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  "Title": "Robust Bayesian Variable Selection via Expectation-Maximization",
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  "Date": "2024-08-26",
  "Authors@R": "c( person(\"Yuwen\", \"Liu\", role = c(\"aut\", \"cre\") , email = \"yuwenliu9@gmail.com\"),\nperson(\"Cen\", \"Wu\", role = \"aut\"))",
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  "Description": "Variable selection methods have been extensively developed\nfor analyzing highdimensional omics data within both the\nfrequentist and Bayesian frameworks. This package provides\nimplementations of the spike-and-slab quantile (group) LASSO\nwhich have been developed along the line of Bayesian\nhierarchical models but deeply rooted in frequentist\nregularization methods by utilizing Expectation–Maximization\n(EM) algorithm. The spike-and-slab quantile LASSO can handle\ndata irregularity in terms of skewness and outliers in response\nvariables, compared to its non-robust alternative, the\nspike-and-slab LASSO, which has also been implemented in the\npackage. In addition, procedures for fitting the spike-and-slab\nquantile group LASSO and its non-robust counterpart have been\nimplemented in the form of quantile/least-square varying\ncoefficient mixed effect models for high-dimensional\nlongitudinal data. The core module of this package is developed\nin 'C++'.",
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