Modern Applied Statistics with S Fourth edition

By William N. Venables, B. D. Ripley. 2002

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Citations in the corpus

Visualizing the effects of predictor variables in black box supervised learning models (2016, methodology): “nerated as noisy observations according to the model 𝑌𝑌= 𝑋𝑋1+ 𝑋𝑋22+ 𝜀𝜀 with 𝜀𝜀~𝑁𝑁(0, 0.12), and we fit a neural network model instead of a tree. For the neural network, we used the package of nnet R (Venables and Ripley, 2002) with ten nodes in the single hidden layer, a linear output ac”
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).”
Warming advances top-down control and reduces producer biomass in a freshwater plankton community (2017, methodology): “Variable selection per limnotron was based on Akaike information criterion (function stepAIC from MASS package; Venables and Ripley 2002), and the variables that were selected in at least one of the limnotrons were used in further analysis.”
Trait interactions help explain plant invasion success in the German flora (2008, methodology): “All statistical analyses were performed within R (R Development Core Team 2005, Version 2.2.1) using the packages MASS (Venables & Ripley 2002), multcomp (Bretz et al .”
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images (2017, methodology): “ANN classification was done with the help of R statistical software (R Core Team, 2015) and the “ nnet ” package (Venables & Ripley, 2002).”
A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017, methodology): “…the image model (with six levels, entered into the model design matrix using successive di ﬀ erence coding using contr.sdif from the MASS package for R; Venables and Ripley (2002)) and the presentation condition (with two levels, parafoveal and inspection, coded with sum contrasts [1, -1]).”
Bamboo overabundance alters forest structure and dynamics in the Atlantic Forest hotspot (2012, methodology): “All analyses were performed in R (www.R-project.org), using MASS (Venables and Ripley, 2002), nlme (Pinheiro et al., 2008), pscl (Zeileis et al., 2008; Jackman, 2010), and bbmle (Bolker, 2009).”