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 |