An Introduction to R. Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics. This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1990 W. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Preface This introduction to R is derived from an original set of notes describing the S and S-PLUS environments written in 1990–2 by Bill Venables and David M.
Comments and corrections are always welcome. Suggestions to the reader 1.1 The R environment Try ? Stat579. intro statistical computing. iastate. 40 Fascinating Blogs for the Ultimate Statistics Geek! | (R news ... | R package. R-Statistics. Statistics Mattters - Jan 9 2011 - My first R package: zipcode. Decisions, decisions Newcomb’s paradox is the name usually given to the following problem. You are playing a game against another player, often called Omega, who claims to be omniscient; in particular, Omega claims to be able to predict how you will pl... Newcomb’s paradox is the name usually given to the following problem. You are playing a game against another player, often called Omega, who claims to be omniscient; in particular, Omega claims to be able to predict how you will play in the game.
Assume that Omega has convinced you in some way that it is, if not omniscient, at least remarkably accurate: for example, perhaps it has accurately predicted your behavior many times in the past. Omega places before you two opaque boxes. Getting Started with Sweave: R, LaTeX, Eclipse, StatET, & TeXlipse. Being able to press a single button that runs all your statistical analyses and integrates the output into your final report is a beautiful thing. If you have not already heard, this is what Sweave can do for you. However, getting your computer to run Sweave can be a little bit fiddly. Thus, this post: (1) sets out the benefits of Sweave; (2) sets out how to install and configure R, Sweave, and Eclipse on Windows; (3) lists resources for people wanting to learn more about how to use LaTeX and Sweave; and (4) lists some specific resources relevant to researchers in psychology wanting to use these tools.
What is Sweave? To Sweave is to weave in S. Why Sweave? Reproducibility: The most important reason to adopt a tool like Sweave is to make your research more reproducible. Common Use Cases Statistics Instructional MaterialsEmpirical reports, journal articles, book chapters, theses, etc.Data sharing, literate programming, reproducible research, weaving: This is future of data analysis. 1. 2.
R-help. R-help. R-help.