{
  "_id": "6a23ca6d530b9bc726be15c7",
  "Package": "GNAR",
  "Type": "Package",
  "Title": "Methods for Fitting Network Time Series Models",
  "Version": "1.1.4",
  "Date": "2024-09-23",
  "Author": "Kathryn Leeming [aut], Guy Nason [aut], Matt Nunes [aut, cre],\nMarina Knight [ctb], James Wei [aut], Daniel Salnikov [aut],\nMario Cortina Borja [ctb]",
  "Authors@R": "c(person(\"Kathryn\", \"Leeming\", role=\"aut\"), person(\"Guy\",\"Nason\",role=\"aut\", email=\"g.nason@imperial.ac.uk\"), person(\"Matt\",\n\"Nunes\", role=c(\"aut\",\"cre\"),email=\"nunesrpackages@gmail.com\"), person(\"Marina\",\"Knight\",role=\"ctb\"), person(\"James\", \"Wei\", role=\"aut\"), person(\"Daniel\", \"Salnikov\",\nrole=c(\"aut\"),email=\"d.salnikov22@imperial.ac.uk\"), person(\"Mario\", \"Cortina Borja\", role=c(\"ctb\"),email=\"m.cortina@ucl.ac.uk\"))",
  "Maintainer": "Matt Nunes <nunesrpackages@gmail.com>",
  "Description": "Simulation of, and fitting models for, Generalised Network\nAutoregressive (GNAR) time series models which take account of\nnetwork structure, potentially with exogenous variables.  Such\nmodels are described in Knight et al. (2020)\n<doi:10.18637/jss.v096.i05> and Nason and Wei (2021)\n<doi:10.1111/rssa.12875>.  Diagnostic tools for GNAR(X) models\ncan be found in Nason et al. (2023)\n<doi:10.48550/arXiv.2312.00530>.",
  "License": "GPL-2",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-06 07:18:05 UTC",
    "User": "root"
  },
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2024-10-03 02:59:45 UTC",
  "RemoteUrl": "https://github.com/cran/GNAR",
  "RemoteRef": "HEAD",
  "RemoteSha": "93f1211efc5e495b1afa118cb1bc45d2a637f8b0",
  "MD5sum": "8238d9040514d34faf0a15a8c0a331cd",
  "_user": "cran",
  "_type": "src",
  "_file": "GNAR_1.1.4.tar.gz",
  "_fileid": "ad67d09303f0474ed8f6bed7ea7679494bde08001283d59a223906855f0490c2",
  "_filesize": 595905,
  "_sha256": "ad67d09303f0474ed8f6bed7ea7679494bde08001283d59a223906855f0490c2",
  "_created": "2026-06-06T07:18:05.000Z",
  "_published": "2026-06-06T07:21:17.121Z",
  "_distro": "noble",
  "_jobs": [
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  "_buildurl": "https://github.com/r-universe/cran/actions/runs/27055949703",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/GNAR",
  "_commit": {
    "id": "93f1211efc5e495b1afa118cb1bc45d2a637f8b0",
    "author": "Matt Nunes <nunesrpackages@gmail.com>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.1.4\n",
    "time": 1727924385
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  "_maintainer": {
    "name": "Matt Nunes",
    "email": "nunesrpackages@gmail.com"
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 3.5.0",
      "role": "Depends"
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    {
      "package": "ggforce",
      "role": "Imports"
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    {
      "package": "ggplot2",
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    },
    {
      "package": "ggpubr",
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    },
    {
      "package": "grid",
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    },
    {
      "package": "igraph",
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    },
    {
      "package": "matrixcalc",
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    },
    {
      "package": "rlang",
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    },
    {
      "package": "stats",
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    },
    {
      "package": "viridis",
      "role": "Imports"
    },
    {
      "package": "wordcloud",
      "role": "Imports"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_stars": 2,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 245,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/GNAR"
  },
  "_searchresults": 65,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/GNAR.html",
    "manual.pdf"
  ],
  "_realowner": "cran",
  "_cranurl": false,
  "_releases": [
    {
      "version": "0.2.7",
      "date": "2018-09-24"
    },
    {
      "version": "0.2.9",
      "date": "2018-11-02"
    },
    {
      "version": "0.3.6",
      "date": "2019-06-07"
    },
    {
      "version": "1.0",
      "date": "2019-10-29"
    },
    {
      "version": "1.1.0",
      "date": "2020-05-31"
    },
    {
      "version": "1.1.1",
      "date": "2020-11-11"
    },
    {
      "version": "1.1.2",
      "date": "2023-04-27"
    },
    {
      "version": "1.1.3",
      "date": "2023-12-18"
    },
    {
      "version": "1.1.4",
      "date": "2024-10-02"
    }
  ],
  "_exports": [
    "active_node_plot",
    "as.GNARnet",
    "corbit_plot",
    "cross_correlation_plot",
    "get_k_stages_adjacency_tensor",
    "GNARdesign",
    "GNARfit",
    "GNARsim",
    "GNARtoigraph",
    "GNARXdesign",
    "GNARXfit",
    "GNARXsim",
    "igraphtoGNAR",
    "is.GNARfit",
    "is.GNARnet",
    "local_relevance_plot",
    "matrixtoGNAR",
    "na.row",
    "nacf",
    "node_relevance_plot",
    "NofNeighbours",
    "pnacf",
    "r_corbit_plot",
    "residToMat",
    "seedToNet",
    "weights_matrix",
    "windnetplot"
  ],
  "_datasets": [
    {
      "name": "fiveNet",
      "title": "Example Network Time Series",
      "object": "fiveNode",
      "class": [
        "GNARnet"
      ],
      "fields": [],
      "table": true,
      "tojson": false
    },
    {
      "name": "fiveVTS",
      "title": "Example Network Time Series",
      "object": "fiveNode",
      "class": [
        "ts"
      ],
      "fields": {},
      "rows": 200,
      "table": true,
      "tojson": true
    },
    {
      "name": "gdpVTS",
      "title": "Differenced GDP values for 35 countries",
      "object": "gdp",
      "class": [
        "mts",
        "ts",
        "matrix"
      ],
      "fields": [
        "AUS",
        "AUT",
        "BEL",
        "CAN",
        "CHL",
        "CZE",
        "DNK",
        "EST",
        "FIN",
        "FRA",
        "DEU",
        "GRC",
        "HUN",
        "ISL",
        "IRL",
        "ISR",
        "ITA",
        "JPN",
        "KOR",
        "LVA",
        "LUX",
        "MEX",
        "NLD",
        "NZL",
        "NOR",
        "POL",
        "PRT",
        "SVK",
        "SVN",
        "ESP",
        "SWE",
        "CHE",
        "TUR",
        "GBR",
        "USA"
      ],
      "rows": 52,
      "table": true,
      "tojson": true
    },
    {
      "name": "logMVbedMVC.vts",
      "title": "Number of COVID19 admission to mechanical ventilation beds",
      "object": "logMVbedMVC.vts",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "RCF",
        "RBS",
        "RTK",
        "RF4",
        "RFF",
        "R1H",
        "RC9",
        "RQ3",
        "RXL",
        "RMC",
        "RAE",
        "RXH",
        "RXQ",
        "RWY",
        "RGT",
        "RT1",
        "RQM",
        "RFS",
        "RJR",
        "RXP",
        "RJ6",
        "RN7",
        "RP5",
        "RBD",
        "RDY",
        "RJN",
        "RVV",
        "RXR",
        "RDE",
        "RXC",
        "RWH",
        "RVR",
        "RDU",
        "RR7",
        "RLT",
        "RTQ",
        "RP4",
        "RN3",
        "RJ1",
        "RN5",
        "RCD",
        "RQX",
        "RWA",
        "RYJ",
        "R1F",
        "RGP",
        "RNQ",
        "RJZ",
        "RAX",
        "RXN",
        "RR8",
        "RJ2",
        "RBQ",
        "REM",
        "R1K",
        "RWF",
        "R0A",
        "RPA",
        "RBT",
        "RXF",
        "RAJ",
        "RD8",
        "RM1",
        "RVJ",
        "RNN",
        "RAP",
        "RVW",
        "RGN",
        "RNS",
        "RP1",
        "RBZ",
        "RJL",
        "RTF",
        "RX1",
        "RNU",
        "RTH",
        "RW6",
        "RHU",
        "RXE",
        "RHW",
        "REF",
        "RH8",
        "RAL",
        "RAN",
        "RGM",
        "RA2",
        "RD1",
        "RM3",
        "RNZ",
        "RXK",
        "RCU",
        "RHQ",
        "RK5",
        "RH5",
        "RTR",
        "R0B",
        "RJC",
        "RVY",
        "RJ7",
        "RBN",
        "RWJ",
        "RTP",
        "RMP",
        "REN",
        "RNA",
        "RAS",
        "RTD",
        "RQW",
        "RCX",
        "RFR",
        "RPY",
        "RRJ",
        "RL4",
        "RXW",
        "RET",
        "RA9",
        "RWD",
        "RRV",
        "RHM",
        "RRK",
        "RA7",
        "RKB",
        "R0D",
        "RK9",
        "RTG",
        "RWE",
        "RTX",
        "RJE",
        "RBK",
        "RWW",
        "RWG",
        "RGR",
        "RYR",
        "RKE",
        "RBL",
        "RWP",
        "RRF",
        "RLQ",
        "RA4",
        "RCB"
      ],
      "rows": 452,
      "table": true,
      "tojson": true
    },
    {
      "name": "NHSTrustMVCAug120.net",
      "title": "Constructed network linking 140 NHS Trusts in England and Wales",
      "object": "NHSTrustMVCAug120.net",
      "class": [
        "GNARnet"
      ],
      "fields": [],
      "table": true,
      "tojson": false
    },
    {
      "name": "seed.nos",
      "title": "Vector of seed numbers",
      "object": "gdp",
      "class": [
        "integer"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "vswindcoords",
      "title": "Wind Speed example network time series",
      "object": "vswind",
      "class": [
        "data.frame"
      ],
      "fields": [
        "x",
        "y"
      ],
      "rows": 102,
      "table": true,
      "tojson": true
    },
    {
      "name": "vswindnames",
      "title": "Wind Speed example network time series",
      "object": "vswind",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "vswindnet",
      "title": "Wind Speed example network time series",
      "object": "vswind",
      "class": [
        "GNARnet"
      ],
      "fields": [],
      "table": true,
      "tojson": false
    },
    {
      "name": "vswindnetD",
      "title": "Wind Speed example network time series",
      "object": "vswind",
      "class": [
        "GNARnet"
      ],
      "fields": [],
      "table": true,
      "tojson": false
    },
    {
      "name": "vswindts",
      "title": "Wind Speed example network time series",
      "object": "vswind",
      "class": [
        "mts",
        "ts",
        "matrix"
      ],
      "fields": [
        "1145",
        "1171",
        "1137",
        "17309",
        "1144",
        "30690",
        "521",
        "55511",
        "556",
        "381",
        "384",
        "386",
        "405",
        "393",
        "16725",
        "1161",
        "1190",
        "643",
        "576",
        "583",
        "395",
        "409",
        "421",
        "1198",
        "1209",
        "16611",
        "669",
        "24996",
        "657",
        "19187",
        "595",
        "461",
        "440",
        "1215",
        "1226",
        "1255",
        "676",
        "692",
        "605",
        "613",
        "17176",
        "709",
        "19188",
        "498",
        "1346",
        "1285",
        "19206",
        "886",
        "888",
        "889",
        "847",
        "862",
        "869",
        "30620",
        "708",
        "726",
        "16588",
        "743",
        "775",
        "1395",
        "1393",
        "1415",
        "1336",
        "1383",
        "1302",
        "1319",
        "842",
        "876",
        "779",
        "858",
        "795",
        "811",
        "744",
        "1125",
        "593",
        "1135",
        "18912",
        "466",
        "725",
        "18929",
        "18923",
        "18919",
        "484",
        "18930",
        "18920",
        "456",
        "554",
        "24102",
        "719",
        "723",
        "56214",
        "25727",
        "17224",
        "1115",
        "18904",
        "1352",
        "1367",
        "18925",
        "1101",
        "18937",
        "1378",
        "854"
      ],
      "rows": 721,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "active_node_plot",
      "title": "Produces an active node matrix heat-map.",
      "topics": [
        "active_node_plot"
      ]
    },
    {
      "page": "AIC.GNARfit",
      "title": "Akaike's Information Criterion for GNAR models",
      "topics": [
        "AIC.GNARfit"
      ]
    },
    {
      "page": "as.matrix.GNARnet",
      "title": "Converts a GNAR networks into a weighted adjacency matrix",
      "topics": [
        "as.matrix.GNARnet"
      ]
    },
    {
      "page": "BIC.GNARfit",
      "title": "Bayesian Information Criterion for GNAR models",
      "topics": [
        "BIC.GNARfit"
      ]
    },
    {
      "page": "coef.GNARfit",
      "title": "Function to return coefficients of GNARfit objects",
      "topics": [
        "coef.GNARfit"
      ]
    },
    {
      "page": "corbit_plot",
      "title": "Corbit (correlation-orbit) plot, which aids model selection by visualising network autocorrelation and partial network autocorrelation.",
      "topics": [
        "corbit_plot"
      ]
    },
    {
      "page": "fitted.GNARfit",
      "title": "Function to return fitted values of GNARfit objects",
      "topics": [
        "fitted.GNARfit"
      ]
    },
    {
      "page": "fiveNode",
      "title": "Example Network Time Series",
      "topics": [
        "fiveNet",
        "fiveNode",
        "fiveVTS"
      ]
    },
    {
      "page": "gdpVTS",
      "title": "Differenced GDP values for 35 countries",
      "topics": [
        "gdpVTS"
      ]
    },
    {
      "page": "get_k_stages_adjacency_tensor",
      "title": "Computes a list of r-stage adjacency matrices.",
      "topics": [
        "get_k_stages_adjacency_tensor"
      ]
    },
    {
      "page": "GNAR",
      "title": "GNAR package",
      "topics": [
        "GNAR"
      ]
    },
    {
      "page": "GNARdesign",
      "title": "Function to create the GNAR design matrix",
      "topics": [
        "GNARdesign"
      ]
    },
    {
      "page": "GNARfit",
      "title": "Fitting function for GNAR models",
      "topics": [
        "GNARfit"
      ]
    },
    {
      "page": "GNARsim",
      "title": "Simulates a GNAR process",
      "topics": [
        "GNARsim"
      ]
    },
    {
      "page": "GNARtoigraph",
      "title": "Converts a GNAR network to a weighted igraph object",
      "topics": [
        "GNARtoigraph"
      ]
    },
    {
      "page": "GNARXdesign",
      "title": "Function to create the GNARX design matrix",
      "topics": [
        "GNARXdesign"
      ]
    },
    {
      "page": "GNARXfit",
      "title": "Fitting function for GNARX models",
      "topics": [
        "GNARXfit"
      ]
    },
    {
      "page": "GNARXsim",
      "title": "Simulates a GNARX process",
      "topics": [
        "GNARXsim"
      ]
    },
    {
      "page": "igraphtoGNAR",
      "title": "Converts an igraph to GNAR network",
      "topics": [
        "igraphtoGNAR"
      ]
    },
    {
      "page": "is.GNARfit",
      "title": "Function to check GNARfit objects",
      "topics": [
        "is.GNARfit"
      ]
    },
    {
      "page": "is.GNARnet",
      "title": "Functions to check and create GNARnet objects",
      "topics": [
        "as.GNARnet",
        "is.GNARnet"
      ]
    },
    {
      "page": "local_relevance_plot",
      "title": "Produces a local neighbourhood relevance plot based on the distances in the underlying network.",
      "topics": [
        "cross_correlation_plot",
        "local_relevance_plot"
      ]
    },
    {
      "page": "logLik.GNARfit",
      "title": "Log-likelihood method for GNARfit objects",
      "topics": [
        "logLik.GNARfit"
      ]
    },
    {
      "page": "logMVbedMVC.vts",
      "title": "Number of COVID19 admission to mechanical ventilation beds",
      "topics": [
        "logMVbedMVC.vts"
      ]
    },
    {
      "page": "matrixtoGNAR",
      "title": "Converts an adjacency matrix to GNAR network",
      "topics": [
        "matrixtoGNAR"
      ]
    },
    {
      "page": "na.row",
      "title": "Identifies which rows of a matrix have NAs",
      "topics": [
        "na.row"
      ]
    },
    {
      "page": "nacf",
      "title": "Computes the Network Autocorrelation Function (NACF)",
      "topics": [
        "nacf"
      ]
    },
    {
      "page": "NHSTrustMVCAug120.net",
      "title": "Constructed network linking 140 NHS Trusts in England and Wales",
      "topics": [
        "NHSTrustMVCAug120.net"
      ]
    },
    {
      "page": "nobs.GNARfit",
      "title": "Function to return the number of observations input to GNARfit objects",
      "topics": [
        "nobs.GNARfit"
      ]
    },
    {
      "page": "node_relevance_plot",
      "title": "Produces a node relevance plot, which compares the impact each node has on the network autocorrelation once a model order has been chosen.",
      "concept": [
        "GNAR"
      ],
      "topics": [
        "node_relevance_plot"
      ]
    },
    {
      "page": "NofNeighbours",
      "title": "Calculates stage-neighbours of a network",
      "topics": [
        "NofNeighbours"
      ]
    },
    {
      "page": "plot.GNARnet",
      "title": "Plot function for GNAR networks",
      "topics": [
        "plot.GNARnet"
      ]
    },
    {
      "page": "pnacf",
      "title": "Computes the Partial Network Autocorrelation Function (PNACF)",
      "topics": [
        "pnacf"
      ]
    },
    {
      "page": "predict.GNARfit",
      "title": "Prediction of a GNARfit object",
      "topics": [
        "predict.GNARfit"
      ]
    },
    {
      "page": "print.GNARfit",
      "title": "Function to print the model and coefficients of GNARfit objects",
      "topics": [
        "print.GNARfit"
      ]
    },
    {
      "page": "print.GNARnet",
      "title": "Print function for GNAR networks",
      "topics": [
        "print.GNARnet"
      ]
    },
    {
      "page": "r_corbit_plot",
      "title": "Produces a R-Corbit plot for the specified choice of covariate-levels and/or time-slices.",
      "concept": [
        "GNAR",
        "R-Corbit",
        "Corbit"
      ],
      "topics": [
        "r_corbit_plot"
      ]
    },
    {
      "page": "residToMat",
      "title": "Converts the output of a GNARfit call to fitted and residual value matrices",
      "topics": [
        "residToMat"
      ]
    },
    {
      "page": "residuals.GNARfit",
      "title": "Function to return residuals of GNARfit objects",
      "topics": [
        "residuals.GNARfit"
      ]
    },
    {
      "page": "seed.nos",
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