Showing 5 of total 5 results (show query)
boost-r
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.
Maintained by Torsten Hothorn. Last updated 4 months ago.
boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selectionopenblas
106.2 match 72 stars 12.70 score 540 scripts 27 dependents
fabianobster
sgboost:Sparse-Group Boosting
Sparse-group boosting to be used in conjunction with the 'mboost' for modeling grouped data. Applicable to all sparse-group lasso type problems where within-group and between-group sparsity is desired. Interprets and visualizes individual variables and groups.
Maintained by Fabian Obster. Last updated 29 days ago.
0.5 match 4.74 score
xmengju
RRBoost:A Robust Boosting Algorithm
An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <10.1016/j.csda.2007.11.006>).
Maintained by Xiaomeng Ju. Last updated 4 months ago.
0.5 match 2.70 score 3 scripts
cran
mermboost:Gradient Boosting for Generalized Additive Mixed Models
Provides a novel framework to estimate mixed models via gradient boosting. The implemented functions are based on 'mboost' and 'lme4'. Hence, the family range is predetermined by 'lme4'. A correction mechanism for cluster-constant covariates is implemented as well as an estimation of random effects' covariance.
Maintained by Lars Knieper. Last updated 21 days ago.
0.5 match 1.70 score
hreulen
gamboostMSM:Boosting Multistate Models
Contains infrastructure for using mboost::gamboost() in order to estimate multistate models.
Maintained by Holger Reulen. Last updated 3 years ago.
0.6 match 1.00 score 4 scripts