The packages above are ordered alphabetically.
Badges show specific package information and test results from github if available.
Click on the badges to go to the CRAN package, package source or test results.
For more information on a package's purpose click on the package name (or scroll down).
Below the packages are grouped by topics. Topics are in alphabetical order.
The following packages are devoted to boosting methods. The
package mboost is a very generic implementation of boosting methods
for a wide range of models. It is further enhanced to fit GAMLSS models,
i.e., models that regress multiple parameters on covariates
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
Authors: Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner
Find out more about mboost
A tutorial showing the usage of mboost (including some of the latest features) is available as
R> vignette("mboost_tutorial", package = "mboost")
and appeared in Computational Statistics.
Short paper showing new features of mboost 2.0 series available at JMLR MLOSS:
Model-based Boosting 2.0.
gamboostLSS: Boosting Methods for GAMLSS models.
Boosting models for fitting generalized additive models for
location, shape and scale (GAMLSS) to potentially high dimensional
Authors: Benjamin Hofner, Andreas Mayr, Nora Fenske,
Find out more about gamboostLSS
CoxFlexBoost: Boosting Flexible Cox Models (with Time-Varying Effects).
Likelihood-based boosting approach to fit flexible, structured
survival models with component-wise linear or P-spline
base-learners. Variable selection and model choice are built in
R package version 0.7-0 (beta) now available on
Author: Benjamin Hofner
betaboost: Boosting Beta Regression
Implements boosting beta regression for potentially high-dimensional data.
The betaboost packages uses the same parametrization as betareg to make results directly comparable.
The underlying boosting algorithms are implemented via the R add-on packages mboost and gamboostLSS.
Authors: Andreas Mayr, Benjamin Hofner, Leonie Weinhold, Matthias Schmid
The package lethal can be used to compute lethal doses for count
data outcomes (e.g., number of cells). It uses flexible smooth effects
models and provides various statistical inference procedures. The
package opm can be used to store, manipulate and analysze phenotype
microarray data. Splines can be used to fit the growth curves.
An implementation of various methods to evaluate the performance of
classification models is given in the package Daim.
Methods to analyze genome-wide assissition studies (GWAS) are implemented
in the package kangar00. The package also allows, in conjunction with
mboost (see above), to fit kernel boosting models for GWAS.
To allow better collaboration the web platform OpenML
provides a rich infrastructure. The following package provides direct access from within
To achieve reproducible results, it is important to be able to easily
generate and modify standard output. The package papeR aims at
providing tools to prettify output for reports and to generate tables for
easy usage in reports.
Stability selection is implemented in a versatile and gerneric
package stabs. It implements both, the standard error bound derived
by Meinshausen & Bühlmann (2010) and the improved error bounds of Shah &