ppstat-package: Point Process Statistics
In ppstat: Point Process Statistics

Description Details Author(s) References See Also

This package implements statistical methods for one-dimensional marked point process models.

Package:	ppstat
Type:	Package
Version:	0.9.1
Date:	2013-05-05
License:	GPL version 2 or newer
URL:	http://www.math.ku.dk/~richard/ppstat
LazyLoad:	yes

The package provides a framework for analyzing data from multivariate point processes in time or one-dimensional space, aka marked point processes with discrete marks, based on a specification of the conditional intensity process. The main function is pointProcessModel, which constructs and fits a generalized linear point process model to a dataset. The data need to be stored as an object of S4-class MarkedPointProcess. A MarkedPointProcess object can hold data from a marked point process as well as additional (covariate) continuous processes. Other functions are ppSmooth and ppKernel that implement non-parametric estimaton of linear filter functions.

The data structures for continuous and marked point processes are implemented in the separate package processdata.

Niels Richard Hansen Niels.R.Hansen@math.ku.dk.

Maintainer: Niels.R.Hansen@math.ku.dk

Andersen, P. K., Borgan, OE., Gill, R. D. and Keiding, N. Statistical models based on counting processes. Springer Series in Statistics, 1993.

Cook, R. J. and Lawless, J. F. The Statistical Analysis of Recurrent Events . Springer Series in Statistics for Biology and Health. 2007

Daley, D. J. and Vere-Jones, D. An introduction to the theory of point processes. Vol. I. Springer Series in Probability and its Applications, 2003.

Jacobsen, M. Point process theory and applications. Birkhauser Series in Probability and its Applications, 2006.

Hansen, N. R. Penalized maximum likelihood estimation for generalized linear point processes. arXiv:1003.0848v1

MarkedPointProcess, PointProcessModel, pointProcessModel, ppSmooth ppKernel