adja_gnp

This function generates a network from the Erdos-Renyi model.

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>.

Michail Tsagris

PNAR

Poisson Network Autoregressive Models

Mirko Armillotta

Konstantinos Fokianos

adja_gnp function

<dl><dt>N</dt>
<dd>The number of nodes on the network.</dd><dt>alpha</dt>
<dd>The network density. A value in \([0,1]\) defining the frequency of
connections in the network.</dd><dt>directed</dt>
<dd>Logical scalar, whether to generate a directed network. If TRUE a directed
network is generated.</dd></dl>

Arguments

Mirko Armillotta, Michail Tsagris and Konstantinos Fokianos.

Author

Generation of a network from the Erdos-Renyi model — adja_gnp

<dl>

<dt>N</dt>
<dd>The number of nodes on the network.</dd>

<dt>alpha</dt>
<dd>The network density. A value in \([0,1]\) defining the frequency of
connections in the network.</dd>

<dt>directed</dt>
<dd>Logical scalar, whether to generate a directed network. If TRUE a directed
network is generated.</dd>

</dl>

adja_gnp: Generation of a network from the Erdos-Renyi model

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples