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booami  

Component-Wise Gradient Boosting after Multiple Imputation
View on CRAN: Click here


Download and install booami package within the R console
Install from CRAN:
install.packages("booami")

Install from Github:
library("remotes")
install_github("cran/booami")

Install by package version:
library("remotes")
install_version("booami", "0.1.1")



Attach the package and use:
library("booami")
Maintained by
Robert Kuchen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-09-04
Latest Update: 2025-09-04
Description:
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>.
How to cite:
Robert Kuchen (2025). booami: Component-Wise Gradient Boosting after Multiple Imputation. R package version 0.1.1, https://cran.r-project.org/web/packages/booami. Accessed 10 Mar. 2026.
Previous versions and publish date:
0.1.0 (2025-09-04 16:10), 0.1.1 (2025-09-30 16:40), 0.1.2 (2026-02-19 16:10)
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Complete documentation for booami
Functions, R codes and Examples using the booami R package
Full booami package functions and examples
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