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  "Title": "Analysis of Count Time Series",
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  "Date": "2023-05-10",
  "Authors@R": "c(\nperson(\"Tobias\", \"Liboschik\", role=c(\"aut\", \"cre\"), email=\"liboschik@statistik.tu-dortmund.de\"),\nperson(\"Roland\", \"Fried\", role=c(\"aut\"), email=\"fried@statistik.tu-dortmund.de\"),\nperson(\"Konstantinos\", \"Fokianos\", role=c(\"aut\"), email=\"fokianos@ucy.ac.cy\"),\nperson(\"Philipp\", \"Probst\", role=c(\"aut\"), email=\"philipp.probst@tu-dortmund.de\"),\nperson(\"Jonathan\", \"Rathjens\", role=c(\"ctb\"), email=\"jonathan.rathjens@tu-dortmund.de\"),\nperson(\"Nicolò\", \"Rubattu\", role=c(\"ctb\"), email=\"nicolo.rubattu@idsia.ch\"))",
  "Description": "Likelihood-based methods for model fitting and assessment,\nprediction and intervention analysis of count time series\nfollowing generalized linear models are provided, see Liboschik\net al. (2017) <doi:10.18637/jss.v082.i05>. Models with the\nidentity and with the logarithmic link function are allowed.\nThe conditional distribution can be Poisson or Negative\nBinomial.",
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