{ "_id": "6a103b48acfb0bcc41c9b6ec", "Package": "dlmtree", "Type": "Package", "Title": "Bayesian Treed Distributed Lag Models", "Version": "1.1.1", "Authors@R": "c(\nperson(\"Daniel\", \"Mork\", email = \"dmork@hsph.harvard.edu\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0002-7924-0706\")),\nperson(\"Seongwon\", \"Im\", email = \"Seongwon.Im@colostate.edu\", role = \"aut\", comment = c(ORCID = \"0009-0000-8447-5852\")),\nperson(\"Ander\", \"Wilson\", email = \"Ander.Wilson@colostate.edu\", role = \"aut\", comment = c(ORCID = \"0000-0003-4774-3883\")))", "Description": "Estimation of distributed lag models (DLMs) based on a\nBayesian additive regression trees framework. Includes several\nextensions of DLMs: treed DLMs and distributed lag mixture\nmodels (Mork and Wilson, 2023) ; treed\ndistributed lag nonlinear models (Mork and Wilson, 2022)\n; heterogeneous DLMs (Mork,\net. al., 2024) ; monotone\nDLMs (Mork and Wilson, 2024) . 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