5 packages on CRAN
Big data statistical analysis for high-dimensional models is made possible by modifying lasso.proj() in 'hdi' package by replacing its nodewise-regression with sparse precision matrix computation using 'BigQUIC'.
Methodology for testing nonlinearity in the conditional mean function in low- or high-dimensional generalized linear models, and the significance of (potentially large) groups of predictors. Details on the algorithms can be found in the paper by Jankova, Shah, Buehlmann and Samworth (2019) <arXiv:1908.03606>.
Implementation of multiple approaches to perform inference in high-dimensional models.
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 high-dimensional data.
Method for protein quantification based on identified and quantified peptides. protiq can be used for absolute and relative protein quantification. Input peptide abundance scores can come from various sources, including SRM transition areas and intensities or spectral counts derived from shotgun experiments. The package is still being extended to also include the model for protein identification, MIPGEM, presented in Gerster, S., Qeli, E., Ahrens, C.H. and Buehlmann, P. (2010). Protein and gene model inference based on statistical modeling in k-partite graphs. Proceedings of the National Academy of Sciences 107(27):12101-12106.