https://github.com/cran/spatstat
Revision adc4d1a73ce0bc27b15a9ab4b12d1a52f68a4a55 authored by Adrian Baddeley on 19 March 2010, 07:31:32 UTC, committed by cran-robot on 19 March 2010, 07:31:32 UTC
1 parent 6ab7c0c
Tip revision: adc4d1a73ce0bc27b15a9ab4b12d1a52f68a4a55 authored by Adrian Baddeley on 19 March 2010, 07:31:32 UTC
version 1.18-1
version 1.18-1
Tip revision: adc4d1a
DESCRIPTION
Package: spatstat
Version: 1.18-1
Date: 2010-03-19
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, Dominic Schuhmacher and Ege Rubak.
Additional contributions by Ang Qi Wei, C. Beale, R. Bernhardt,
B. Biggerstaff, R. Bivand, F. Bonneu, J. Burgos, S. Byers, J.B.
Chen, Y.C. Chin, B. Christensen, M. de la Cruz, P. Dalgaard,
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, A. Raftery, 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, 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. Also
supports three-dimensional point patterns. 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, pixel images and
tessellations. 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: 2010-03-19 04:19:48 UTC; adrian
Repository: CRAN
Date/Publication: 2010-03-19 07:31:32
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