{ "_id": "6a103a77acfb0bcc41c9b3ae", "Package": "VCBART", "Type": "Package", "Title": "Fit Varying Coefficient Models with Bayesian Additive Regression\nTrees", "Version": "1.2.5", "Date": "2026-04-17", "Authors@R": "c(\nperson(given = \"Sameer K.\", family = \"Deshpande\", role = c(\"aut\", \"cre\"), email = \"sameer.deshpande@wisc.edu\", comment = c(ORCID = \"0000-0003-4116-5533\")),\nperson(given = \"Ray\", family = \"Bai\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-7190-7844\")),\nperson(given = \"Cecilia\", family = \"Balocchi\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-1234-6063\")),\nperson(given = \"Jennifer\", family = \"Starling\", role = c(\"aut\")),\nperson(given = \"Jordan\", family = \"Weiss\", role = c(\"aut\")))", "Description": "Fits linear varying coefficient (VC) models, which assert\na linear relationship between an outcome and several covariates\nbut allow that relationship (i.e., the coefficients or slopes\nin the linear regression) to change as functions of additional\nvariables known as effect modifiers, by approximating the\ncoefficient functions with Bayesian Additive Regression Trees.\nImplements a Metropolis-within-Gibbs sampler to simulate draws\nfrom the posterior over coefficient function evaluations. 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