A comprehensive tutorial is given in: An overview of the implementation is given in: The theory and the package (until version 2.0) are described in: Details of stability selection in the context of boosting are described in:

Hofner B, Mayr A, Robinzonov N, Schmid M (2014). “Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost.”

Computational Statistics,29, 3–35.

Hothorn T, Buehlmann P, Kneib T, Schmid M, Hofner B (2010). “Model-based Boosting 2.0.”

Journal of Machine Learning Research,11, 2109–2113.

Buehlmann P, Hothorn T (2007). “Boosting Algorithms: Regularization, Prediction and Model Fitting (with Discussion).”

Statistical Science,22(4), 477–505.

Hofner B, Boccuto L, Goeker M (2015). “Controlling false discoveries in high-dimensional situations: Boosting with stability selection.”

BMC Bioinformatics,16(144).

Corresponding BibTeX entries:

@Article{, title = {Model-based Boosting in {R}: A Hands-on Tutorial Using the {R} Package mboost}, author = {Benjamin Hofner and Andreas Mayr and Nikolay Robinzonov and Matthias Schmid}, journal = {Computational Statistics}, year = {2014}, volume = {29}, pages = {3--35}, }

@Article{, title = {Model-based Boosting 2.0}, author = {Torsten Hothorn and Peter Buehlmann and Thomas Kneib and Matthias Schmid and Benjamin Hofner}, journal = {Journal of Machine Learning Research}, year = {2010}, volume = {11}, pages = {2109--2113}, }

@Article{, title = {Boosting Algorithms: Regularization, Prediction and Model Fitting (with Discussion)}, author = {Peter Buehlmann and Torsten Hothorn}, journal = {Statistical Science}, year = {2007}, volume = {22}, number = {4}, pages = {477--505}, }

@Article{, title = {Controlling false discoveries in high-dimensional situations: Boosting with stability selection}, author = {Benjamin Hofner and Luigi Boccuto and Markus Goeker}, journal = {{BMC} Bioinformatics}, year = {2015}, volume = {16}, number = {144}, }