Matthias Schmid

Matthias Schmid

7 packages on CRAN

betaboost

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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>).

discSurv

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Provides data transformations, estimation utilities, predictive evaluation measures and simulation functions for discrete time survival analysis.

DStree

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Building discrete-time survival trees and bagged trees based on the functionalities of the rpart package. Splitting criterion maximizes the likelihood of a covariate-free logistic discrete time hazard model.

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

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Contains functions for estimation and model selection of kernel deep stacking networks.

mboost

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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.

survAUC

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The package provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data.