booami: Component-Wise Gradient Boosting after Multiple Imputation

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>.

Version:	0.1.0
Depends:	R (≥ 4.0)
Imports:	MASS, stats, utils, withr
Suggests:	mice, miceadds, Matrix, knitr, rmarkdown, testthat (≥ 3.0.0), spelling
Published:	2025-09-04
DOI:	10.32614/CRAN.package.booami
Author:	Robert Kuchen [aut, cre]
Maintainer:	Robert Kuchen <rokuchen at uni-mainz.de>
BugReports:	https://github.com/RobertKuchen/booami/issues
License:	MIT + file LICENSE
URL:	https://arxiv.org/abs/2507.21807, https://github.com/RobertKuchen/booami
NeedsCompilation:	no
Language:	en-US
Citation:	booami citation info
CRAN checks:	booami results

Documentation:

Reference manual:

booami.html , booami.pdf

Downloads:

Package source:	booami_0.1.0.tar.gz
Windows binaries:	r-devel: booami_0.1.0.zip, r-release: booami_0.1.0.zip, r-oldrel: booami_0.1.0.zip
macOS binaries:	r-release (arm64): booami_0.1.0.tgz, r-oldrel (arm64): booami_0.1.0.tgz, r-release (x86_64): booami_0.1.0.tgz, r-oldrel (x86_64): booami_0.1.0.tgz

Linking:

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