Model-Based Boosting
Base-learners for Gradient Boosting
Gradient Boosting with Regression Trees
Class "boost_family": Gradient Boosting Family
Pointwise Bootstrap Confidence Intervals
Control Hyper-parameters for Boosting Algorithms
Cross-Validation
Gradient Boosting Families
Fractional Polynomials
Gradient Boosting for Additive Models
Gradient Boosting with Component-wise Linear Models
Inverse Probability of Censoring Weights
Model-based Gradient Boosting
Call internal functions.
mboost: Model-Based Boosting
Methods for Gradient Boosting Objects
Plot effect estimates of boosting models
Stability Selection
Survival Curves for a Cox Proportional Hazards Model
Variable Importance
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. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.