R Statistical Software Download for Mac UPDATED

R Statistical Software Download for Mac

R on the Mac

R is a comprehensive statistical programming language that is cooperatively developed on the Net as an open source project. Information technology is oft referred to as the "GNU Southward," because it most completely emulates the S programming language. It has packages to do regression, ANOVA, general linear models, take a chance models and structural equations. Graphical output can be created using a TeX plug-in to convert the standard ASCII-based output.

R has a massive range of tests, PDF and PostScript output, a function to expand zip athenaeum, and numerous other unexpected features. R programs and algorithms are distributed by the Comprehensive R Archive Network (CRAN). A simple graphic user interface is included for Mac users; R Commander can be installed using the built-in package installer, which can too install file import features (which aren't installed by default). R Commander is an X11 plan, which means it uses an alien interface and has odd open/relieve dialogues, but if you get past that it offers carte du jour driven commands non dissimilar from, say, SPSS, just a lot more awkward to apply, and without an output or information window.

Like many open up source projects, R is exceedingly capable simply has a steep learning curve. Some believe this is for the best because people will get a deeper understanding of the statistics they generate with a plan such as R, versus one which allows the rapid creation of scads of irrelevant statistics leading to incorrect conclusions. Those who wait fifty-fifty a basic graphical interface (e.m. SPSS 4) may be disappointed by the R community's definition of a GUI.

Virtually of this page is rather out of date. Run into our free software page for more current simply less detailed information.

Ashish Ranpura wrote:

Concluding week I finally put R through its paces on two recent experiments from our lab. Information technology performed spectacularly. It's pretty easy to learn using online tutorials, in particular John Verzani's tutorial which is a grade in introductory statistics using R.

The highlight: figuring out the 15 or so commands to import, parse, slice and graph a 3-way comparison of control subjects using a scatterplot and a violin plot. And then using BBEdit to search and supplant the word "control" with my two experimental conditions, pasting that back into R, and generating a report with all 6 graphs in about three keystrokes! Now that's how a program ought to work.

Only the major advantages of R are that information technology is absolutely cross-platform (Linux, MacOS, Windows) and that it's open source. You've a good gamble of accessing your data 10 years from now, which I wouldn't say with the commercial packages. The user base of operations is large, active, and productive. The S language on which it's based is a well-accustomed standard in statistics. R has stood the test of time and is likely to go on to do and then.

There is one meaning caveat: R is relentlessly command-line driven, and even the graphs cannot be edited with mouse clicks. Information technology'south little to take the PDF graphs into Illustrator, though, so this limitation hasn't been a trouble for me.

Some resources include:

R for Mac FAQ
The R project home page (with download links)
This spider web page on R, S and S/Plus statistics systems, which provides a background on the software and summarizes available packages
Using R for structural equation modeling

R has a massive range of tests and now has Matrix as a recommended package, a useKerning argument for PDF and PostScript output, a recursive argument for file.copy(), an unzip office to expand or list zip athenaeum, and other changes.

There is a R for Mac Special Interest Group, chosen R-Sig-Mac. The group is implemented as an electronic mail list. Yous tin can subscribe to the list or see the archives going to its official web page: http://www.stat.math.ethz.ch/mailman/listinfo/r-sig-mac

Due south and R Programming Languages

Beginning in 1976, the S programming language was adult at Bell Labs (whose statistics section employed John Tukey and Joseph Kruskal) past John Chambers and others. Version i required Honeywell mainframes, Version ii (1980) added Unix support, Version iii (1988) added functions and objects, and Version iv (1998) added full support for object-oriented design. In 1993, Bong Labs issued an exclusive license to StatSci (later MathSoft). S-Plus is Mathsoft's commercial implementation of South, and the only way the language is available outside Clear-cut.

R was begun by Robert Admirer and Ross Ihaka of the University of Auckland. Information technology is at present an open source project staffed past volunteers from around the earth whose development is coordinated through the Comprehensive R Archive network. Source code, binaries, and documentation are at the CRAN web site.

Documentation that compares R and S include:

The R and S give-and-take in CRAN's FAQ.
The online supplement to Venables and Ripley (1999).
The published text of Venables and Ripley (2000), and its online errata.

Adapted from an August 2000 University of Management workshop on stat packages, nosotros are showing how to use R for analyses common in management research:

Base bundle commands:

anova: assay of variance
glm: full general linear model, including logit, probit and poisson models
ls/lsfit: fit an OLS or WLS regression model

Built-in packages

ts parcel:
- arima: ARIMA fourth dimension series models

Contributed R packages and their capabilities:

kick: bootstrapping and jacknifing
coda: analysis and diagnostics for Markov Concatenation Monte Carlo simulation
fracdiff: ARIMA time series models
matrix: matrix math
cmdscale: multi-dimensional scaling
multiv: cluster analysis, correspondance analysis, primary component factor analysis
pls: Partial Least Squares structural equation modeling
survival5: survival assay (hazard models)

Books by the MacStats maintainer