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. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584–612. 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.
- Version1.7
- R version≥ 4.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526–2552.
- Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584–612.
- 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.
- Last release09/05/2024
Team
Michail Tsagris
MaintainerShow author detailsKonstantinos Fokianos
Show author detailsRolesAuthorMirko Armillotta
Show author detailsRolesAuthor, Copyright holder
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- Imports6 packages