mboost

R package mboost: Model-Based Boosting. Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.


References in zbMATH (referenced in 36 articles , 2 standard articles )

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  1. Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang: SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting (2018) arXiv
  2. Thomas, Janek; Mayr, Andreas; Bischl, Bernd; Schmid, Matthias; Smith, Adam; Hofner, Benjamin: Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates (2018)
  3. Brockhaus, Sarah; Melcher, Michael; Leisch, Friedrich; Greven, Sonja: Boosting flexible functional regression models with a high number of functional historical effects (2017)
  4. Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin: Pathway-based kernel boosting for the analysis of genome-wide association studies (2017)
  5. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  6. Pierre-André Cornillon and Nicolas Hengartner and Eric Matzner-Løber: Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package (2017)
  7. Ternès, Nils; Rotolo, Federico; Heinze, Georg; Michiels, Stefan: Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces (2017)
  8. De Bin, Riccardo: Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \itCoxBoost and \itmboost (2016)
  9. Dubossarsky, E.; Friedman, J. H.; Ormerod, J. T.; Wand, M. P.: Wavelet-based gradient boosting (2016)
  10. Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)
  11. Reulen, Holger; Kneib, Thomas: Boosting multi-state models (2016)
  12. Sweeney, Elizabeth; Crainiceanu, Ciprian; Gertheiss, Jan: Testing differentially expressed genes in dose-response studies and with ordinal phenotypes (2016)
  13. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  14. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  15. Barber, Rina Foygel; Drton, Mathias: High-dimensional Ising model selection with Bayesian information criteria (2015)
  16. Leha, Andreas: Statistical methods to enhance clinical prediction with high-dimensional data and ordinal response (2015)
  17. Benjamin Hofner, Andreas Mayr, Matthias Schmid: gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework (2014) arXiv
  18. Binder, Harald (ed.); Kestler, Hans A. (ed.); Schmid, Matthias (ed.): Proceedings of Reisensburg 2011 (2014)
  19. Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias: Model-based boosting in R: a hands-on tutorial using the R package mboost (2014)
  20. Esteban Alfaro; Matias Gamez; Noelia García: adabag: An R Package for Classification with Boosting and Bagging (2013)

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