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  "Title": "Likelihood Based Inference for ARIMA Modeling",
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  "Date": "2025-12-10",
  "Authors@R": "c(\nperson(\"Jesse\",\n\"Wheeler\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"jessewheeler@isu.edu\"),\nperson(\"Noel\",\n\"McAllister\",\nrole = \"aut\",\nemail = \"noelmcp@umich.edu\"),\nperson(\"Dhajanae\",\n\"Sylvertooth\",\nrole = \"aut\",\nemail = \"dsylvert@umich.edu\"),\nperson(\"Edward\",\n\"Ionides\",\nrole = \"ctb\"),\nperson(\"Brian\",\n\"Ripley\",\nrole = \"ctb\",\ncomment = \"Author of arima source code in stats package.\"),\nperson(\"R Core Team\",\nrole = \"cph\",\ncomment = \"Author of arima source code in stats package.\"))",
  "Description": "Estimating and analyzing auto regressive integrated moving\naverage (ARIMA) models. The primary function in this package is\narima(), which fits an ARIMA model to univariate time series\ndata using a random restart algorithm. This approach frequently\nleads to models that have model likelihood greater than or\nequal to that of the likelihood obtained by fitting the same\nmodel using the arima() function from the 'stats' package. This\npackage enables proper optimization of model likelihoods, which\nis a necessary condition for performing likelihood ratio tests.\nThis package relies heavily on the source code of the arima()\nfunction of the 'stats' package. For more information, please\nsee Jesse Wheeler and Edward L. Ionides (2025)\n<doi:10.1371/journal.pone.0333993>.",
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