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  "Title": "Co-Data Learning for Bayesian Additive Regression Trees",
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  "Description": "Estimate prior variable weights for Bayesian Additive\nRegression Trees (BART). These weights correspond to the\nprobabilities of the variables being selected in the splitting\nrules of the sum-of-trees. Weights are estimated using\nempirical Bayes and external information on the explanatory\nvariables (co-data). BART models are fitted using the 'dbarts'\n'R' package. See Goedhart and others (2023)\n<doi:10.1002/sim.70004> for details.",
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