mboost v0.4-11
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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 | |
| IPCweights | Inverse Probability of Censoring Weights | |
| cvrisk | Cross-Validation | |
| blackboost | Gradient Boosting with Regression Trees | |
| bodyfat | Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths | |
| Family | Gradient Boosting Families | |
| boost_family-class | Class "boost_family": Gradient Boosting Family | |
| methods | Methods for Gradient Boosting Objects | |
| gamboost | Gradient Boosting with Componentwise Smoothing Splines | |
| FP | Fractional Polynomials | |
| boost_dpp | Data Preprocessing for Gradient Boosting | |
| boost_control | Control Hyper Parameters for Boosting Algorithms | |
| glmboost | Gradient Boosting with Componentwise Linear Models | |
| wpbc | Wisconsin Prognostic Breast Cancer Data | |
| westbc | Breast Cancer Gene Expression | |
| No Results! | ||
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Details
| Date | $Date: 2006/08/31 15:06:54 $ |
| SaveImage | yes |
| License | GPL |
| Packaged | Thu Sep 7 13:30:35 2006; hothorn |
| depends | base (>= 2.3.0) , methods , modeltools , party (>= 0.8-6) , R (>= 2.3.0) |
| suggests | ipred , mlbench |
| Contributors | Peter Buhlmann, Torsten Hothorn |
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