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Papers: 10.1186/s12859-016-1149-8

https://doi.org/10.1186/s12859-016-1149-8

Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection

Cited by: 27
Author(s): Andreas Mayr, Benjamin Hofner, Matthias Schmid
Published: about 9 years ago

Software Mentions 9

cran: gbm
Generalized Boosted Regression Models
Papers that mentioned: 250
Very Likely Science (100)
cran: glmnet
Lasso and Elastic-Net Regularized Generalized Linear Models
Papers that mentioned: 1,607
Very Likely Science (100)
cran: mboost
Model-Based Boosting
Papers that mentioned: 32
Very Likely Science (77)
cran: pec
Prediction Error Curves for Risk Prediction Models in Survival Analysis
Papers that mentioned: 56
Very Likely Science (85)
cran: peperr
Parallelised Estimation of Prediction Error
Papers that mentioned: 4
Very Likely Science (100)
cran: stabs
Stability Selection with Error Control
Papers that mentioned: 7
Very Likely Science (100)
cran: survAUC
Estimators of Prediction Accuracy for Time-to-Event Data
Papers that mentioned: 22
Very Likely Science (75)
pypi: gbm
Read and analyze GBM data
Papers that mentioned: 250
Very Likely Science (100)
pypi: glmnet
Python wrapper for glmnet
Papers that mentioned: 1,607
Very Likely Science (100)