Alphabetic package overview
- CoxFlexBoost: Boosting Flexible Cox Models (with Time-Varying Effects).
- Daim: Diagnostic accuracy of classification models.
- gamboostLSS: Boosting Methods for GAMLSS models.
- lethal: Compute lethal doses (LD) with confidence intervals.
- mboost: Model-Based Boosting.
- OpenML: Exploring Machine Learning Better, Together
- opm: Tools for analysing OmniLog(R) Phenotype Microarray data.
- papeR: A Toolbox for Writing Pretty Papers and Reports.
- stabs: Stability Selection with Error Control.
In the following paragraphs 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
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.
lethal: Compute lethal doses (LD) with confidence intervals.
Compute lethal doses for count data based on generalized additive
models (GAMs) together with parametric bootstrap confidence
intervals for the lethal dose. The package is designed for
experiments with counts as outcome, which need a separate
preparation for each measurment. Examples for such experiments are
survival experiments where the survival is measured as the number
of colony forming units (c.f.u.). In this case, one cannot measure
one prepartation multiple times with various doses but one needs
one experiment (with one or more biological replicates) for each
Author: Benjamin Hofner
Find out more about lethal
opm: Tools for analysing OmniLog(R) Phenotype Microarray data.
Tools for analysing OmniLog® and MicroStation™
phenotype microarray (PM) data as produced by the devices
distributed by BIOLOG Inc. as well as similar kinds of data such
as growth curves. Major facilities are plotting data, accurately
estimating curve parameters, comparing and discretising data,
creating phylogenetic formats and reports for taxonomic journals,
drawing the PM analysis results in biochemical pathway graphs
optionally including genome annotations, running multiple
comparisons of means, easy interaction with powerful
feature-selection approaches, integrating metadata, using the YAML
format for the storage of data and metadata, batch conversion of
large numbers of files, and database I/O.
R package version 1.1-0 available on
Current development version available on R-forge.
An install script for current versions can be found at http://www.goeker.org/opm.
Author: Markus Goeker with contributions by Benjamin Hofner, Lea A.I. Vaas,
Johannes Sikorski, Nora Buddruhs and Anne Fiebig
Find out more about opm
An implementation of various methods to evaluate the performance of
classification models is given in the package Daim.
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 &