R: Poisson Network Autoregressive Models

# Poisson Network Autoregressive Models

## Documentation for package ‘PNAR’ version 1.8

- DESCRIPTION file

## Help Pages

PNAR-package | Poisson Network Autoregressive Models
adja | Generation of a network from the Stochastic Block Model
adja_gnp | Generation of a network from the Erdos-Renyi model
crime | Chicago crime dataset
crime_W | Network matrix for Chicago crime dataset
getN | Count the number of events within a specified time
global_optimise_LM_stnarpq | Optimization of the score test statistic for the ST-PNAR(p) model
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))
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))
lin_narpq_init | Starting values for the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))
log_lin_estimnarpq | Estimation of the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))
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))
log_lin_narpq_init | Starting values for the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))
PNAR | Poisson Network Autoregressive Models
poisson.MODpq | Generation of counts from a linear Poisson NAR(p) model with q covariates (PNAR(p))
poisson.MODpq.log | Generation of multivariate count time series from a log-linear Poisson NAR(p) model with 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))
poisson.MODpq.tnar | Generation of counts from a non-linear Threshold Poisson NAR(p) model with q covariates (T-PNAR(p))
print.DV | S3 methods for extracting the results of the bound p-value for testing for smooth transition effects on PNAR(p) model
print.nonlin | S3 methods for extracting the results of the non-linear hypothesis test
print.PNAR | S3 methods for extracting the results of the estimation functions
print.summary.DV | S3 methods for extracting the results of the bound p-value for testing for smooth transition effects on PNAR(p) model
print.summary.nonlin | S3 methods for extracting the results of the non-linear hypothesis test
print.summary.PNAR | S3 methods for extracting the results of the estimation functions
rcopula | Random number generation of copula functions
score_test_nonlinpq_h0 | Linearity test against non-linear ID-PNAR(p) model
score_test_stnarpq_DV | Bound p-value for testing for smooth transition effects on PNAR(p) model
score_test_stnarpq_j | Bootstrap test for smooth transition effects on PNAR(p) model
score_test_tnarpq_j | Bootstrap test for threshold effects on PNAR(p) model
summary.DV | S3 methods for extracting the results of the bound p-value for testing for smooth transition effects on PNAR(p) model
summary.nonlin | S3 methods for extracting the results of the non-linear hypothesis test
summary.PNAR | S3 methods for extracting the results of the estimation functions