{ "_id": "6a2287d9cd65a98ecbd5381d", "Package": "BMIselect", "Title": "Bayesian MI-LASSO for Variable Selection on Multiply-Imputed\nDatasets", "Version": "1.0.3", "Author": "Jungang Zou [aut, cre], Sijian Wang [aut], Qixuan Chen [aut]", "Authors@R": "c(person(\"Jungang\", \"Zou\", email = \"jungang.zou@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(given = \"Sijian\",\nfamily = \"Wang\",\nrole = c(\"aut\")\n),\nperson(given = \"Qixuan\",\nfamily = \"Chen\",\nrole = c(\"aut\")\n))", "Maintainer": "Jungang Zou ", "Description": "Provides a suite of Bayesian MI-LASSO for variable\nselection methods for multiply-imputed datasets. The package\nincludes four Bayesian MI-LASSO models using shrinkage\n(Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab\n(Spike-and-Laplace) priors, along with tools for model fitting\nvia MCMC, four-step projection predictive variable selection,\nand hyperparameter calibration. Methods are suitable for both\ncontinuous and binary covariates under missing-at-random or\nmissing-completely-at-random assumptions. See Zou, J., Wang, S.\nand Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection\non Multiply-Imputed Data. ArXiv, 2211.00114.\n for more details. 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