mboost v2.4-1
0
Monthly downloads
Model-Based Boosting
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.
Functions in mboost
| Name | Description | |
| FP | Fractional Polynomials | |
| mboost-package | mboost: Model-Based Boosting | |
| Family | Gradient Boosting Families | |
| boost_family-class | Class "boost_family": Gradient Boosting Family | |
| glmboost | Gradient Boosting with Component-wise Linear Models | |
| methods | Methods for Gradient Boosting Objects | |
| confint.mboost | Pointwise Bootstrap Confidence Intervals | |
| mboost | Model-based Gradient Boosting | |
| baselearners | Base-learners for Gradient Boosting | |
| IPCweights | Inverse Probability of Censoring Weights | |
| stabsel | Stability Selection | |
| cvrisk | Cross-Validation | |
| blackboost | Gradient Boosting with Regression Trees | |
| gamboost | Gradient Boosting with Smooth Components | |
| survFit | Survival Curves for a Cox Proportional Hazards Model | |
| boost_control | Control Hyper-parameters for Boosting Algorithms | |
| No Results! | ||
Last month downloads
Details
| Date | 2014-12-15 |
| LazyData | yes |
| License | GPL-2 |
| URL | http://mboost.r-forge.r-project.org/ |
| Packaged | 2014-12-15 13:57:42 UTC; bhofner |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2014-12-16 00:39:25 |
| depends | base (>= 2.14.0) , methods , parallel , R (>= 2.14.0) , stabs (>= 0.5-0) , stats |
| suggests | BayesX , fields , gbm , MASS , mlbench , nnet , party (>= 1.0-3) , randomForest , RColorBrewer , rpart (>= 4.0-3) , TH , TH.data |
| imports | lattice , Matrix , nnls , quadprog , splines , survival , utils |
| Contributors | Thomas Kneib, Torsten Hothorn, Peter Buehlmann, Fabian Scheipl, Benjamin Hofner, Matthias Schmid, Fabian Sobotka |
Include our badge in your README
[](http://www.rdocumentation.org/packages/mboost)