Personality Project: An introduction to psychometric theory This page is devoted to teaching others about psychometric theory as well as R. It consists of chapters of an in progress text as well as various short courses on R. The e-book is a work in progress. Chapters will appear sporadically. Parts of it are from the draft of a book being prepared for the Springer series on using R, other parts are just interesting tid-bits that would not be appropriate as chapters. It is written in the hope that I can instill in a new generation of psychologists the love for quantitative methodology imparted to me by reading the popular and then later the scientific texts of Ray Cattell [Cattell, 1966b] and Hans Eysenck [Eysenck, 1964, Eysenck, 1953, Eysenck, 1965]. My course in psychometric theory, on which much of this book is based, was inspired by a course of the same name by Warren Norman. This book would not be possible without the amazing contributions of the R-Core Team and the many contributers to R and the R-Help listserve.
Google's R Style Guide R is a high-level programming language used primarily for statistical computing and graphics. The goal of the R Programming Style Guide is to make our R code easier to read, share, and verify. The rules below were designed in collaboration with the entire R user community at Google. Summary: R Style Rules File Names: end in .R Identifiers: variable.name (or variableName), FunctionName, kConstantName Line Length: maximum 80 characters Indentation: two spaces, no tabs Spacing Curly Braces: first on same line, last on own line else: Surround else with braces Assignment: use <-, not = Semicolons: don't use them General Layout and Ordering Commenting Guidelines: all comments begin with # followed by a space; inline comments need two spaces before the # Function Definitions and Calls Function Documentation Example Function TODO Style: TODO(username) Summary: R Language Rules Notation and Naming File Names File names should end in .R and, of course, be meaningful. Identifiers Syntax Spacing
Code School - Try R Workload Characterization and Modeling Book by Dror G. Feitelson This is the final version of a book I am working on, entitled Workload Modeling for Computer Systems Performance Evaluation. It will be published by Cambridge University Press, hopefully towards the end of 2014. It will continue to be available here in pdf format also after it is published. Your are welcome to make one copy for your personal use, but further unauthorized distribution is not allowed. ©2014 All rights reserved. Download current version (pdf format, x+591 pages, 235 figures, 18 tables, 51 boxes, 756 references, 15.3 MB) Table of Contents Introduction Workload Data Statistical Distributions Fitting Distributions to Data Heavy Tails Correlations in Workloads Self Similarity and Long-Range Dependence Hierarchical Generative Models Case Studies Summary and Outlook The bibliography is available as a BibTeX file. Links to data sources. Please let me know if you find any typos or other problems (email firstname.lastname@example.org).
Short-refcard Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg In recent years there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. The book is based on an inter-disciplinary course that we teach at Cornell. You can download a complete pre-publication draft of Networks, Crowds, and Markets here.
Stack Overflow Natural Language Processing with Python Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper This version of the NLTK book is updated for Python 3 and NLTK 3. The first edition of the book, published by O'Reilly, is available at (There are currently no plans for a second edition of the book.) 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Bibliography Term Index This book is made available under the terms of the Creative Commons Attribution Noncommercial No-Derivative-Works 3.0 US License. ProjectTemplate