Component-Wise Gradient Boosting after Multiple Imputation
Predict with booami models
Boosting with Multiple Imputation (booami)
Cross-validated boosting on already-imputed data
Cross-Validated Component-Wise Gradient Boosting with Multiple Imputat...
Component-Wise Gradient Boosting Across Multiply Imputed Datasets
Predict from booami objects
Simulate a Booami Example Dataset with Missing Values
Component-wise gradient boosting for analysis of multiply imputed datasets. Implements the algorithm Boosting after Multiple Imputation (MIBoost), which enforces uniform variable selection across imputations and provides utilities for pooling. Includes a cross-validation workflow that first splits the data into training and validation sets and then performs imputation on the training data, applying the learned imputation models to the validation data to avoid information leakage. Supports Gaussian and logistic loss. Methods relate to gradient boosting and multiple imputation as in Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>, Friedman (2001) <doi:10.1214/aos/1013203451>, and van Buuren (2018, ISBN:9781138588318) and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; see also Kuchen (2025) <doi:10.48550/arXiv.2507.21807>.
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