{ "_id": "6a1023feacfb0bcc41c8da3f", "Package": "BayesPocket", "Type": "Package", "Title": "Bayesian Causal Inference for Periodontal Diseases in\nLongitudinal Studies", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Qingyang\", \"Liu\", email = \"rh8liuqy@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-3265-6330\")),\nperson(\"Debdeep\", \"Pati\", email = \"dpati2@wisc.edu\", role = c(\"aut\")),\nperson(\"Yang\", \"Ni\", email = \"yang.ni@austin.utexas.edu\", role = c(\"aut\")),\nperson(\"Dipankar\", \"Bandyopadhyay\", email = \"dbandyop@vcu.edu\", role = \"aut\")\n)", "Description": "Implements the Mixed Treatment-State Causal Model (MTSCM),\na Bayesian framework for estimating causal effects of clinical\ninterventions on bounded continuous outcomes in longitudinal\nobservational studies with irregular visits. The methodology is\nspecifically designed for periodontal disease research, where\ndiscrete treatments and continuous disease states (e.g.,\nproportion of periodontal pockets exceeding 3 mm) reciprocally\ninfluence one another under dynamic feedback. The package\nintegrates a double-censored Tobit likelihood to handle\nboundary mass at zero and one, subject-specific random effects\nto capture within-subject correlation, and flexible tree-based\nensemble priors (standard BART and Soft BART) to model complex\nnonlinear interactions without parametric restrictions. Causal\nidentification is established under the potential outcomes\nframework via the G-computation formula, with key estimands\nincluding the Mixed Average Potential Outcome (MAPO) and the\nMixed Probability of Disease Resolution (MPDR). 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