Misc Functions of the Department of Statistics (e1071), TU Wien

By David Meyer, Evgenia Dimitriadou, Kurt Hornik, A. Weingessel, Friedrich Leisch. 2014

917 citations recorded by the source corpus.

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Citations in the corpus

An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, methodology): “We use the implementation of na€ıve bayes that is provided by the naiveBayes R function [71].”
Universal Approach for Structural Interpretation of QSAR/QSPR Models (2013, methodology): “[44] No variables were pre-selected prior to modeling.”
Multi-Objective Parameter Selection for Classifiers (2012, methodology): “The e1071 package (Dimitriadou et al. 2010) provides a generic tuning function that optimizes the classiﬁcation error for some classiﬁers, such as k -nearest neighbour, SVMs (Vapnik 1998), decision trees (Breiman et al. 1984), and random forests (Breiman 2001).”
Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types (2013, methodology): “We used the svm function from the e1071 package for R [24].”
Genomic Selection in Plant Breeding: A Comparison of Models (2012, methodology): “The model was implemented using the R libraries “nnet” (Venables and Ripley, 2002) and “e1071” (Dimitriadou et al., 2011).”
Variation of xylem vessel diameters across a climate gradient: insight from a reciprocal transplant experiment with a widespread boreal tree (2015, background): “…the additional R packages plyr (Wickham 2011), ggplot2 (Wickham 2009), reshape2 (Wickham 2007), lmerTest (Kuznetsova, Brunn Brockhoff & Haubo Bojesen Christensen 2014), multcomp (Hothorn, Bretz & Westfall 2008), lsmeans (Lenth & Herv e 2015), e1071 (Meyer et al. 2014) and diptest (Maechler 2013).”