{
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  "Package": "PNAR",
  "Type": "Package",
  "Title": "Poisson Network Autoregressive Models",
  "Version": "1.8",
  "Date": "2026-03-27",
  "Authors@R": "c(\nperson( \"Michail\", \"Tsagris\", role = c(\"aut\", \"cre\"), email = \"mtsagris@uoc.gr\" ),\nperson( \"Mirko\", \"Armillotta\", role = c(\"aut\", \"cph\"), email = \"m.armillotta@vu.nl\" ),\nperson( \"Konstantinos\", \"Fokianos\", role = c(\"aut\"), email = \"fokianos@ucy.ac.cy\" ) )",
  "Author": "Michail Tsagris [aut, cre], Mirko Armillotta [aut, cph],\nKonstantinos Fokianos [aut]",
  "Maintainer": "Michail Tsagris <mtsagris@uoc.gr>",
  "Description": "Quasi likelihood-based methods for estimating linear and\nlog-linear Poisson Network Autoregression models with p lags\nand covariates. Tools for testing the linearity versus several\nnon-linear alternatives. Tools for simulation of multivariate\ncount distributions, from linear and non-linear PNAR models, by\nusing a specific copula construction. References include:\nArmillotta, M. and K. Fokianos (2023). \"Nonlinear network\nautoregression\". Annals of Statistics, 51(6): 2526--2552.\n<doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos\n(2024). \"Count network autoregression\". Journal of Time Series\nAnalysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>.\nArmillotta, M., Tsagris, M. and Fokianos, K. (2023). \"Inference\nfor Network Count Time Series with the R Package PNAR\". The R\nJournal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.",
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  "Date/Publication": "2026-03-27 18:07:33 UTC",
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    "score_test_nonlinpq_h0",
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    "score_test_stnarpq_j",
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      "name": "crime",
      "title": "Chicago crime dataset",
      "object": "crime",
      "class": [
        "mts",
        "ts",
        "matrix"
      ],
      "fields": [],
      "rows": 72,
      "table": true,
      "tojson": true
    },
    {
      "name": "crime_W",
      "title": "Network matrix for Chicago crime dataset",
      "object": "crime_W",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [],
      "rows": 552,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "PNAR-package",
      "title": "Poisson Network Autoregressive Models",
      "topics": [
        "PNAR-package",
        "PNAR"
      ]
    },
    {
      "page": "adja",
      "title": "Generation of a network from the Stochastic Block Model",
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        "adja"
      ]
    },
    {
      "page": "adja_gnp",
      "title": "Generation of a network from the Erdos-Renyi model",
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        "adja_gnp"
      ]
    },
    {
      "page": "crime",
      "title": "Chicago crime dataset",
      "topics": [
        "crime"
      ]
    },
    {
      "page": "crime_W",
      "title": "Network matrix for Chicago crime dataset",
      "topics": [
        "crime_W"
      ]
    },
    {
      "page": "getN",
      "title": "Count the number of events within a specified time",
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        "getN"
      ]
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      "title": "Optimization of the score test statistic for the ST-PNAR(p) model",
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    },
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      "title": "Optimization of the score test statistic for the T-PNAR(p) model",
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        "global_optimise_LM_tnarpq"
      ]
    },
    {
      "page": "lin_estimnarpq",
      "title": "Estimation of the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))",
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        "lin_estimnarpq"
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    {
      "page": "lin_ic_plot",
      "title": "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))",
      "topics": [
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    },
    {
      "page": "lin_narpq_init",
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        "lin_narpq_init"
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    {
      "page": "log_lin_estimnarpq",
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      "page": "log_lin_ic_plot",
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