PNAR: Poisson Network Autoregressive Models
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 |
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