Peter Bühlmann

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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. (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
References:
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
References:
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
References:
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.