A simple temporal point process (SPP) is an important class of time series, where the sample realization of the process is solely composed of the times at which events occur. Particular examples of point process data are neuronal spike patterns or spike trains, and a large number of distance and similarity metrics for those data have been proposed. A marked point process (MPP) is an extension of a simple temporal point process, in which a certain vector valued mark is associated with each of the temporal points in the SPP. Analyses of MPPs are of practical importance because instances of MPPs include recordings of natural disasters such as earthquakes and tornadoes. In this paper, we introduce the R package mmpp, which implements a number of distance and similarity metrics for SPPs, and also extends those metrics for dealing with MPPs.
splancs, spatstat, PtProcess, stpp, mmpp, SAPP, etasFLP
SpatioTemporal, Spatial, Survival
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For attribution, please cite this work as
Hino, et al., "The R Journal: mmpp: A Package for Calculating Similarity and Distance Metrics for Simple and Marked Temporal Point Processes", The R Journal, 2015
BibTeX citation
@article{RJ-2015-033,
author = {Hino, Hideitsu and Takano, Ken and Murata, Noboru},
title = {The R Journal: mmpp: A Package for Calculating Similarity and Distance Metrics for Simple and Marked Temporal Point Processes},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-033},
doi = {10.32614/RJ-2015-033},
volume = {7},
issue = {2},
issn = {2073-4859},
pages = {237-248}
}