verbeke, A. Villers,
H. Wang, H. Wendrock and S. Wong.
Maintainer: Adrian Baddeley <adrian@maths.uwa.edu.au>
Depends: R (>= 2.10.0), stats, graphics, utils, mgcv, deldir (>= 0.0-7)
Suggests: gpclib, sm, maptools, rpanel, tkrplot, scatterplot3d
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: 2009-11-06 03:02:06 UTC; adrian
Repository: CRAN
Date/Publication: 2009-11-06 10:18:11
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