mboost v0.5-7

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

Date $Date: 2007/05/29 20:32:51 $
LazyLoad yes
License GPL
Packaged Wed May 30 12:13:51 2007; hothorn

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