**http://cran.r-project.org/doc/contrib/Short-refcard.pdf**

Forecasting: principles and practice Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details.

Useful new R packages for data visualization and analysis The following is from a hands-on session I led at the recent Computer Assisted Reporting conference. There's a lot of activity going on with R packages now because of a new R development package called htmlwidgets, making it easy for people to write R wrappers for existing JavaScript libraries. The first html-widget-inspired package I want us to demo is Getting started with the `boot' package in R for bootstrap inference The package boot has elegant and powerful support for bootstrapping. In order to use it, you have to repackage your estimation function as follows. R has very elegant and abstract notation in array indexes. Suppose there is an integer vector OBS containing the elements 2, 3, 7, i.e. that OBS <- c(2,3,7);. Suppose x is a vector.

R Programming Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline[5].

knitr: Elegant, flexible and fast dynamic report generation with R Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ’s, see README. This website serves as the full documentation of knitr, and you can find the main manual, the graphics manual and other demos / examples here. For a more organized reference, see the knitr book. Motivation

Mann-Whitney-Wilcoxon Test Two data samples are independent if they come from distinct populations and the samples do not affect each other. Using the Mann-Whitney-Wilcoxon Test, we can decide whether the population distributions are identical without assuming them to follow the normal distribution. Example Learn R for beginners with our PDF With so much emphasis on getting insight from data these days, it's no wonder that R is rapidly rising in popularity. R was designed from day one to handle statistics and data visualization, it's highly extensible with many new packages aimed at solving real-world problems and it's open source (read "free"). If you're ready to learn, we have just the ticket: A free PDF of Computerworld's "Beginner's guide to R." Included in this 45-page guide: Introduction: First steps, including downloading R and RStudio, setting your working directory and installing and using packages. Get your data into R: Importing local and remote files, copying data from your clipboard, saving after import.

Bootstrapping Nonparametric Bootstrapping The boot package provides extensive facilities for bootstrapping and related resampling methods. You can bootstrap a single statistic (e.g. a median), or a vector (e.g., regression weights). This section will get you started with basic nonparametric bootstrapping. The main bootstrapping function is boot( ) and has the following format: bootobject <- boot(data= , statistic= , R=, ...) where R Programming - Manuals R Basics The R & BioConductor manual provides a general introduction to the usage of the R environment and its basic command syntax. Code Editors for R Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console. Programming in R using Vim or Emacs Programming in R using RStudio Integrating R with Vim and Tmux

Related: R and Matlab programming
- Data Scientist
- Data analysis
- Matlab
- Matlab
- Matlab filter
- Matlab
- MATLAB
- MATLAB 類神經 [調整train、test、val比例]
- Scientist talking about what they do.
- R-Programming
- DATA SCIENTIST
- R programming
- Reading, Writing, and Thinking Like a Scientist
- The Comprehensive R Archive Network
- R-bloggers | R news and tutorials contributed by (750) R bloggers
- Regulations.gov - Home
- Producing Simple Graphs with R
- YouTube - Broadcast Yourself
- The Data Visualization Beginner’s Toolkit #2: Visualization Tools — Fell in Love with Data
- Understanding Formal Analysis
- Bunker Roy: Learning from a barefoot movement | Video on TED.com
- Bing - Webmaster Tools
- Gestures and Tools for Kinect - Eternal Coding - HTML5 / Windows / Kinect / 3D development - Site Home - MSDN Blogs
- software for social network analysis
- Visual Representation of SQL Joins
- twotorials
- Personal SWOT Analysis - Career Planning from MindTools.com
- Coursera
- IBM Analytics - Home
- EngLangBlog
- R Graph Gallery :: Home
- Packet Storm
- Stanford Large Network Dataset Collection
- OpenIntro
- OpenIntro
- Code School - Try R
- Free Statistical Software - FreeStatistics.Info
- Argument map
- Impatient R
- Learn Git Branching