Revolution R for Linux

Screenshot of the Redhat Enterprise Linux Desktop
Image via Wikipedia

New software just released from the guys in California (@RevolutionR) so if you are a Linux user and have academic credentials you can download it for free  (@Cmastication doesnt), you can test it to see what the big fuss is all about (also see –

Revolution Analytics has just released Revolution R Enterprise 4.0.1 for Red Hat Enterprise Linux, a significant step forward in enterprise data analytics. Revolution R Enterprise 4.0.1 is built on R 2.11.1, the latest release of the open-source environment for data analysis and graphics. Also available is the initial release of our deployment server solution, RevoDeployR 1.0, designed to help you deliver R analytics via the Web. And coming soon to Linux: RevoScaleR, a new package for fast and efficient multi-core processing of large data sets.

As a registered user of the Academic version of Revolution R Enterprise for Linux, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.0.1 today. You can install Revolution R Enterprise 4.0.1 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.

Download Information

The following information is all you will need to download and install the Academic Edition.

Supported Platforms:

Revolution R Enterprise Academic edition and RevoDeployR are supported on Red Hat® Enterprise Linux® 5.4 or greater (64-bit processors).

Approximately 300MB free disk space is required for a full install of Revolution R Enterprise. We recommend at least 1GB of RAM to use Revolution R Enterprise.

For the full list of system requirements for RevoDeployR, refer to the RevoDeployR™ Installation Guide for Red Hat® Enterprise Linux®.

Download Links:

You will first need to download the Revolution R Enterprise installer.

Installation Instructions for Revolution R Enterprise Academic Edition

After downloading the installer, do the following to install the software:

  • Log in as root if you have not already.
  • Change directory to the directory containing the downloaded installer.
  • Unpack the installer using the following command:
    tar -xzf Revo-Ent-4.0.1-RHEL5-desktop.tar.gz
  • Change directory to the RevolutionR_4.0.1 directory created.
  • Run the installer by typing ./ and following the on-screen prompts.

Getting Started with the Revolution R Enterprise

After you have installed the software, launch Revolution R Enterprise by typing Revo64 at the shell prompt.

Documentation is available in the form of PDF documents installed as part of the Revolution R Enterprise distribution. Type Revo.home(“doc”) at the R prompt to locate the directory containing the manuals Getting Started with Revolution R (RevoMan.pdf) and the ParallelR User’s Guide(parRman.pdf).

Installation Instructions for RevoDeployR (and RServe)

After downloading the RevoDeployR distribution, use the following steps to install the software:

Note: These instructions are for an automatic install.  For more details or for manual install instructions, refer to RevoDeployR_Installation_Instructions_for_RedHat.pdf.

  1. Log into the operating system as root.
    su –
  2. Change directory to the directory containing the downloaded distribution for RevoDeployR and RServe.
  3. Unzip the contents of the RevoDeployR tar file. At prompt, type:
    tar -xzf deployrRedHat.tar.gz
  4. Change directories. At the prompt, type:
    cd installFiles
  5. Launch the automated installation script and follow the on-screen prompts. At the prompt, type:
    Note: Red Hat installs MySQL without a password.

Getting Started with RevoDeployR

After installing RevoDeployR, you will be directed to the RevoDeployR landing page. The landing page has links to documentation, the RevoDeployR management console, the API Explorer development tool, and sample code.


For help installing this Academic Edition, please email

Also interestingly some benchmarks on Revolution R vs R.

R-25 Benchmarks

The simple R-benchmark-25.R test script is a quick-running survey of general R performance. The Community-developed test consists of three sets of small benchmarks, referred to in the script as Matrix Calculation, Matrix Functions, and Program Control.

R-25 Matrix Calculation R-25 Matrix Functions R-Matrix Program Control
R-25 Benchmarks Base R 2.9.2 Revolution R (1-core) Revolution R (4-core) Speedup (4 core)
Matrix Calculation 34 sec 6.6 sec 4.4 sec 7.7x
Matrix Functions 20 sec 4.4 sec 2.1 sec 9.5x
Program Control 4.7 sec 4 sec 4.2 sec Not Appreciable

Speedup = Slower time / Faster Time – 1   Test descriptions available at

Additional Benchmarks

Revolution Analytics has created its own tests to simulate common real-world computations.  Their descriptions are explained below.

Matrix Multiply Cholesky Factorization
Singular Value Decomposition Principal Component Analysis Linear Discriminant Analysis
Linear Algebra Computation Base R 2.9.2 Revolution R (1-core) Revolution R (4-core) Speedup (4 core)
Matrix Multiply 243 sec 22 sec 5.9 sec 41x
Cholesky Factorization 23 sec 3.8 sec 1.1 sec 21x
Singular Value Decomposition 62 sec 13 sec 4.9 sec 12.6x
Principal Components Analysis 237 sec 41 sec 15.6 sec 15.2x
Linear Discriminant Analysis 142 sec 49 sec 32.0 sec 4.4x

Speedup = Slower time / Faster Time – 1

Matrix Multiply

This routine creates a random uniform 10,000 x 5,000 matrix A, and then times the computation of the matrix product transpose(A) * A.

set.seed (1)
m <- 10000
n <-  5000
A <- matrix (runif (m*n),m,n)
system.time (B <- crossprod(A))

The system will respond with a message in this format:

User   system elapsed
37.22    0.40   9.68

The “elapsed” times indicate total wall-clock time to run the timed code.

The table above reflects the elapsed time for this and the other benchmark tests. The test system was an INTEL® Xeon® 8-core CPU (model X55600) at 2.5 GHz with 18 GB system RAM running Windows Server 2008 operating system. For the Revolution R benchmarks, the computations were limited to 1 core and 4 cores by calling setMKLthreads(1) and setMKLthreads(4) respectively. Note that Revolution R performs very well even in single-threaded tests: this is a result of the optimized algorithms in the Intel MKL library linked to Revolution R. The slight greater than linear speedup may be due to the greater total cache available to all CPU cores, or simply better OS CPU scheduling–no attempt was made to pin execution threads to physical cores. Consult Revolution R’s documentation to learn how to run benchmarks that use less cores than your hardware offers.

Cholesky Factorization

The Cholesky matrix factorization may be used to compute the solution of linear systems of equations with a symmetric positive definite coefficient matrix, to compute correlated sets of pseudo-random numbers, and other tasks. We re-use the matrix B computed in the example above:

system.time (C <- chol(B))

Singular Value Decomposition with Applications

The Singular Value Decomposition (SVD) is a numerically-stable and very useful matrix decompisition. The SVD is often used to compute Principal Components and Linear Discriminant Analysis.

# Singular Value Deomposition
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (S <- svd (A,nu=0,nv=0))

# Principal Components Analysis
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (P <- prcomp(A))

# Linear Discriminant Analysis
require (‘MASS’)
g <- 5
k <- round (m/2)
A <- data.frame (A, fac=sample (LETTERS[1:g],m,replace=TRUE))
train <- sample(1:m, k)
system.time (L <- lda(fac ~., data=A, prior=rep(1,g)/g, subset=train))

Interview Michael J. A. Berry Data Miners, Inc

Here is an interview with noted Data Mining practitioner Michael Berry, author of seminal books in data mining, noted trainer and consultantmjab picture

Ajay- Your famous book “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management” came out in 2004, and an update is being planned for 2011. What are the various new data mining techniques and their application that you intend to talk about in that book.

Michael- Each time we do a revision, it feels like writing a whole new book. The first edition came out in 1997 and it is hard to believe how much the world has changed since then. I’m currently spending most of my time in the on-line retailing world. The things I worry about today–improving recommendations for cross-sell and up-sell,and search engine optimization–wouldn’t have even made sense to me back then. And the data sizes that are routine today were beyond the capacity of the most powerful super computers of the nineties. But, if possible, Gordon and I have changed even more than the data mining landscape. What has changed us is experience. We learned an awful lot between the first and second editions, and I think we’ve learned even more between the second and third.

One consequence is that we now have to discipline ourselves to avoid making the book too heavy to lift. For the first edition, we could write everything we knew (and arguably, a bit more!); now we have to remind ourselves that our intended audience is still the same–intelligent laymen with a practical interest in getting more information out of data. Not statisticians. Not computer scientists. Not academic researchers. Although we welcome all readers, we are primarily writing for someone who works in a marketing department and has a title with the word “analyst” or “analytics” in it. We have relaxed our “no equations” rule slightly for cases when the equations really do make things easier to explain, but the core explanations are still in words and pictures.

The third edition completes a transition that was already happening in the second edition. We have fully embraced standard statistical modeling techniques as full-fledged components of the data miner’s toolkit. In the first edition, it seemed important to make a distinction between old, dull, statistics, and new, cool, data mining. By the second edition, we realized that didn’t really make sense, but remnants of that attitude persisted. The third edition rectifies this. There is a chapter on statistical modeling techniques that explains linear and logistic regression, naive Bayes models, and more. There is also a brand new chapter on text mining, a curious omission from previous editions.

There is also a lot more material on data preparation. Three whole chapters are devoted to various aspects of data preparation. The first focuses on creating customer signatures. The second is focused on using derived variables to bring information to the surface, and the third deals with data reduction techniques such as principal components. Since this is where we spend the greatest part of our time in our work, it seemed important to spend more time on these subjects in the book as well.

Some of the chapters have been beefed up a bit. The neural network chapter now includes radial basis functions in addition to multi-layer perceptrons. The clustering chapter has been split into two chapters to accommodate new material on soft clustering, self-organizing maps, and more. The survival analysis chapter is much improved and includes material on some of our recent application of survival analysis methods to forecasting. The genetic algorithms chapter now includes a discussion of swarm intelligence.

Ajay- Describe your early career and how you came into Data Mining as a profession. What do you think of various universities now offering MS in Analytics. How do you balance your own teaching experience with your consulting projects at The Data Miners.

Michael- I fell into data mining quite by accident. I guess I always had a latent interest in the topic. As a high school and college student, I was a fan of Martin Gardner‘s mathematical games in in Scientific American. One of my favorite things he wrote about was a game called New Eleusis in which one players, God, makes up a rule to govern how cards can be played (“an even card must be followed by a red card”, say) and the other players have to figure out the rule by watching what plays are allowed by God and which ones are rejected. Just for my own amusement, I wrote a computer program to play the game and presented it at the IJCAI conference in, I think, 1981.

That paper became a chapter in a book on computer game playing–so my first book was about finding patterns in data. Aside from that, my interest in finding patterns in data lay dormant for years. At Thinking Machines, I was in the compiler group. In particular, I was responsible for the run-time system of the first Fortran Compiler for the CM-2 and I represented Thinking Machines at the Fortran 8X (later Fortran-90) standards meetings.

What changed my direction was that Thinking Machines got an export license to sell our first machine overseas. The machine went to a research lab just outside of Paris. The connection machine was so hard to program, that if you bought one, you got an applications engineer to go along with it. None of the applications engineers wanted to go live in Paris for a few months, but I did.

Paris was a lot of fun, and so, I discovered, was actually working on applications. When I came back to the states, I stuck with that applied focus and my next assignment was to spend a couple of years at Epsilon, (then a subsidiary of American Express) working on a database marketing system that stored all the “records of charge” for American Express card members. The purpose of the system was to pick ads to go in the billing envelope. I also worked on some more general purpose data mining software for the CM-5.

When Thinking Machines folded, I had the opportunity to open a Cambridge office for a Virginia-based consulting company called MRJ that had been a major channel for placing Connection Machines in various government agencies. The new group at MRJ was focused on data mining applications in the commercial market. At least, that was the idea. It turned out that they were more interested in data warehousing projects, so after a while we parted company.

That led to the formation of Data Miners. My two partners in Data Miners, Gordon Linoff and Brij Masand, share the Thinking Machines background.

To tell the truth, I really don’t know much about the university programs in data mining that have started to crop up. I’ve visited the one at NC State, but not any of the others.

I myself teach a class in “Marketing Analytics” at the Carroll School of Management at Boston College. It is an elective part of the MBA program there. I also teach short classes for corporations on their sites and at various conferences.

Ajay- At the previous Predictive Analytics World, you took a session on Forecasting and Predicting Subsciber levels ( .

It seems inability to forecast is a problem many many companies face today. What do you think are the top 5 principles of business forecasting which companies need to follow.

Michael- I don’t think I can come up with five. Our approach to forecasting is essentially simulation. We try to model the underlying processes and then turn the crank to see what happens. If there is a principal behind that, I guess it is to approach a forecast from the bottom up rather than treating aggregate numbers as a time series.

Ajay- You often partner your talks with SAS Institute, and your blog at sometimes contain SAS code as well. What particular features of the SAS software do you like. Do you use just the Enterprise Miner or other modules as well for Survival Analysis or Forecasting.

Michael- Our first data mining class used SGI’s Mineset for the hands-on examples. Later we developed versions using Clementine, Quadstone, and SAS Enterprise Miner. Then, market forces took hold. We don’t market our classes ourselves, we depend on others to market them and then share in the revenue.

SAS turned out to be much better at marketing our classes than the other companies, so over time we stopped updating the other versions. An odd thing about our relationship with SAS is that it is only with the education group. They let us use Enterprise Miner to develop course materials, but we are explicitly forbidden to use it in our consulting work. As a consequence, we don’t use it much outside of the classroom.

Ajay- Also any other software you use (apart from SQL and J)

Michael- We try to fit in with whatever environment our client has set up. That almost always is SQL-based (Teradata, Oracle, SQL Server, . . .). Often SAS Stat is also available and sometimes Enterprise Miner.

We run into SPSS, Statistica, Angoss, and other tools as well. We tend to work in big data environments so we’ve also had occasion to use Ab Initio and, more recently, Hadoop. I expect to be seeing more of that.


Together with his colleague, Gordon Linoff, Michael Berry is author of some of the most widely read and respected books on data mining. These best sellers in the field have been translated into many languages. Michael is an active practitioner of data mining. His books reflect many years of practical, hands-on experience down in the data mines.

Data Mining Techniques cover

Data Mining Techniques for Marketing, Sales and Customer Relationship Management

by Michael J. A. Berry and Gordon S. Linoff
copyright 2004 by John Wiley & Sons

Mining the Web cover

Mining the Web

by Michael J.A. Berry and Gordon S. Linoff
copyright 2002 by John Wiley & Sons
ISBN 0-471-41609-6

Non-English editions available in Traditional Chinese and Simplified Chinese

This book looks at the new opportunities and challenges for data mining that have been created by the web. The book demonstrates how to apply data mining to specific types of online businesses, such as auction sites, B2B trading exchanges, click-and-mortar retailers, subscription sites, and online retailers of digital content.

Mastering Data Mining

by Michael J.A. Berry and Gordon S. Linoff
copyright 2000 by John Wiley & Sons
ISBN 0-471-33123-6

Non-English editions available in JapaneseItalianTraditional Chinese , and Simplified Chinese

A case study-based guide to applying data mining techniques for solving practical business problems. These “warts and all” case studies are drawn directly from consulting engagements performed by the authors.

A data mining educator as well as a consultant, Michael is in demand as a keynote speaker and seminar leader in the area of data mining generally and the application of data mining to customer relationship management in particular.

Prior to founding Data Miners in December, 1997, Michael spent 8 years at Thinking Machines Corporation. There he specialized in the application of massively parallel supercomputing techniques to business and marketing applications, including one of the largest database marketing systems of the time.

New Deal in Statistical Training

The United States Government is planning a new initiative at providing employable skills to people, to cope with unemployment.
One skill perpetually in shortage is analytics training along with skills in statistics.

It is time that corporates like IBM SPSS, SAS Institute and Revolution Analytics as well as offshore companies in India or Asia can ramp up their on demand trainings, certification as well as academic partnership bundles. Indeed offshroing companies can earn revenue as well as goodwill if they help in with trainers available via video- conferencing. The new Deal initiative would require creative thinking as well as direct top management support to focus their best internal brains at developing this new revenue stream. Again the company that trains the most users (be it Revolution for R, IBM for SPSS-Cognos, SAS Institute for Base SAS-JMP, WPS for SAS language) is going to get a bigger chunk of new users and analysts.

Analytics skills are hot. There is big new demand for hot new skills by millions of unemployed Americans and Asians. How do you think this services market will play out?

If the US government could pump 800 Billion for bailouts, how much is your opinion it should spend on training programs to help citizens compete globally?


The national program is a response to frustrations from both workers and employers who complain that public retraining programs frequently do not provide students with employable skills. This new initiative is intended to help better align community college curriculums with the demands of local companies.

SAS recognizes the market –


In tough economic times, it is more important than ever that companies be able to make better decisions using analytics. SAS is involved in two programs this summer that offer MBAs and unemployed technology workers the opportunity to learn and enhance analytics skills, and increase their marketability.

SAS is a partner in TechEngage, a week-long program of training classes that offer unemployed technology professionals new skills at a low cost to help them compete effectively in the marketplace.”

So does IBM-

. “Fordham has a long history of collaboration with IBM that has brought innovative new skills to our curriculum to prepare students for future jobs. With this effort, Fordham is preparing students with marketable skills for a coming wave of jobs in healthcare, sustainability, and social services where analytics can be applied to everyday challenges.”

and R

Well TIBCO and Revolution ….hmmm…mmmm

I am not sure there is even a R Analytics Certification program at the least.

JMP 9 releasing on Oct 12

JMP 9 releases on Oct 12- it is a very good reliable data visualization and analytical tool ( AND available on Mac as well)

AND IT is advertising R Graphics as well (lol- I can visualize the look on some ahem SAS fans in the R Project)

Updated Pricing- note I am not sure why they are charging US academics 495$ when SAS On Demand is free for academics. Shouldnt JMP be free to students- maybe John Sall and his people can do a tradeoff analysis for this given JMP’s graphics are better than Base SAS (which is under some pressure from WPS and R)


*Offer good in the U.S. only.

New Corporate Customer


Save $300.

No special requirements.
Corporate Upgrade


Save $155.

Complete the form below or call 1-877-594-6567. Requires valid JMP® 8 serial number.
New Academic


Save $100.

Complete the form below or call 1-877-594-6567. Requires campus street address and campus e-mail address.
Academic Upgrade


Save $45.

Complete the form below or call 1-877-594-6567. Requires campus street address and campus e-mail address.

From- the mailer-

Be First in Line for JMP® 9
Save up to $300 when you pre-order a
single-user license by Oct. 11

Pre-Order JMP 9

Make JMP your analytic hub for visual data discovery with this special offer, good through Oct. 11, 2010. Pre-order a single-user license of JMP 9 – for a discount of up to $300 – and get ready for a leap in data interactivity.

Order now and enjoy the compelling new features of JMP 9 when the software is released Oct. 12. New capabilities in JMP 9 let you:

  • Optimize and simulate using your Microsoft Excel spreadsheets.
  • Use maps to find patterns in your geographic data.
  • Enjoy the updated look and flexibility of JMP 9 on Microsoft Windows.
  • Create and share custom add-ins that extend JMP.
  • Leverage an expanded array of advanced statistical methodologies.
  • Display analytic results from R using interactive graphics.


What if I already have a JMP 8 single-user license?
Great news! You can upgrade to JMP 9 for less than half the regular price.

What if I’m an annual license customer?
Don’t worry, we’ve got you covered. Annual license customers enjoy priority access to all the latest JMP releases as soon as they become available. JMP 9 will be shipped to you automatically.

What if I work or study in the academic world?
Call 1-877-594-6567 to learn about significant discounts for students and professors through the JMP Academic Program.

Please feel free to forward this offer to interested colleagues.

Got two or more users?
A JMP® annual license is the way to go. Call for details.

Remember: Act by Oct. 11!

JMP runs on Macintosh and Windows

John M. Chambers Statistical Software Award – 2011

Write code, win cash, and the glory. Deep bow to Father John M Chambers, inventor of S ,for endowing this award for statistical software creation by grads and undergrads.

An effort to be matched by companies like SAS, SPSS which after all came from grad school work. Now back to the competition, I gotta get my homies from U Tenn in a team ( I was a grad student last year though taking this year off due to medico- financial reasons)

John M. Chambers Statistical Software Award – 2011
Statistical Computing Section
American Statistical Association

The Statistical Computing Section of the American Statistical
Association announces the competition for the John M.  Chambers
Statistical Software Award. In 1998 the Association for Computing
Machinery presented its Software System Award to John Chambers for the
design and development of S. Dr. Chambers generously donated his award
to the Statistical Computing Section to endow an annual prize for
statistical software written by an undergraduate or graduate student.
The prize carries with it a cash award of $1000, plus a substantial
allowance for travel to the annual Joint Statistical Meetings where
the award will be presented.

Teams of up to 3 people can participate in the competition, with the
cash award being split among team members. The travel allowance will
be given to just one individual in the team, who will be presented the
award at JSM.  To be eligible, the team must have designed and
implemented a piece of statistical software.
The individual within
the team indicated to receive the travel allowance must have begun the
development while a student, and must either currently be a student,
or have completed all requirements for her/his last degree after
January 1, 2009.  To apply for the award, teams must provide the
following materials:

Current CV’s of all team members.

A letter from a faculty mentor at the academic institution of the
individual indicated to receive the travel award.  The letter
should confirm that the individual had substantial participation in
the development of the software, certify her/his student status
when the software began to be developed (and either the current
student status or the date of degree completion), and briefly
discuss the importance of the software to statistical practice.

A brief, one to two page description of the software, summarizing
what it does, how it does it, and why it is an important
contribution.  If the team member competing for the travel
allowance has continued developing the software after finishing
her/his studies, the description should indicate what was developed
when the individual was a student and what has been added since.

An installable software package with its source code for use by the
award committee. It should be accompanied by enough information to allow
the judges to effectively use and evaluate the software (including
its design considerations.)  This information can be provided in a
variety of ways, including but not limited to a user manual (paper
or electronic), a paper, a URL, and online help to the system.

All materials must be in English.  We prefer that electronic text be
submitted in Postscript or PDF.  The entries will be judged on a
variety of dimensions, including the importance and relevance for
statistical practice of the tasks performed by the software, ease of
use, clarity of description, elegance and availability for use by the
statistical community. Preference will be given to those entries that
are grounded in software design rather than calculation.  The decision
of the award committee is final.

All application materials must be received by 5:00pm EST, Monday,
February 21, 2011 at the address below.  The winner will be announced
in May and the award will be given at the 2011 Joint Statistical

Information on the competition can also be accessed on the website of
the Statistical Computing Section ( or see the
ASA website, for a pointer), including the names and
contributions of previous winners.  Inquiries and application
materials should be emailed or mailed to:

Chambers Software Award
c/o Fei Chen
Avaya Labs
233 Mt Airy Rd.
Basking Ridge, NJ 07920

Trrrouble in land of R…and Open Source Suggestions

Recently some comments by Ross Ihake , founder of R Statistical Software on Revolution Analytics, leading commercial vendor of R….. came to my attention-

[R-downunder] Article on Revolution Analytics

Ross Ihaka ihaka at
Mon May 10 14:27:42 NZST 2010

On 09/05/10 09:52, Murray Jorgensen wrote:
> Perhaps of interest:

Please note that R is "free software" not "open source".  These guys
are selling a GPLed work without disclosing the source to their part
of the work. I have complained to them and so far they have given me
the brush off. I am now considering my options.

Don't support these guys by buying their product. The are not feeding
back to the rights holders (the University of Auckland and I are rights
holders and they didn't even have the courtesy to contact us).

Ross Ihaka                         Email:  ihaka at
Department of Statistics           Phone:  (64-9) 373-7599 x 85054
University of Auckland             Fax:    (64-9) 373-7018
Private Bag 92019, Auckland
New Zealand
and from
Open source purists probably won't be all too happy to learn that Revolution is going to be employing an "open core" strategy, which means the core R programs will remain open source and be given tech support under a license model, but the key add-ons that make R more scalable will be closed source and sold under a separate license fee. Because most of those 2,500 add-ons for R were built by academics and Revolution wants to supplant SPSS and SAS as the tools used by students, Revolution will be giving the full single-user version of the R Enterprise stack away for free to academics. 
So one co-founder of R is advocating not to buy from Revolution Analytics , which has the other co-founder of R, Gentleman on its board. 

2) If Revolution Analytics is using 2500 packages for free but insisting on getting paid AND closing source of it’s packages (which is a technical point- how exactly can you prevent source code of a R package from being seen)

Maybe there can be a PACKAGE marketplace just like Android Apps, Facebook Apps, and Apps – so atleast some of the thousands of R package developers can earn – sorry but email lists do not pay mortgages and no one is disputing the NEED for commercializing R or rewarding developers.

Though Barr created SAS, he gave up control to Goodnight and Sall

and Goodnight and Sall do pay their developers well- to the envy of not so well paid counterparts.

3) I really liked the innovation of Revolution Analytics RevoScalar, and I wish that the default R dataset be converted to XDF dataset so that it basically kills

off the R criticism of being slow on bigger datasets. But I also realize the need for creating an analytics marketplace for R developers and R students- so academic version of R being free and Revolution R being paid seems like a trade off.

Note- You can still get a job faster as a stats student if you mention SAS and not R as a statistical skill- not all stats students go into academics.

4) There can be more elegant ways of handling this than calling for ignoring each other as REVOLUTION and Ihake seem to be doing to each other.

I can almost hear people in Cary, NC chuckling at Norman Nie, long time SPSS opponent and now REVOLUTION CEO, and his antagonizing R’s academicians within 1 year of taking over- so I hope this ends well for all. The road to hell is paved with good intentions- so if REVOLUTION can share some source code with say R Core members (even Microsoft shares source code with partners)- and R Core and Revolution agree on a licensing royalty from each other, they can actually speed up R package creation rather than allow this 2 decade effort to end up like S and S plus and TIBCO did.

Maybe Richard Stallman can help-or maybe Ihaka has a better sense of where things will go down in a couple of years-he must know something-he invented it, didnt he

On 09/05/10 09:52, Murray Jorgensen wrote:
> Perhaps of interest:

Please note that R is "free software" not "open source".  These guys
are selling a GPLed work without disclosing the source to their part
of the work. I have complained to them and so far they have given me
the brush off. I am now considering my options.

Don't support these guys by buying their product. The are not feeding
back to the rights holders (the University of Auckland and I are rights
holders and they didn't even have the courtesy to contact us).

Ross Ihaka                         Email:  ihaka at
Department of Statistics           Phone:  (64-9) 373-7599 x 85054
University of Auckland             Fax:    (64-9) 373-7018
Private Bag 92019, Auckland
New Zealand

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