Benjamin Hofner

Benjamin Hofner

8 packages on CRAN

stabs

cran
96th

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Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are implemented. The package can be combined with arbitrary user specified variable selection approaches.

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Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.

mboost

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94th

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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.

papeR

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A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models.

kangar00

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94th

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Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel (Friedrichs et al., 2017, <doi:10.1155/2017/6742763>).

OpenML

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We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.

betaboost

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88th

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Implements boosting beta regression for potentially high-dimensional data (Mayr et al., 2018 <doi:10.1093/ije/dyy093>). The 'betaboost' package uses the same parametrization as 'betareg' (Cribari-Neto and Zeileis, 2010 <doi:10.18637/jss.v034.i02>) to make results directly comparable. The underlying algorithms are implemented via the R add-on packages 'mboost' (Hofner et al., 2014 <doi:10.1007/s00180-012-0382-5>) and 'gamboostLSS' (Mayr et al., 2012 <doi:10.1111/j.1467-9876.2011.01033.x>).

Daim

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Several functions for evaluating the accuracy of classification models. The package provides the following performance measures: repeated k-fold cross-validation, 0.632 and 0.632+ bootstrap estimation of the misclassification rate, sensitivity, specificity and AUC. If an application is computationally intensive, parallel execution can be used to reduce the computational effort.