Nadja Klein

Nadja Klein

5 packages on CRAN

bivquant

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Computation of bivariate quantiles via linear programming based on a novel direction-based approach using the cumulative distribution function.

sdPrior

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Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.

bamlss

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Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2019) <arXiv:1909.11784>.

BayesX

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Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<http://www.BayesX.org>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.

tram

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Formula-based user-interfaces to specific transformation models implemented in package 'mlt'. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Hothorn, 2019, <arxiv:1910.09219>). Multivariate conditional transformation models (<arxiv:1906.03151>) can be fitted as well.