Peter Bühlmann

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R-package pcalg: PC-algorithm for causal inference
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. (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software 47 (11), 1-26. PDF

R-package protiq: Protein (identification and) quantification based on peptide evidence
Gerster, S., Kwon, T., Ludwig, C., Matondo, M., Vogel, C., Marcotte, E., Aebersold, R. and Bühlmann, P. (2014). Statistical approach to protein quantification. Molecular and Cellular Proteomics 13, 666-677. Download
Gerster, S., Qeli, E., Ahrens, C.H. and Bühlmann, P. (2010). Protein and gene model inference based on statistical modeling in k-partite graphs. Proceedings of the National Academy of Sciences 107, 12101-12106.
PDF. Supporting Information

R-package mboost: Model-Based Boosting
Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: regularization, prediction and model fitting (with discussion). Statistical Science 22, 477-522. PDF
Hothorn, T., Bühlmann, P., Kneib, T., Schmid M. and Hofner, B. (2010). Model-based boosting 2.0. Journal of Machine Learning Research 11, 2109-2113. PDF

R-package glmmlasso: Generalized linear mixed-effects models with Lasso
Reference: Schelldorfer, J., Meier, L. and Bühlmann, P. (2014). GLMMLasso: An algorithm for high-dimensional generalized linear mixed models using L1-penalization. Journal of Computational and Graphical Statistics 23, 460-477.PDF

R-package lmmlasso: Linear mixed-effects models with Lasso
Reference: Schelldorfer, J., Bühlmann, P. and van de Geer, S. (2011). Estimation for high-dimensional linear mixed-effects models using L1-penalization. Scandinavian Journal of Statistics 38, 197-214. 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
Dettling, M. and Bühlmann, P. (2002). Supervised clustering of genes. Genome Biology, 3(12): research0069.1-0069.15. Click here.
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.