Package: PNAR 1.6

Michail Tsagris

PNAR: Poisson Network Autoregressive Models

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 (2022a). Poisson network autoregression. <arxiv:2104.06296>. Armillotta, M. and K. Fokianos (2022b). Testing linearity for network autoregressive models. <arxiv:2202.03852>. Armillotta, M., Tsagris, M. and Fokianos, K. (2022c). The R-package PNAR for modelling count network time series. <arxiv:2211.02582>.

Authors:Michail Tsagris [aut, cre], Mirko Armillotta [aut, cph], Konstantinos Fokianos [aut]

PNAR_1.6.tar.gz
PNAR_1.6.tar.gz(r-4.5-noble)PNAR_1.6.tar.gz(r-4.4-noble)
PNAR_1.6.tgz(r-4.4-emscripten)PNAR_1.6.tgz(r-4.3-emscripten)
PNAR.pdf |PNAR.html✨
PNAR/json (API)

# Install 'PNAR' in R:
install.packages('PNAR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • crime - Chicago crime dataset
  • crime_W - Network matrix for Chicago crime dataset

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

21 exports 0.09 score 24 dependencies 206 downloads

Last updated 10 months agofrom:f5a6f0fc3b

Exports:adjaadja_gnpgetNglobal_optimise_LM_stnarpqglobal_optimise_LM_tnarpqlin_estimnarpqlin_ic_plotlin_narpq_initlog_lin_estimnarpqlog_lin_ic_plotlog_lin_narpq_initpoisson.MODpqpoisson.MODpq.logpoisson.MODpq.nonlinpoisson.MODpq.stnarpoisson.MODpq.tnarrcopulascore_test_nonlinpq_h0score_test_stnarpq_DVscore_test_stnarpq_jscore_test_tnarpq_j

Dependencies:clicodetoolscpp11doParallelforeachglueigraphiteratorslatticelifecyclemagrittrMatrixnloptrpkgconfigRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfastRfast2rlangRnanoflannvctrs

Readme and manuals

Help Manual

Help pageTopics
Poisson Network Autoregressive ModelsPNAR-package PNAR
Generation of a network from the Stochastic Block Modeladja
Generation of a network from the Erdos-Renyi modeladja_gnp
Chicago crime datasetcrime
Network matrix for Chicago crime datasetcrime_W
Count the number of events within a specified timegetN
Optimization of the score test statistic for the ST-PNAR(p) modelglobal_optimise_LM_stnarpq
Optimization of the score test statistic for the T-PNAR(p) modelglobal_optimise_LM_tnarpq
Estimation of the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))lin_estimnarpq
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_ic_plot
Starting values for the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))lin_narpq_init
Estimation of the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))log_lin_estimnarpq
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_ic_plot
Starting values for the log-linear Poisson NAR(p) model with p lags and q covariates (log-PNAR(p))log_lin_narpq_init
Generation of counts from a linear Poisson NAR(p) model with q covariates (PNAR(p))poisson.MODpq
Generation of multivariate count time series from a log-linear Poisson NAR(p) model with q covariates (log-PNAR(p))poisson.MODpq.log
Generation of multivariate count time series from a non-linear Intercept Drift Poisson NAR(p) model with q covariates (ID-PNAR(p))poisson.MODpq.nonlin
Generation of counts from a non-linear Smooth Transition Poisson NAR(p) model with q covariates (ST-PNAR(p))poisson.MODpq.stnar
Generation of counts from a non-linear Threshold Poisson NAR(p) model with q covariates (T-PNAR(p))poisson.MODpq.tnar
Random number generation of copula functionsrcopula
Linearity test against non-linear ID-PNAR(p) modelscore_test_nonlinpq_h0
Bound p-value for testing for smooth transition effects on PNAR(p) modelscore_test_stnarpq_DV
Bootstrap test for smooth transition effects on PNAR(p) modelscore_test_stnarpq_j
Bootstrap test for threshold effects on PNAR(p) modelscore_test_tnarpq_j
S3 methods for extracting the results of the bound p-value for testing for smooth transition effects on PNAR(p) modelprint.DV print.summary.DV summary.DV
S3 methods for extracting the results of the non-linear hypothesis testprint.nonlin print.summary.nonlin summary.nonlin
S3 methods for extracting the results of the estimation functionsprint.PNAR print.summary.PNAR summary.PNAR