{
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  "Title": "Convolution-Closed Models for Count Time Series",
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  "Date": "2026-06-06",
  "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>.",
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  "Repository": "https://manuhuth.r-universe.dev",
  "Date/Publication": "2026-06-06 15:57:32 UTC",
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