The R-package ppstat implements methods for point process statistics for multivariate point processes (that is, marked point processes) on the line. The package is still under development. Version 0.8 is available from CRAN. The development version is available from R-Forge. The package is based on another R-package processdata. This package implements a data structure for storing point process and general stochastic process data. It includes various plotting and subsetting facilities. The ppstat package implements a framework termed generalized linear point process models where the model is specified in terms of a (non-linear) transformation of a linear combination of predictor processes. The package supports a specification of such models via a formula, which can be used to express a number of different transformations and filters of the basic processes that enter into the predictor. The data structure supports observations of one or several independent point processes, whose intensity can be given in terms of its own internal history as well as additional covariate processes. The package was originally developed for point process modeling of genome features. However, there is nothing in the package that is specific to genomic modeling. Indeed, that package implements models that have been used to model such diverse things as financial trade times and neuron spike times.
References in zbMATH (referenced in 1 article , 1 standard article )
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- Hansen, Niels Richard: Nonparametric likelihood based estimation of linear filters for point processes (2015)