Modern applied statistics with S, 4th Edition

By William N. Venables, B. D. Ripley. Statistics and computing, 2002

3,018 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cdd982d212ae4bd294cd5d83e13543ea89d7103d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Modeling wine preferences by data mining from physicochemical properties (2009, methodology): “We adopted the default suggestions of the R tool [29], except for the hyperparameters (which were set using a grid search).”
Dimensions and their poles: A metric and topological approach to opposites (2011, methodology): “To reveal the structure of these correlations, we used a principal component analysis (PCA, see Venables & Ripley, 2002, pp. 302 305).”
Low‐Rank Scale‐Invariant Tensor Product Smooths for Generalized Additive Mixed Models (2006, methodology): “R is the spawning rate at the location (loi, lai) and time ti of the ith sample and δ is the per capita mortality rate, which is assumed to depend only on time. Δij is the duration of the jth age class at the ith sampling station, while aij is the mid-point age of the class. The log(bi) are i.i.d. N”
Detecting Common Method Bias: Performance of the Harman's Single-Factor Test (2019, methodology): “The mvrnorm function of the MASS package (Venables & Ripley, 2002, v. 7.3-45) was used for data generation.”
Regression Models for Count Data in R (2008, methodology): “In R ( R Development Core Team 2008), GLMs are provided by the model ﬁtting functions glm() (Chambers and Hastie 1992) in the stats package and glm.nb() in the MASS package (Venables and Ripley 2002) along with associated methods for diagnostics and inference.”
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013, methodology): “But the Naı̈ve Bayes’s classifier is just one example of a very large and diverse literature, including Random Forests (Breiman 2001), Support Vector Machines (Venables and Ripley 2002), and neural networks (Bishop 1995).”
An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. (2009, methodology): “We implemented these methods using the nnet library (23) in R.”
A guide to the Dagum distributions (2008, methodology): “The computations for Figure 3 were also carried out in R, Version 2.5.1, but along different lines, namely via modifying the fitdistr() function from the MASS package, the package accompanying Venables and Ripley (2002).”
Sex differences in indirect aggression : Psychological evidence from young adults (2006, methodology): “We therefore used iterated reweighted least squares (IWLS) to fit a robust regression model with Tukey's bisquare M-estimator; this procedure is less sensitive to outliers than ordinary least-squares regression (Venables & Ripley, 2002).”
The VGAM Package for Categorical Data Analysis (2010, methodology): “From the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/ ) there is polr() (in MASS ; Venables and Ripley 2002) for a proportional odds model and multinom() (in nnet ; Venables and Ripley 2002) for the multinomial logit model.”
Handbook of Big Data (2016, background): “Each endogenous variable is associated with a set of background factors U = ( U W , U A , U Y ) with some joint distribution P U .”
Fair Is Not Fair Everywhere (2015, methodology): “All analyses were conducted in R (R Development Core Team, 2013) using the packages MASS and lme4 (Bates, Maechler, Bolker, & Walker, 2013; Venables & Ripley, 2002).”
A general class of zero-or-one inflated beta regression models (2011, methodology): “The iterative process (B.1) may be performed, for instance, in the R package by taking advantage of the library MASS (Venables & Ripley, 2002).”
Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020, methodology): “For balanced data these can be arranged in a T × G matrix, and the meat of the panel-corrected covariance matrix can be computed using the Kronecker product as The details for the unbalanced case are omitted here for brevity but are discussed in detail in Bailey and Katz (2011).”
Comparison of Methods of Handling Missing Time-Invariant Covariates in Latent Growth Models Under the Assumption of Missing Completely at Random (2007, methodology): “R syntax to calculate mean substitution, EM, and multiple imputation procedures require(norm) # “norm” (Schafer, 2002) is the library required for # the EM algorithm and the MI analysis require(MASS) # “MASS” (Venables & Ripley, 2002) is the library required # for writing external files setwd(“C:/mi”