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PNAR (version 1.7)

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. .

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Version

Install

install.packages('PNAR')

Monthly Downloads

170

Version

1.7

License

GPL (>= 2)

Maintainer

Michail Tsagris

Last Published

September 5th, 2024

Functions in PNAR (1.7)

lin_narpq_init

Starting values for the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))
summary.nonlin

S3 methods for extracting the results of the non-linear hypothesis test
log_lin_narpq_init

Starting values for the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))
score_test_stnarpq_j

Bootstrap test for smooth transition effects on PNAR(p) model
poisson.MODpq

Generation of counts from a linear Poisson NAR(p) model with q covariates (PNAR(p))
score_test_nonlinpq_h0

Linearity test against non-linear ID-PNAR(p) model
poisson.MODpq.log

Generation of multivariate count time series from a log-linear Poisson NAR(p) model with q covariates (log-PNAR(p))
score_test_tnarpq_j

Bootstrap test for threshold effects on PNAR(p) model
lin_ic_plot

Scatter plot of information criteria versus the number of lags in the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))
score_test_stnarpq_DV

Bound p-value for testing for smooth transition effects on PNAR(p) model
poisson.MODpq.tnar

Generation of counts from a non-linear Threshold Poisson NAR(p) model with q covariates (T-PNAR(p))
log_lin_estimnarpq

Estimation of the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))
summary.DV

S3 methods for extracting the results of the bound p-value for testing for smooth transition effects on PNAR(p) model
summary.PNAR

S3 methods for extracting the results of the estimation functions
rcopula

Random number generation of copula functions
getN

Count the number of events within a specified time
adja_gnp

Generation of a network from the Erdos-Renyi model
PNAR-package

Poisson Network Autoregressive Models
crime

Chicago crime dataset
global_optimise_LM_stnarpq

Optimization of the score test statistic for the ST-PNAR(p) model
crime_W

Network matrix for Chicago crime dataset
global_optimise_LM_tnarpq

Optimization of the score test statistic for the T-PNAR(p) model
lin_estimnarpq

Estimation of the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))
adja

Generation of a network from the Stochastic Block Model
log_lin_ic_plot

Scatter plot of information criteria versus the number of lags in the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))
poisson.MODpq.nonlin

Generation of multivariate count time series from a non-linear Intercept Drift Poisson NAR(p) model with q covariates (ID-PNAR(p))
poisson.MODpq.stnar

Generation of counts from a non-linear Smooth Transition Poisson NAR(p) model with q covariates (ST-PNAR(p))