mboost - 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. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

Last updated 1 months ago

boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selection

74 stars 5.83 score 12 dependencies 23 dependents

gamboostLSS - Boosting Methods for 'GAMLSS'

Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.

Last updated 11 months ago

boosting-algorithmsgamboostlssgamlssmachine-learningr-languagevariable-selection

26 stars 2.98 score 13 dependencies 1 dependents

FDboost - Boosting Functional Regression Models

Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.

Last updated 5 months ago

boostingboosting-algorithmsfunction-on-function-regressionfunction-on-scalar-regressionmachine-learningscalar-on-function-regressionvariable-selection

16 stars 2.46 score 18 dependencies