Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526–2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584–612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2024). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255–269. <doi:10.32614/RJ-2023-094>.
Version: 1.7 Depends: R (≥ 4.0) Imports: doParallel, foreach, igraph, nloptr, parallel, Rfast, Rfast2, stats Published: 2024-09-05 DOI: 10.32614/CRAN.package.PNAR Author: Michail Tsagris [aut, cre], Mirko Armillotta [aut, cph], Konstantinos Fokianos [aut] Maintainer: Michail Tsagris <mtsagris at uoc.gr> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no CRAN checks: PNAR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=PNAR to link to this page.
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