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  "Title": "MAnifold-Constrained Gaussian Process Inference",
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  "Date": "2025-10-22",
  "Authors@R": "c(person(given = \"Shihao\",\nfamily = \"Yang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"shihao.yang@isye.gatech.edu\",\ncomment = c(ORCID = \"0000-0003-3910-4969\")),\nperson(given = \"Samuel W.K.\",\nfamily = \"Wong\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-7325-7267\")),\nperson(given = \"S.C.\",\nfamily = \"Kou\",\nrole = c(\"ctb\", \"cph\"),\ncomment = c(role = \"Contributor of MAGI method development\"))\n)",
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  "Description": "Provides fast and accurate inference for the parameter\nestimation problem in Ordinary Differential Equations,\nincluding the case when there are unobserved system components.\nImplements the MAGI method (MAnifold-constrained Gaussian\nprocess Inference) of Yang, Wong, and Kou (2021)\n<doi:10.1073/pnas.2020397118>. A user guide is provided by the\naccompanying software paper Wong, Yang, and Kou (2024)\n<doi:10.18637/jss.v109.i04>.",
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        "magioutput"
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