comparing R, Matlab, SAS, STATA, SPSS

The following table compares the standard procedures of the five packages in detail. By "standard" I mean built-in or readily available from the official or widely known and reliable public web-sites.

TYPE OF STATISTICAL ANALYSIS	R	MATLAB	SAS	STATA	SPSS
Nonparametric Tests	Yes	Yes	Yes	Yes	Yes
T-test	Yes	Yes	Yes	Yes	Yes
ANOVA & MANOVA	Yes	Yes	Yes	Yes	Yes
ANCOVA & MANCOVA	Yes	Yes	Yes	Yes	Yes
Linear Regression	Yes	Yes	Yes	Yes	Yes
Generalized Least Squares	Yes	Yes	Yes	Yes	Yes
Ridge Regression	Yes	Yes	Yes
Lasso	Yes	Yes	Experimental
Generalized Linear Models	Yes	Yes	Yes	Yes	Yes
Mixed Effects Models	Yes	Yes	Yes	Yes	Yes
Logistic Regression	Yes	Yes	Yes	Yes	Yes
Nonlinear Regression	Yes	Yes	Yes
Discriminant Analysis	Yes	Yes	Yes	Yes	Yes
Nearest Neighbor	Yes	Yes	Yes		Yes
Factor & Principal Components Analysis	Yes	Yes	Yes	Yes	Yes
Copula Models	Yes	Yes	Experimental
Cross-Validation	Yes	Yes	Yes
Bayesian Statistics	Yes	Yes	Limited
Monte Carlo, Classic Methods	Yes	Yes	Yes	Yes	Limited
Markov Chain Monte Carlo	Yes	Yes	Yes
Bootstrap & Jackknife	Yes	Yes	Yes	Yes
EM Algorithm	Yes	Yes	Yes
Missing Data Imputation	Yes	Yes	Yes	Yes	Yes
Outlier Diagnostics	Yes	Yes	Yes	Yes	Yes
Robust Estimation	Yes	Yes	Yes	Yes
Longitudinal (Panel) Data	Yes	Yes	Yes	Yes
Survival Analysis	Yes	Yes	Yes	Yes	Yes
Path Analysis	Yes	Yes	Yes
Propensity Score Matching	Yes	Yes	Limited
Stratified Samples (Survey Data)	Yes	Yes	Yes	Yes	Yes
Experimental Design	Yes	Yes
Quality Control	Yes	Yes		Yes	Yes
Reliability Theory	Yes	Yes	Yes	Yes	Yes
Univariate Time Series	Yes	Yes	Yes	Yes	Limited
Multivariate Time Series	Yes	Yes	Yes	Yes
Markov Chains	Yes	Yes
Hidden Markov Models	Yes	Yes
Stochastic Volatility Models	Yes	Yes	Limited	Limited	Limited
Diffusions	Yes	Yes
Counting Processes	Yes	Yes	Yes
Filtering	Yes	Yes	Limited	Limited
Instrumental Variables	Yes	Yes	Yes	Yes
Simultaneous Equations	Yes	Yes	Yes	Yes
Splines	Yes	Yes	Yes
Nonparametric Smoothing Methods	Yes	Yes	Yes	Yes
Extreme Value Theory	Yes	Yes
Variance Stabilization	Yes	Yes
Cluster Analysis	Yes	Yes	Yes	Yes	Yes
Neural Networks	Yes	Yes	Yes		Limited
Classification & Regression Trees	Yes	Yes	Yes		Limited
Boosting Classification & Regression Trees	Yes	Yes
Random Forests	Yes	Yes
Support Vector Machines	Yes	Yes
Signal Processing	Yes	Yes
Wavelet Analysis	Yes	Yes	Yes
ROC Curves	Yes	Yes	Yes	Yes	Yes
Optimization	Yes	Yes	Yes

Please read the detailed description of the services offered in the areas of statistical consulting and financial consulting: home page, types of service, experience, case studies and payment options. You may also find the following pages useful: statistics resources and finance resources.

Source: http://stanfordphd.com/Statistical_Software.html

Rlinx

Some useful R links (State of the R)

http://had.co.nz/plyr/plyr-intro-090510.pdf for data manipulation
http://www.stats.ox.ac.uk/~ruth/RCourse/Rcourse3.pdfhttp://cran.r-project.org/doc/contrib/usingR.pdf for R basics
http://www.ats.ucla.edu/stat/r/dae/default.htm with annotated outputs in R
http://cran.r-project.org/doc/contrib/Rossiter-RIntro-ITC.pdf tutorial with info on plots
http://www.statmethods.net/stats/regression.htmlhttp://www.rmetrics.org/ provides an Open Source framework for Financial Analysis.
http://www.econ.uiuc.edu/~econ472/e-Tutorial.html has lecture notes with R code
A brief guide to R and Economicshttp://people.su.se/~ma/R_intro/R_intro.pdfhttp://www.stat.pitt.edu/stoffer/tsa2/index.html has a good beginner’s tutorial for Time Series
http://www.quantmod.com/ provides a great analysis and visualization framework for quantitative trading
http://www.wise.xmu.edu.cn/2007summerworkshop/download/Advanced%20Topics%20in%20Time%20Series%20Econometrics%20Using%20R1_ZongwuCAI.pdf advanced time series in R
Interesting time series packages in R http://robjhyndman.com/software
A Data Mining tool in R http://rattle.togaware.com/
An online e-book for Data Mining with R http://www.liaad.up.pt/~ltorgo/DataMiningWithR/
Advanced Statistics using R http://www.statmethods.net/advstats/index.html
Guide to Credit Scoring using R http://cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdfhttp://addictedtor.free.fr/graphiques/ is a graph gallery of R plots and charts with supporting code
A tutorial for Latticehttp://osiris.sunderland.ac.uk/~cs0her/Statistics/UsingLatticeGraphicsInR.htm
Ggplot R graphics http://had.co.nz/ggplot2/
Ggplot Vs Lattice @ http://had.co.nz/ggplot/vs-lattice.html
Multiple tutorials for using ggplot2 and Lattice http://learnr.wordpress.com/tag/ggplot2/
Introduction to the Text Mining package in R http://cran.r-project.org/web/packages/tm/vignettes/tm.pdf
Social Network Analysis http://www.r-project.org/conferences/useR-2008/slides/Bojanowski.pdf
Web Scraping in R http://www.programmingr.com/content/webscraping-using-readlines-and-rcurlhttp://learnr.wordpress.com/2009/10/06/export-data-frames-to-multi-worksheet-excel-file/ to embed R data frames in Excel via multiple approaches.
http://www.statconn.com/ provides a tool to make R usable from Excel
Connect to MySQL from R http://erikvold.com/blog/index.cfm/2008/8/20/how-to-connect-to-mysql-with-r-in-wndows-using-rmysqlhttp://www.statmethods.net/input/importingdata.htmlprovides info about pulling data from SAS, STATA, SPSS, etc.
Thematic Maps with R http://stackoverflow.com/questions/1260965/developing-geographic-thematic-maps-with-rhttp://smartdatacollective.com/Home/22052 for geographic maps in R
Google Charts with Rhttp://www.iq.harvard.edu/blog/sss/archives/2008/04/google_charts_f_1.shtml
Intro to using RGoogleMaps @ http://cran.r-project.org/web/packages/RgoogleMaps/vignettes/RgoogleMaps-intro.pdfhttp://www.stat.uni-muenchen.de/~leisch/Sweave/
R2HTML http://www.feferraz.net/en/P/R2HTML
Poor Man GUI for R http://wiener.math.csi.cuny.edu/pmg/
R Commander is a robust GUI for R http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/installation-notes.html
JGR is a Java-based GUI for R http://jgr.markushelbig.org/Screenshots.html
Tinn-R makes for a good R editor http://www.sciviews.org/Tinn-R/
An Eclipse plugin for R @ http://www.walware.de/goto/statet
Instructions to install StatET in Eclipsehttp://www.splusbook.com/Rintro/R_Eclipse_StatET.pdf
Komodo Edit R editor http://www.sciviews.org/SciViews-K/index.htmlhttp://www.omegahat.org/has a very interesting list of packages that is seriously worth a look
Commercial versions of R @ http://www.revolution-computing.com/
A very informative blog http://blog.revolution-computing.com/
Red R for R tasks http://code.google.com/p/r-orange/
KNIME for R http://www.knime.org/introduction/screenshots and is worth a serious look.

source: LinkedInRGroup with more

R in Action

R in Action. Data Analysis and Graphics with R, Robert I. Kabacoff // August, 2011 | 472 pages, ISBN 9781935182399

Part I Getting Started
1 Introduction to R
1.1 Why use R?
1.2 Obtaining and installing R
1.3 Working with R
1.4 Packages
1.5 Batch processing
1.6 Using output as input—reusing results
1.7 Working with large datasets
1.8 Working through an example
1.9 Summary
2 Creating a dataset
2.1 Understanding datasets
2.2 Data structures
2.3 Data input
2.4 Annotating datasets
2.5 Useful functions for working with data objects
2.6 Summary
3 Getting started with graphs
3.1 Working with graphs
3.2 A simple example
3.3 Graphical parameters
3.4 Adding text, customized axes, and legends
3.5 Combining graphs
3.6 Summary
4 Basic data management
4.1 A working example
4.2 Creating new variables
4.3 Recoding variables
4.4 Renaming variables
4.5 Missing values
4.6 Date values
4.7 Type conversions
4.8 Sorting data
4.9 Merging datasets
4.10 Subsetting datasets
4.11 Using SQL statements to manipulate data frames
4.12 Summary
5 Advanced data management
5.1 A data management challenge
5.2 Numerical and character functions
5.3 A solution for our data management challenge
5.4 Control flow
5.5 User-written functions
5.6 Aggregation and restructuring
5.7 Summary
Part II Basic Methods
6 Basic graphs
6.1 Bar plots
6.2 Pie charts
6.3 Histograms
6.4 Kernel density plots
6.5 Box plots
6.6 Dot plots
6.7 Summary
7 Basic statistics
7.1 Descriptive statistics
7.2 Frequency and contingency tables
7.3 Correlations
7.4 t-tests
7.5 Nonparametric tests of group differences
7.6 Visualizing group differences
7.7 Summary
Part III Intermediate Methods
8 Regression
8.1 The many faces of regression
8.2 OLS regression
8.3 Regression diagnostics
8.4 Unusual observations
8.5 Corrective measures
8.6 Selecting the “best” regression model
8.7 Taking the analysis further
8.8 Summary
9 Analysis of variance
9.1 A crash course on terminology
9.2 Fitting ANOVA models
9.3 One-way ANOVA
9.4 One-way ANCOVA
9.5 Two-way factorial ANOVA
9.6 Repeated measures ANOVA
9.7 Multivariate analysis of variance (MANOVA)
9.8 ANOVA as regression
9.9 Summary
10 Power analysis
10.1 A quick review of hypothesis testing
10.2 Implementing power analysis with the pwr package
10.3 Creating power analysis plots
10.4 Other packages
10.5 Summary
11 Intermediate graphs
11.1 Scatter plots
11.2 Line charts
11.3 Correlograms
11.4 Mosaic plots
11.5 Summary
12 Resampling statistics and bootstrapping
12.1 Permutation tests
12.2 Permutation test with the coin package
12.3 Permutation tests with the lmPerm package
12.4 Additional comments on permutation tests
12.5 Bootstrapping
12.6 Bootstrapping with the boot package
12.7 Summary
Part IV Advanced Methods
13 Generalized linear models
13.1 Generalized linear models and the glm() function
13.2 Logistic regression
13.3 Poisson regression
13.4 Summary
14 Principal components and factor analysis
14.1 Principal components and factor analysis in R
14.2 Principal components
14.3 Exploratory factor analysis
14.4 Other latent variable models
14.5 Summary
15 Advanced methods for missing data
15.1 Steps in dealing with missing data
15.2 Identifying missing values
15.3 Exploring missing values patterns
15.4 Understanding the sources and impact of missing data
15.5 Rational approaches for dealing with incomplete data
15.6 Complete-case analysis (listwise deletion)
15.7 Multiple imputation
15.8 Other approaches to missing data
15.9 Summary
16 Advanced graphics
16.1 The four graphic systems in R
16.2 The lattice package
16.3 The ggplot2 package
16.4 Interactive graphs
16.5 Summary

afterword Into the rabbit hole
appendix A Graphic user interfaces
appendix B Customizing the startup environment
appendix C Exporting data from R
appendix D Creating publication-quality output
appendix E Matrix Algebra in R
appendix F Packages used in this book
appendix G Working with large datasets
appendix H Updating an R installation
index

log-linear analysis in LGBT community

Логлинейный анализ

Перевод материала William B. KING, http://ww2.coastal.edu/kingw/statistics/R-tutorials/loglin.html 

см перевод 

Forecasting time series using R

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