Spatial Point Pattern analysis, model-fitting, simulation, tests
Adrian Baddeley <adrian@maths.uwa.edu.au> and
Rolf Turner <rolf@math.unb.ca>, 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, F. Bonneu, J.B. Chen, Y.C. Chin,
P.J. Diggle, S. Eglen, A. Gault, M. Genton, P. Grabarnik,
C. Graf, U. Hahn, M. Hering, M.B. Hansen, M. Hazelton, J. Heikkinen,
K. Hornik, R. John-Chandran, J. Mateu, P. McCullagh, X.C. Mi,
J. Moller, L.S. Nielsen, E. Parilov, M. Reiter, B.D. Ripley,
B. Rowlingson, A. Sarkka, K. Schladitz, B.T. Scott, M. Spiess,
M. Stevenson, P. Surovy, B. Turlach, A. van Burgel and S. Wong.
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 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).