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Component-wise boosting applies the boosting framework to statistical models, e.g., general additive models using component-wise smoothing splines. Boosting these kinds of models maintains interpretability and enables unbiased model selection in high dimensional feature spaces.

The R package compboost is an alternative implementation of component-wise boosting written in C++ to obtain high runtime performance and full memory control. The main idea is to provide a modular class system which can be extended without editing the source code. Therefore, it is possible to use R functions as well as C++ functions for custom base-learners, losses, logging mechanisms or stopping criteria.

For an introduction and overview about the functionality visit the project page.


CRAN version:


Developer version:



The examples are rendered using compboost 0.1.1.

The fastest way to train a Compboost model is to use the wrapper functions boostLinear() or boostSplines():

cboost = boostSplines(data = iris, target = "Sepal.Length", loss = LossQuadratic$new(),
  oob_fraction = 0.3, iterations = 500L, trace = 100L)

  plotPEUni(cboost, "Petal.Length"),
  plotIndividualContribution(cboost, iris[70, ], offset = FALSE),
  ncol = 3L

For more extensive examples and how to use the R6 interface visit the project page.


To get an idea of the performance of compboost, we have conduct a small benchmark in which compboost is compared with mboost. For this purpose, the runtime behavior and memory consumption of the two packages were compared. The results of the benchmark can be read here.


To cite compboost in publications, please use:

Schalk et al., (2018). compboost: Modular Framework for Component-Wise Boosting. Journal of Open Source Software, 3(30), 967,

  author = {Daniel Schalk, Janek Thomas, Bernd Bischl},
  title = {compboost: Modular Framework for Component-Wise Boosting},
  URL = {},
  year = {2018},
  publisher = {Journal of Open Source Software},
  volume = {3},
  number = {30},
  pages = {967},
  journal = {JOSS}


On your local machine

In order to test the pacakge functionality you can use devtools to test the package on your local machine: