mboost v1.0-1


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by Torsten Hothorn

Model-Based Boosting

Functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, or regression trees as base learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

Functions in mboost

Name Description
FP Fractional Polynomials
glmboost Gradient Boosting with Component-wise Linear Models
boost_dpp Data Preprocessing for Gradient Boosting
wpbc Wisconsin Prognostic Breast Cancer Data
methods Methods for Gradient Boosting Objects
boost_family-class Class "boost_family": Gradient Boosting Family
boost_control Control Hyper-parameters for Boosting Algorithms
Family Gradient Boosting Families
bodyfat Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths
baselearners Base learners for Gradient Boosting with Smooth Components
IPCweights Inverse Probability of Censoring Weights
westbc Breast Cancer Gene Expression
cvrisk Cross-Validation
gamboost Gradient Boosting with Smooth Components
blackboost Gradient Boosting with Regression Trees
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Last month downloads


Date $Date: 2007/07/08 15:52:23 $
LazyLoad yes
License GPL-2
Packaged Sun Dec 9 15:15:45 2007; hothorn

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