Poisson Network Autoregressive Models
Overview
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. (2023). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. doi:10.32614/RJ-2023-094.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 27includes a package overview page
- Examples that run
- 100%26 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 96%
Downloads
Dependencies
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2022-06-08
- Total releases
- 9 / 4 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.0
- Bundled data
- 33 KB / 2 files
- Download size
- 65 KB
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