{
  "_id": "6ab2454a3d719c1f8408b1cf",
  "Package": "coconots",
  "Type": "Package",
  "Title": "Convolution-Closed Models for Count Time Series",
  "Version": "2.0.4",
  "Date": "2026-08-23",
  "Authors@R": "c(\nperson(given = \"Manuel\",\nfamily = \"Huth\",\nrole = c(\"aut\", \"cre\"),\nemail = \"manuel.huth@yahoo.com\"),\nperson(given = \"Robert C.\",\nfamily = \"Jung\",\nrole = \"aut\"),\nperson(given = \"Andy\",\nfamily = \"Tremayne\",\nrole =  \"aut\"\n))",
  "Description": "Useful tools for fitting, validating, and forecasting of\npractical convolution-closed time series models for low counts\nare provided. Marginal distributions of the data can be\nmodelled via Poisson and Generalized Poisson innovations.\nRegression effects can be incorporated through time varying\ninnovation rates. The models are described in Jung and Tremayne\n(2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model\nassessment tools are presented in Czado et al. (2009)\n<doi:10.1111/j.1541-0420.2009.01191.x> and, Tsay (1992)\n<doi:10.2307/2347612>.",
  "URL": "https://github.com/manuhuth/coconots,\nhttps://manuhuth.github.io/coconots/",
  "BugReports": "https://github.com/manuhuth/coconots/issues",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "Config/testthat/edition": "3",
  "Config/Needs/website": "pkgdown",
  "License": "MIT + file LICENSE",
  "Repository": "https://manuhuth.r-universe.dev",
  "Date/Publication": "2026-08-23 12:17:48 UTC",
  "RemoteUrl": "https://github.com/manuhuth/coconots",
  "RemoteRef": "HEAD",
  "RemoteSha": "1d6c855993a65524c74d4b6091041e5eb73ac9eb",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-09-22 08:58:13 UTC",
    "User": "root"
  },
  "Author": "Manuel Huth [aut, cre],\nRobert C. Jung [aut],\nAndy Tremayne [aut]",
  "Maintainer": "Manuel Huth <manuel.huth@yahoo.com>",
  "_user": "manuhuth",
  "_type": "src",
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  "_created": "2026-09-22T08:58:13.000Z",
  "_published": "2026-09-22T09:07:22.598Z",
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  "_buildurl": "https://github.com/r-universe/manuhuth/actions/runs/35707375993",
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    "author": "Manuel Huth <64041311+manuhuth@users.noreply.github.com>",
    "committer": "GitHub <noreply@github.com>",
    "message": "Release 2.0.4 to clear the CRAN valgrind issue (#11)\n\nCRAN's valgrind run on 2.0.3 reports a heap over-read:\n\n  Invalid read of size 8\n    at inner_product<double*, double*, double> (stl_numeric.h:198)\n    by innerProduct(...)     src/cfunctions.cpp:36\n    by likelihoodGP2cov(...) src/cfunctions.cpp:528\n  Address ... is 0 bytes after a block of size 7,960 alloc'd\n  ERROR SUMMARY: 298 errors from 1 contexts\n\nThe post-hoc log-likelihood for Poisson models with covariates reused the\nGeneralized-Poisson parameter slicing, which drops eta. Poisson models carry\nno eta, so the slice also discarded beta1 and passed a coefficient vector one\nelement shorter than the number of covariate columns; the kernel iterates over\nthe covariate row and read one double past its end on every observation\n(T - seas2 = 298 in the affected test, matching the error count).\n\nThe code fix already landed in 02fd036 but has never been on CRAN, so this is\na release, not a code change: bump to 2.0.4, write the NEWS entry for the\nunreleased fixes and performance work (#6-#10), and rewrite cran-comments.md\nfor the resubmission.\n\nAlso add the regression test that was missing: for Poisson covariate models the\nstored $likelihood must equal the GP objective evaluated at eta = 0. Verified it\nfails against the old slicing for both order 1 and order 2, and passes on the\nfix. R CMD check --as-cran: 0 errors, 0 warnings, 2 notes (both local tooling).\n\nCo-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>",
    "time": 1787487468
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    "email": "manuel.huth@yahoo.com",
    "login": "manuhuth",
    "description": "PhD candidate in Mathematics with a focus on Computational Methods.",
    "uuid": 64041311
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      "role": "Suggests"
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  "_owner": "manuhuth",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [
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      "week": "2026-23",
      "n": 2
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    "cocoPit",
    "cocoReg",
    "cocoResid",
    "cocoScore",
    "cocoSim",
    "cocoSoc",
    "installJuliaPackages",
    "setJuliaSeed"
  ],
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      "name": "cuts",
      "title": "Time Series of Monthly Counts of Claimants Collecting Wage Loss Benefit",
      "object": "cuts",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "downloads",
      "title": "Time Series of Daily Downloads of a TeX-Editor",
      "object": "downloads",
      "class": [
        "ts"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "goldparticle",
      "title": "Time Series of Gold Particle Counts in a Well-Defined Colloidal Solution",
      "object": "goldparticle",
      "class": [
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      ],
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      "table": false,
      "tojson": true
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      "title": "Bootstrap Based Model Assessment Procedure",
      "topics": [
        "cocoBoot"
      ]
    },
    {
      "page": "cocoPit",
      "title": "Probability Integral Transform Based Model Assessment Procedure",
      "topics": [
        "cocoPit"
      ]
    },
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      "page": "cocoReg",
      "title": "Fitting First- and Second Order (G)PAR Models",
      "topics": [
        "cocoReg"
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    },
    {
      "page": "cocoResid",
      "title": "Residual Based Model Assessment Procedure",
      "topics": [
        "cocoResid"
      ]
    },
    {
      "page": "cocoScore",
      "title": "Scoring Rule Based Model Assessment Procedure",
      "topics": [
        "cocoScore"
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