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stpp Documentation
Space-Time Point Pattern Simulation, Visualisation
and Analysis
Edith Gabriel, Peter J Diggle, Barry Rowlingson and
Francisco J Rodriguez-Cortes
2024-06-27
Many of the models encountered in applications of point process
methods to the study of spatio-temporal phenomena are covered in ‘stpp’.
This package provides statistical tools for analyzing the global and
local second-order properties of spatio-temporal point processes,
including estimators of the space-time inhomogeneous K-function and pair
correlation function among others. It also includes tools to get static
and dynamic display of spatio-temporal point patterns.
References
Baddeley,
A., Rubak, E., Turner, R. (2015). Spatial Point Patterns: Methodology
and Applications with R. CRC Press, Boca Raton.
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G. and Wood A. (1997). An algorithm for simulating stationary Gaussian
random fields. Applied Statistics, Algorithm Section,
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G. and Wood A. (1999). Simulation of stationary Gaussian vector fields.
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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.
Diggle,
P.J., 2013. Statistical Analysis of Spatial and Spatio-Temporal Point
Patterns. CRC Press, Boca Raton.
Gabriel
E., Rowlingson B., Diggle P. (2013). stpp: an R package for plotting,
simulating and analyzing Spatio-Temporal Point Patterns. Journal of
Statistical Software, 53(2), 1-29.
Gabriel
E., Diggle P. (2009). Second-order analysis of inhomogeneous
spatio-temporal point process data. Statistica Neerlandica,
63, 43–51.
Gabriel
E. (2014). Estimating second-order characteristics of inhomogeneous
spatio-temporal point processes: influence ofedge correction methods and
intensity estimates. Methodology and computing in Applied
Probabillity, 16(2), 411–431.
Gneiting
T. (2002). Nonseparable, stationary covariance functions for space-time
data. Journal of the American Statistical Association,
97, 590–600.
Gonzalez,
J. A., Rodriguez-Cortes, F. J., Cronie, O. and Mateu, J. (2016).
Spatio-temporal point process statistics: a review. Spatial
Statiscts, 18, 505–544.
Siino,
M., Rodriguez-Cortes, F. J., Mateu, J. and Adelfio, G. (2017). Testing
for local structure in spatio-temporal point pattern data.
Environmetrics. DOI: 10.1002/env.2463.
Stoyan,
D., Rodriguez-Cortes, F. J., Mateu, J., and Gille, W. (2017). Mark
variograms for spatio-temporal point processes. Spatial
Statistics. 20, 125-147.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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