Software

R-package pcalg: PC-algorithm for causal inference
References:
Maathuis, M.H., Kalisch, M. and Bühlmann, P. (2009). Estimating high-dimensional intervention effects from observational data. Annals of Statistics 37, 3133-3164.PDF
Kalisch, M., Mächler, M., Colombo, D., Maathuis, M.H. and Bühlmann, P. (2010). Causal inferemce using graphical models with the R package pcalg. Preprint. PDF

R-package mboost: Model-Based Boosting
Reference: Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: regularization, prediction and model fitting (with discussion). Statistical Science 22, 477-522. PDF

R-package howmany: Lower bounds for total number of non-null hypotheses in multiple testing
Reference: Meinshausen, N. and Bühlmann, P. (2005). Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures. Biometrika 92, 893-907. PDF

R-package VLMC: Variable Length Markov Chains
Reference: Mächler, M. and Bühlmann, P. (2004). Variable length Markov chains: methodology, computing and software. Journal of Computational and Graphical Statistics 13, 435-455.

R-package supclust: Supervised Clustering of Genes
Reference: Genome Biology (2002), 3(12): research0069.1-0069.15. Click here.

R-package supclust: Finding Predictive Gene Groups from Microarray Data
Reference: Dettling, M. and Bühlmann, P. (2003). Finding predictive gene groups from microarray data. Journal of Multivariate Analysis 90, 106-131. PDF

Boosting for Tumor Classification with Gene Expression Data
Reference: Dettling, M. and Bühlmann, P. (2003). Boosting for tumor classification with gene expression data. Bioinformatics 19, No. 9, 1061-1069. Compressed postscript. PDF.