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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R, Revised and Expanded Edition by Thomas W. Miller

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D. Code and Utilities

“On second thought, let’s not go to Camelot. It’s a silly place.”

—GRAHAM CHAPMAN AS KING ARTHUR IN Monty Python and the Holy Grail (1975)

Doing data science with R means looking for task views posted with the Comprehensive R Archive Network (CRAN). We go to RForge and GitHub. We read package vignettes and papers in The R Journal and the Journal of Statistical Software. At the time of this writing, the R programming environment consists of more than 5,000 packages, many of them focused upon modeling methods. Useful general references for learning R include Matloff (2011) and Lander (2014). Venables and Ripley (2002), although written with S/SPlus in mind, remains a critical reference in the statistical programming community. ...

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