Package: fastgbm 0.7.0

fastgbm: Fast Histogram Gradient Boosting for Regression, Classification, and Survival Analysis

A fast gradient boosting machine covering four task types with one interface: regression (squared error), binary and multiclass classification (logistic and one-vs-rest), and right-censored survival analysis via Cox (Breslow ties), accelerated failure time (normal location-scale), or piecewise-exponential objectives, and competing risks via Fine-Gray subdistribution hazards, cause-specific hazards, or coupled cause-specific hazards, following 'CoxBoost' (Binder et al. 2009) <doi:10.1093/bioinformatics/btp088>. Provides native missing-value routing, baseline-hazard estimation and survival-probability prediction for the survival objectives, and deterministic multi-threaded training via 'RcppParallel'. Methods are described in Friedman (2001) <doi:10.1214/aos/1013203451>.

Authors:Imad El Badisy [aut, cre]