R Packages and Utilities
- combines typesetting with LaTeX
and data anlysis with S into integrated statistical documents. When
run through R, all data analysis output (tables, graphs, ...)
is created on the fly and inserted into a final LaTeX document. The
report can be automatically updated if data or analysis change, which
allows for truly reproducible research.
implements a general framework for k-centroid clustering
algorithms. The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary
distance/similarity measures and centroid computation. Further
cluster methods include hard competitive learning, neural gas and QT
implements a general framework for finite mixtures of regression
models using the EM algorithm. FlexMix provides the E-step and all
data handling, while the M-step can be supplied by the user to
easily define new models. Existing drivers implement mixtures of
standard linear models, generalized linear models and model-based
The main function archetypes implements a framework for archetypal
analysis supporting arbitary problem solving mechanisms for the
different conceputal parts of the algorithm.
- The main function biclust provides several algorithms to find
biclusters in two-dimensional data: Cheng and Church, Spectral,
Plaid Model, Xmotifs and Bimax. In addition, the package provides
methods for data preprocessing (normalization and discretisation),
visualisation, and validation of bicluster solutions.
- Visualize cluster results and investigate additional properties
of clusters using interactive neighbourhood graphs. By clicking on
the node representing the cluster, information about the cluster is
provided using additional graphics or summary statistics. For
microarray data, tables with links to genetic databases like gene
ontolgy can be created for each cluster.
Testing, dating and monitoring of structural change in
linear regression relationships.
strucchange features tests/methods from the generalized
fluctuation test framework as well as from the F test (Chow
test) framework. This includes methods to fit, plot and test
fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving
estimates) and F statistics, respectively.
It is possible to monitor incoming data online using
Finally, the breakpoints in regression models with structural
changes can be estimated together with confidence intervals.
Emphasis is always given to methods for visualizing the data.
- A collection of functions written by members of my former working
group at the department of statistics, TU Wien. It includes
functions for latent class analysis, short time Fourier
transform, fuzzy clustering, support vector machines,
shortest path computation, bagged clustering, naive Bayes
- A collection of artificial and real-world machine learning
benchmark problems, including, e.g., several
data sets from the UCI repository.
- A collection of tools to deal with statistical models.
- Functions for import, export, plotting and other
manipulations of bitmapped images.
All R packages described above are availabe from CRAN
, most of them are joint
work with various other researchers. Sweave is part of the R base