PNAR-package: Poisson Network Autoregressive Models

In PNAR: Poisson Network Autoregressive Models

Poisson Network Autoregressive Models

Description

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.

Details

Package:	PNAR
Type:	Package
Version:	1.7
Date:	2024-09-05
License:	GPL(>=2)

Note

Disclaimer: Dr Mirko Armillotta and Konstantinos Fokianos wrote the initial functions. Dr Tsagris modified them, created the package and he is the maintainer.

We would to like to acknowledge Manos Papadakis for his help with the "htest" class object and S3 methods (print() and summary() functions).

Author(s)

Michail Tsagris, Mirko Armillotta and Konstantinos Fokianos.

References

Armillotta, M. and K. Fokianos (2024). Count network autoregression. Journal of Time Series Analysis, 45(4): 584–612.

Armillotta, M. and K. Fokianos (2023). Nonlinear network autoregression. Annals of Statistics, 51(6): 2526–2552.

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.

PNAR documentation built on Sept. 12, 2024, 7:30 a.m.