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  "Title": "Continuous Time Stochastic Modelling using Template Model\nBuilder",
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  "Date": "2026-02-01",
  "Authors@R": "c(\nperson(\"Phillip\", \"Vetter\", , \"pbrve@dtu.dk\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Jan\", \"Møller\", , \"jkmo@dtu.dk\", role = \"ctb\"),\nperson(\"Uffe\", \"Thygesen\", , \"uhth@dtu.dk\", role = \"ctb\"),\nperson(\"Peder\", \"Bacher\", , \"pbac@dtu.dk\", role = \"ctb\"),\nperson(\"Henrik\", \"Madsen\", , \"hmad@dtu.dk\", role = \"ctb\")\n)",
  "Description": "Perform state and parameter inference, and forecasting, in\nstochastic state-space systems using the 'ctsmTMB' R6 class.\nThis class provides a user-friendly interface for working with\nstochastic state space models. Inference is based on maximum\nlikelihood estimation, with derivatives efficiently computed\nthrough automatic differentiation enabled by the 'TMB'/'RTMB'\npackages (Kristensen et al., 2016) <doi:10.18637/jss.v070.i05>.\nThe available inference methods include Kalman filters, in\naddition to a Laplace approximation-based smoothing method. For\nfurther details of these methods refer to the documentation of\nthe 'CTSMR' package <https://ctsm.info/ctsmr-reference.pdf> and\nThygesen (2025) <doi:10.48550/arXiv.2503.21358>. Forecasting\ncapabilities include moment predictions and stochastic path\nsimulations implemented in 'C++' using 'Rcpp' (Eddelbuettel et\nal., 2018) <doi:10.1080/00031305.2017.1375990> for\ncomputational efficiency.",
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