CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks. This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Jaeger, Byron C.; Long, D. Leann; Long, Dustin M.; Sims, Mario; Szychowski, Jeff M.; Min, Yuan-I; McClure, Leslie A.; Howard, George; Simon, Noah: Oblique random survival forests (2019)
- Seibold, Heidi; Bernau, Christoph; Boulesteix, Anne-Laure; De Bin, Riccardo: On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models (2018)
- Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf: An update on statistical boosting in biomedicine (2017)
- De Bin, Riccardo: Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \textitCoxBoostand \textitmboost (2016)
- Lang, M.; Kotthaus, H.; Marwedel, P.; Weihs, C.; Rahnenführer, J.; Bischl, B.: Automatic model selection for high-dimensional survival analysis (2015)
- Porzelius, Christine: Model complexity selection in high-dimensional time-to-event data analysis (2011)