This package provides models of spatio-temporal
point processes in a region S x T and statistical tools for
analysing second-order properties of such processes. It also
includes static and dynamic (2D and 3D) plots.
the first dedicated unified computational environment in the
area of spatio-temporal point processes.
stpp package depends upon some other packages:
splancs: spatial and space-time point pattern analysis
rgl: interactive 3D plotting of densities and surfaces
rpanel: simple interactive controls for R using
KernSmooth: functions for kernel smoothing for Wand & Jones (1995)
stpp is a package for simulating, analysing and visualising
patterns of points in space and time.
Following is a summary of the main functions and the dataset in the
To visualise a spatio-temporal point pattern
animation: space-time data animation.
as.3dpoints: create data in spatio-temporal point
plot.stpp: plot spatio-temporal point object.
Either a two-panels plot showing spatial locations and cumulative times,
a one-panel plot showing spatial locations with times treated as a
attached to each location.
stan: 3D space-time animation.
To simulate spatio-temporal point patterns
rinfec: simulate an infection point process,
rinter: simulate an interaction (inhibition or
contagious) point process,
rlgcp: simulate a log-Gaussian Cox point process,
rpcp: simulate a Poisson cluster point process,
rpp: simulate a Poisson point process.
To analyse spatio-temporal point patterns
PCFhat: space-time inhomogeneous pair correlation
STIKhat: space-time inhomogeneous K-function.
fmd: 2001 food-and-mouth epidemic in north Cumbria (UK).
Edith Gabriel <email@example.com>, Barry Rowlingson and Peter J Diggle.
Baddeley A., Moller J. and Waagepetersen R. (2000). Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54, 329–350.
Chan, G. and Wood A. (1997). An algorithm for simulating stationary Gaussian random fields. Applied Statistics, Algorithm Section, 46, 171–181.
Chan, G. and Wood A. (1999). Simulation of stationary Gaussian vector fields. Statistics and Computing, 9, 265–268.
Diggle P. , Chedwynd A., Haggkvist R. and Morris S. (1995). Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 4, 124–136.
Gabriel E., Diggle P. (2009) Second-order analysis of inhomogeneous spatio-temporal point process data. Statistica Neerlandica, 63, 43–51.
Gneiting T. (2002). Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association, 97, 590–600.
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