mboost v0.5-4

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

Date $Date: 2007/04/18 08:28:35 $
SaveImage yes
License GPL
Packaged Thu Apr 19 17:55:13 2007; hothorn

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