| stopp-package {stopp} | R Documentation |
Spatio-Temporal Point Pattern Methods, Model Fitting, Diagnostics, Simulation, Local Tests
Description
Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers: Siino, Adelfio, and Mateu (2018), Siino et al. (2018), Adelfio et al. (2020), D’Angelo, Adelfio, and Mateu (2021), D’Angelo, Adelfio, and Mateu (2022), and D’Angelo, Adelfio, and Mateu (2023). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks.
Author(s)
Nicoletta D'Angelo [aut,cre] nicoletta.dangelo@unipa.it, Giada Adelfio [aut]
References
Adelfio, G., Siino, M., Mateu, J., and Rodríguez-Cortés, F. J. (2020). Some properties of local weighted second-order statistics for spatio-temporal point processes. Stochastic Environmental Research and Risk Assessment, 34(1), 149-168.
D’Angelo, N., Adelfio, G., and Mateu, J. (2021). Assessing local differences between the spatio-temporal second-order structure of two point patterns occurring on the same linear network. Spatial Statistics, 45, 100534.
D’Angelo, N., Adelfio, G. and Mateu, J. (2022) Local inhomogeneous second-order characteristics for spatio-temporal point processes on linear networks. Stat Papers. https://doi.org/10.1007/s00362-022-01338-4
D'Angelo, N., Adelfio, G., and Mateu, J. (2023). Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes. Computational Statistics & Data Analysis, 180, 107679.
Siino, M., Adelfio, G., and Mateu, J. (2018). Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes. Stochastic environmental research and risk assessment, 32(12), 3525-3539.
Siino, M., Rodríguez‐Cortés, F. J., Mateu, J. ,and Adelfio, G. (2018). Testing for local structure in spatiotemporal point pattern data. Environmetrics, 29(5-6), e2463.