Is 21 st century cloud computing same as 1960's time sharing

Diagram showing three main types of cloud comp...
Image via Wikipedia

and yes Prof Goodnight, cloud computing is not time sharing. (Dr J was on a roll there- bashing open source AND cloud computing in the SAME interview at http://www.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal)

What was time sharing? In the 1960’s when people had longer hair, listened to the Beatles and IBM actually owned ALL computers-

http://en.wikipedia.org/wiki/Time-sharing

or is it?

The Internet has brought the general concept of time-sharing back into popularity. Expensive corporate server farms costing millions can host thousands of customers all sharing the same common resources. As with the early serial terminals, websites operate primarily in bursts of activity followed by periods of idle time. This bursting nature permits the service to be used by many website customers at once, and none of them notice any delays in communications until the servers start to get very busy.

What is 21 st century cloud computing? Well… they are still writing papers to define it BUT http://en.wikipedia.org/wiki/Cloud_computing

Cloud computing is Web-based processing, whereby shared resources, software, and information are provided to computers and other devices (such as smartphones) on demand over the Internet.

 

 

Open Source's worst enemy is itself not Microsoft/SAS/SAP/Oracle

The decision of quality open source makers to offer their software at bargain basement prices even to enterprise customers who are used to pay prices many times more-pricing is the reason open source software is taking a long time to command respect in enterprise software.

I hate to be the messenger who brings the bad news to my open source brethren-

but their worst nightmare is not the actions of their proprietary competitors like Oracle, SAP, SAS, Microsoft ( they hate each other even more than open source )

nor the collective marketing tactics which are textbook like (but referred as Fear Uncertainty Doubt by those outside that golden quartet)- it is their own communities and their own cheap pricing.

It is community action which prevents them from offering their software by ridiculously low bargain basement prices. James Dixon, head geek and founder at Pentaho has a point when he says traditional metrics like revenue need o be adjusted for this impact in his article at http://jamesdixon.wordpress.com/2010/11/02/comparing-open-source-and-proprietary-software-markets/

But James, why offer software to enterprise customers at one tenth the next competitor- one reason is open source companies more often than not compete more with their free community version software than with big proprietary packages.

Communities including academics are used to free- hey how about paying say 1$ for each download.

There are two million R users- if say even 50 % of them  paid 1 $ as a lifetime license fee- you could sponsor enough new packages than twenty years of Google Summer of Code does right now.

Secondly, this pricing can easily be adjusted by shifting the licensing to say free for businesses less than 2 people (even for the enhanced corporate software version not just the plain vanilla community software thus further increasing the spread of the plain vanilla versions)- for businesses from 10 to 20 people offer a six month trial rather than one month trial.

– but adjust the pricing to much more realistic levels compared to competing software. Make enterprise software pay a real value.

That’s the only way to earn respect. as well as a few dollars more.

As for SAS, it is time it started ridiculing Python now that it has accepted R.

Python is even MORE powerful than R in some use cases for stat computing

Dixon’s Pentaho and the Jaspersoft/ Revolution combo are nice _ I tested both Jasper and Pentaho thanks to these remarks this week 🙂  (see slides at http://www.jaspersoft.com/sites/default/files/downloads/events/Analytics%20-Jaspersoft-SEP2010.pdf or http://www.revolutionanalytics.com/news-events/free-webinars/2010/deploying-r/index.php )

Pentaho and Jasper do give good great graphics in BI (Graphical display in BI is not a SAS forte though probably I dont know how much they cross sell JMP to BI customers- probably too much JMP is another division syndrome there)

Jim Goodnight on Open Source- and why he is right -sigh

Logo Open Source Initiative
Image via Wikipedia

Jim Goodnight – grand old man and Godfather of the Cosa Nostra of the BI/Database Analytics software industry said recently on open source in BI (btw R is generally termed in business analytics and NOT business intelligence software so these remarks were more apt to Pentaho and Jaspersoft )

Asked whether open source BI and data integration software from the likes of Jaspersoft, Pentaho and Talend is a growing threat, [Goodnight] said: “We haven’t noticed that a lot. Most of our companies need industrial strength software that has been tested, put through every possible scenario or failure to make sure everything works correctly.”

quotes from Jim Goodnight are courtesy Jason’s  story here:
http://www.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal

and the Pentaho follow-up reaction is here

http://bi.cbronline.com/news/pentaho-fires-back-across-sas-bows-over-limited-open-source-appeal

 

 

While you can rage and screech- here is the reality in terms of market share-

From Merv Adrian-‘s excellent article on market shares in BI

http://www.enterpriseirregulars.com/22444/decoding-bi-market-share-numbers-%E2%80%93-play-sudoku-with-analysts/

The first, labeled BI Platforms, is drawn fromGartner Market Share Analysis: Business Intelligence, Analytics and Performance Management Software, Worldwide, 2009, published May 2010 , and Gartner Dataquest Market Share: Business Intelligence, Analytics and Performance Management Software, Worldwide, 2009.

and

Advanced Analytics category.

and 

so whats the performance of Talend, Pentaho and Jaspersoft

From http://www.dbms2.com/category/products-and-vendors/talend/

It seems that Talend’s revenue was somewhat shy of $10 million in 2008.

and Talend itself says

http://www.talend.com/press/Talend-Announces-Record-2009-and-Continues-Growth-in-the-New-Year.php

Additional 2009 highlights include:

  • Achieved record revenue, more then doubling from 2008. The fourth quarter of 2009 was Talend’s tenth consecutive quarter of growth.
  • Grew customer base by 140% to over 1,000 customers, up from 420 at the end of 2008. Of these new customers, over 50% are Fortune 1000 companies.
  • Total downloads reached seven million, with over 300,000 users of the open source products.
  • Talend doubled its staff, increasing to 200 global employees. Continuing this trend, Talend has already hired 15 people in 2010 to support its rapid growth.

now for Jaspersoft numbers

http://www.dbms2.com/2008/09/14/jaspersoft-numbers/

Highlights include:

  • Revenue run rate in the double-digit millions.
  • 40% sequential growth most recent quarter. (I didn’t ask whether there was any reason to suspect seasonality.)
  • 130% annual revenue growth run rate.
  • “Not quite” profitable.
  • Several hundred commercial subscribers, at an average of $25K annually per, including >100 in Europe.
  • 9,000 paying customers of some kind.
  • 100,000+ total deployments, “very conservatively,” counting OEMs as one deployment each and not double-counting for OEMs’ customers. (Nick said Business Objects quotes 45,000 deployments by the same standards.)
  • 70% of revenue from the mid-market, defined as $100 million – $1 billion revenue. 30% from bigger enterprises. (Hmm. That begs a couple of questions, such as where OEM revenue comes in, and whether <$100 million enterprises were truly a negligible part of revenue.)

and for Pentaho numbers-

http://www.dbms2.com/2009/01/27/introduction-to-pentaho/

and http://www.monash.com/uploads/Pentaho-January-2009.pdf

suggests there are far far away from the top 5-6 vendors in BI

and a special mention  for postgreSQL– which is a non Profit but is seriously denting Oracle/MySQL

http://www.postgresql.org/about/

Limit Value
Maximum Database Size Unlimited
Maximum Table Size 32 TB
Maximum Row Size 1.6 TB
Maximum Field Size 1 GB
Maximum Rows per Table Unlimited
Maximum Columns per Table 250 – 1600 depending on column types
Maximum Indexes per Table Unlimited

and leading vendor is EnterpriseDB which is again IBM-partnering as well as IBM funded

http://www.sramanamitra.com/2009/05/18/enterprise-db/

and

http://www.enterprisedb.com/company/news_events/press_releases/2010_21.do

suggest it is still in early stages.

————————————————————–

So what do we conclude-

1) There is a complete lack of transparency in open source BI market shares as almost all these companies are privately held and do not disclose revenues.

2) What may be a pure play open source company may actually be a company funded by a big BI vendor (like Revolution Analytics is funded among others by Intel-Microsoft) and EnterpriseDB has IBM as an investor.MySQL and Sun of course are bought by Oracle

The degree of control by proprietary vendors on open source vendors is still not disclosed- whether they are holding a stake for strategic reasons or otherwise.

3) None of the Open Source Vendors are even close to a 1 Billion dollar revenue number.

Jim Goodnight is pointing out market reality when he says he has not seen much impact (in terms of market share). As for the rest of his remarks, well he’s got a job to do as CEO and thats talk up his company and trash the competition- which he as been doing for 3 decades and unlikely to change now unless there is severe market share impact. Unless you expect him to notice companies less than 5% of his size in revenue.

http://www.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal

http://bi.cbronline.com/news/pentaho-fires-back-across-sas-bows-over-limited-open-source-appeal

 

Getting Inside R

Forums and Minerals, the new Internet tools
Image via Wikipedia

I loved the new upgraded design of Inside-R, Revo’s new(?) community.

And promptly shot up a blog application.

What makes Inside- R- slightly better than SDC, Analyticbridge,PlanetR and R _bloggers (with due respects)

  1. Open Id logins (I think thats a new and good step)
  2. Options for automated feed parsing for blogs
  3. More than just a blog aggregator- includes sections on other stuff- thus more like a community than a big feed
  4. Abbreviated feeds- just gives you two-three lines of summary per post  than the whole big schmakaround -thats a time saver for me —(D Smith is the only -lonely blogger atm there)
  5. The more the merrier- One more place to read and write R.


btw is the name insider (as in guy who knows inside stuff) or Inside- R (as in get inside the R box)- just kidding. With PlyR, ManipulatR, ApplyR and now Inside R- the pun gets MerrieR

If my blog app gets rejected- these views may change ,grr


Interesting R competition at Reddit

Image representing Reddit as depicted in Crunc...
Image via CrunchBase

Here is an interesting R competition going on at Reddit and it is to help Reddit make a recommendation engine 🙂

http://www.reddit.com/r/redditdev/comments/dtg4j/want_to_help_reddit_build_a_recommender_a_public/

by ketralnis

As promised, here is the big dump of voting information that you guys donated to research. Warning: this contains much geekery that may result in discomfort for the nerd-challenged.

I’m trying to use it to build a recommender, and I’ve got some preliminary source code. I’m looking for feedback on all of these steps, since I’m not experienced at machine learning.

Here’s what I’ve done

  • I dumped all of the raw data that we’ll need to generate the public dumps. The queries are the comments in the two .pig files and it took about 52 minutes to do the dump against production. The result of this raw dump looks like:
    $ wc -l *.dump
     13,830,070 reddit_data_link.dump
    136,650,300 reddit_linkvote.dump
         69,489 reddit_research_ids.dump
     13,831,374 reddit_thing_link.dump
    
  • I filtered the list of votes for the list of users that gave us permission to use their data. For the curious, that’s 67,059 users: 62,763 with “public votes” and 6,726 with “allow my data to be used for research”. I’d really like to see that second category significantly increased, and hopefully this project will be what does it. This filtering is done by srrecs_researchers.pig and took 83m55.335s on my laptop.
  • I converted data-dumps that were in our DB schema format to a more useable format using srrecs.pig(about 13min)
  • From that dump I mapped all of the account_ids, link_ids, and sr_ids to salted hashes (using obscure() insrrecs.py with a random seed, so even I don’t know it). This took about 13min on my laptop. The result of this, votes.dump is the file that is actually public. It is a tab-separated file consisting in:
    account_id,link_id,sr_id,dir
    

    There are 23,091,688 votes from 43,976 users over 3,436,063 links in 11,675 reddits. (Interestingly these ~44k users represent almost 17% of our total votes). The dump is 2.2gb uncompressed, 375mb in bz2.

What to do with it

The recommendations system that I’m trying right now turns those votes into a set of affinities. That is, “67% of user #223’s votes on /r/reddit.com are upvotes and 52% on programming). To make these affinities (55m45.107s on my laptop):

 cat votes.dump | ./srrecs.py "affinities_m()" | sort -S200m | ./srrecs.py "affinities_r()" > affinities.dump

Then I turn the affinities into a sparse matrix representing N-dimensional co-ordinates in the vector space of affinities (scaled to -1..1 instead of 0..1), in the format used by R’s skmeans package (less than a minute on my laptop). Imagine that this matrix looks like

          reddit.com pics       programming horseporn  bacon
          ---------- ---------- ----------- ---------  -----
ketralnis -0.5       (no votes) +0.45       (no votes) +1.0
jedberg   (no votes) -0.25      +0.95       +1.0       -1.0
raldi     +0.75      +0.75      +0.7        (no votes) +1.0
...

We build it like:

# they were already grouped by account_id, so we don't have to
# sort. changes to the previous step will probably require this
# step to have to sort the affinities first
cat affinities.dump | ./srrecs.py "write_matrix('affinities.cm', 'affinities.clabel', 'affinities.rlabel')"

I pass that through an R program srrecs.r (if you don’t have R installed, you’ll need to install that, and the packageskmeans like install.packages('skmeans')). This program plots the users in this vector space finding clusters using a sperical kmeans clustering algorithm (on my laptop, takes about 10 minutes with 15 clusters and 16 minutes with 50 clusters, during which R sits at about 220mb of RAM)

# looks for the files created by write_matrix in the current directory
R -f ./srrecs.r

The output of the program is a generated list of cluster-IDs, corresponding in order to the order of user-IDs inaffinities.clabel. The numbers themselves are meaningless, but people in the same cluster ID have been clustered together.

Here are the files

These are torrents of bzip2-compressed files. If you can’t use the torrents for some reason it’s pretty trivial to figure out from the URL how to get to the files directly on S3, but please try the torrents first since it saves us a few bucks. It’s S3 seeding the torrents anyway, so it’s unlikely that direct-downloading is going to go any faster or be any easier.

  • votes.dump.bz2 — A tab-separated list of:
    account_id, link_id, sr_id, direction
    
  • For your convenience, a tab-separated list of votes already reduced to percent-affinities affinities.dump.bz2, formatted:
    account_id, sr_id, affinity (scaled 0..1)
    
  • For your convenience, affinities-matrix.tar.bz2 contains the R CLUTO format matrix files affinities.cm,affinities.clabelaffinities.rlabel

And the code

  • srrecs.pigsrrecs_researchers.pig — what I used to generate and format the dumps (you probably won’t need this)
  • mr_tools.pysrrecs.py — what I used to salt/hash the user information and generate the R CLUTO-format matrix files (you probably won’t need this unless you want different information in the matrix)
  • srrecs.r — the R-code to generate the clusters

Here’s what you can experiment with

  • The code isn’t nearly useable yet. We need to turn the generated clusters into an actual set of recommendations per cluster, preferably ordered by predicted match. We probably need to do some additional post-processing per user, too. (If they gave us an affinity of 0% to /r/askreddit, we shouldn’t recommend it, even if we predicted that the rest of their cluster would like it.)
  • We need a test suite to gauge the accuracy of the results of different approaches. This could be done by dividing the data-set in and using 80% for training and 20% to see if the predictions made by that 80% match.
  • We need to get the whole process to less than two hours, because that’s how often I want to run the recommender. It’s okay to use two or three machines to accomplish that and a lot of the steps can be done in parallel. That said we might just have to accept running it less often. It needs to run end-to-end with no user-intervention, failing gracefully on error
  • It would be handy to be able to idenfity the cluster of just a single user on-the-fly after generating the clusters in bulk
  • The results need to be hooked into the reddit UI. If you’re willing to dive into the codebase, this one will be important as soon as the rest of the process is working and has a lot of room for creativity
  • We need to find the sweet spot for the number of clusters to use. Put another way, how many different types of redditors do you think there are? This could best be done using the aforementioned test-suite and a good-old-fashioned binary search.

Some notes:

  • I’m not attached to doing this in R (I don’t even know much R, it just has a handy prebaked skmeans implementation). In fact I’m not attached to my methods here at all, I just want a good end-result.
  • This is my weekend fun project, so it’s likely to move very slowly if we don’t pick up enough participation here
  • The final version will run against the whole dataset, not just the public one. So even though I can’t release the whole dataset for privacy reasons, I can run your code and a test-suite against it

——————————————————————————————-

 

I am thinking of using Rattle and using the arules package, and running it on the EC2 to get the horsepower.

How else do you think you can tackle a recommendation engine problem.

 

Ajay

 

So what's new in R 2.12.0

PoissonCDF
Image via Wikipedia

and as per http://cran.r-project.org/src/base/NEWS

the answer is plenty is new in the newR.

While you and me, were busy writing and reading blogs, or generally writing code for earning more money, or our own research- Uncle Peter D and his band of merry men have been really busy in a much more upgraded R.

————————————–

CHANGES————————-

NEW FEATURES:

    • Reading a packages's CITATION file now defaults to ASCII rather
      than Latin-1: a package with a non-ASCII CITATION file should
      declare an encoding in its DESCRIPTION file and use that encoding
      for the CITATION file.

    • difftime() now defaults to the "tzone" attribute of "POSIXlt"
      objects rather than to the current timezone as set by the default
      for the tz argument.  (Wish of PR#14182.)

    • pretty() is now generic, with new methods for "Date" and "POSIXt"
      classes (based on code contributed by Felix Andrews).

    • unique() and match() are now faster on character vectors where
      all elements are in the global CHARSXP cache and have unmarked
      encoding (ASCII).  Thanks to Matthew Dowle for suggesting
      improvements to the way the hash code is generated in unique.c.

    • The enquote() utility, in use internally, is exported now.

    • .C() and .Fortran() now map non-zero return values (other than
      NA_LOGICAL) for logical vectors to TRUE: it has been an implicit
      assumption that they are treated as true.

    • The print() methods for "glm" and "lm" objects now insert
      linebreaks in long calls in the same way that the print() methods
      for "summary.[g]lm" objects have long done.  This does change the
      layout of the examples for a number of packages, e.g. MASS.
      (PR#14250)

    • constrOptim() can now be used with method "SANN".  (PR#14245)

      It gains an argument hessian to be passed to optim(), which
      allows all the ... arguments to be intended for f() and grad().
      (PR#14071)

    • curve() now allows expr to be an object of mode "expression" as
      well as "call" and "function".

    • The "POSIX[cl]t" methods for Axis() have been replaced by a
      single method for "POSIXt".

      There are no longer separate plot() methods for "POSIX[cl]t" and
      "Date": the default method has been able to handle those classes
      for a long time.  This _inter alia_ allows a single date-time
      object to be supplied, the wish of PR#14016.

      The methods had a different default ("") for xlab.

    • Classes "POSIXct", "POSIXlt" and "difftime" have generators
      .POSIXct(), .POSIXlt() and .difftime().  Package authors are
      advised to make use of them (they are available from R 2.11.0) to
      proof against planned future changes to the classes.

      The ordering of the classes has been changed, so "POSIXt" is now
      the second class.  See the document ‘Updating packages for
      changes in R 2.12.x’ on  for
      the consequences for a handful of CRAN packages.

    • The "POSIXct" method of as.Date() allows a timezone to be
      specified (but still defaults to UTC).

    • New list2env() utility function as an inverse of
      as.list() and for fast multi-assign() to existing
      environment.  as.environment() is now generic and uses list2env()
      as list method.

    • There are several small changes to output which ‘zap’ small
      numbers, e.g. in printing quantiles of residuals in summaries
      from "lm" and "glm" fits, and in test statisics in print.anova().

    • Special names such as "dim", "names", etc, are now allowed as
      slot names of S4 classes, with "class" the only remaining
      exception.

    • File .Renviron can have architecture-specific versions such as
      .Renviron.i386 on systems with sub-architectures.

    • installed.packages() has a new argument subarch to filter on
      sub-architecture.

    • The summary() method for packageStatus() now has a separate
      print() method.

    • The default summary() method returns an object inheriting from
      class "summaryDefault" which has a separate print() method that
      calls zapsmall() for numeric/complex values.

    • The startup message now includes the platform and if used,
      sub-architecture: this is useful where different
      (sub-)architectures run on the same OS.

    • The getGraphicsEvent() mechanism now allows multiple windows to
      return graphics events, through the new functions
      setGraphicsEventHandlers(), setGraphicsEventEnv(), and
      getGraphicsEventEnv().  (Currently implemented in the windows()
      and X11() devices.)

    • tools::texi2dvi() gains an index argument, mainly for use by R
      CMD Rd2pdf.

      It avoids the use of texindy by texinfo's texi2dvi >= 1.157,
      since that does not emulate 'makeindex' well enough to avoid
      problems with special characters (such as (, {, !) in indices.

    • The ability of readLines() and scan() to re-encode inputs to
      marked UTF-8 strings on Windows since R 2.7.0 is extended to
      non-UTF-8 locales on other OSes.

    • scan() gains a fileEncoding argument to match read.table().

    • points() and lines() gain "table" methods to match plot().  (Wish
      of PR#10472.)

    • Sys.chmod() allows argument mode to be a vector, recycled along
      paths.

    • There are |, & and xor() methods for classes "octmode" and
      "hexmode", which work bitwise.

    • Environment variables R_DVIPSCMD, R_LATEXCMD, R_MAKEINDEXCMD,
      R_PDFLATEXCMD are no longer used nor set in an R session.  (With
      the move to tools::texi2dvi(), the conventional environment
      variables LATEX, MAKEINDEX and PDFLATEX will be used.
      options("dvipscmd") defaults to the value of DVIPS, then to
      "dvips".)

    • New function isatty() to see if terminal connections are
      redirected.

    • summaryRprof() returns the sampling interval in component
      sample.interval and only returns in by.self data for functions
      with non-zero self times.

    • print(x) and str(x) now indicate if an empty list x is named.

    • install.packages() and remove.packages() with lib unspecified and
      multiple libraries in .libPaths() inform the user of the library
      location used with a message rather than a warning.

    • There is limited support for multiple compressed streams on a
      file: all of [bgx]zfile() allow streams to be appended to an
      existing file, but bzfile() reads only the first stream.

    • Function person() in package utils now uses a given/family scheme
      in preference to first/middle/last, is vectorized to handle an
      arbitrary number of persons, and gains a role argument to specify
      person roles using a controlled vocabulary (the MARC relator
      terms).

    • Package utils adds a new "bibentry" class for representing and
      manipulating bibliographic information in enhanced BibTeX style,
      unifying and enhancing the previously existing mechanisms.

    • A bibstyle() function has been added to the tools package with
      default JSS style for rendering "bibentry" objects, and a
      mechanism for registering other rendering styles.

    • Several aspects of the display of text help are now customizable
      using the new Rd2txt_options() function.
      options("help_text_width") is no longer used.

    • Added \href tag to the Rd format, to allow hyperlinks to URLs
      without displaying the full URL.

    • Added \newcommand and \renewcommand tags to the Rd format, to
      allow user-defined macros.

    • New toRd() generic in the tools package to convert objects to
      fragments of Rd code, and added "fragment" argument to Rd2txt(),
      Rd2HTML(), and Rd2latex() to support it.

    • Directory R_HOME/share/texmf now follows the TDS conventions, so
      can be set as a texmf tree (‘root directory’ in MiKTeX parlance).

    • S3 generic functions now use correct S4 inheritance when
      dispatching on an S4 object.  See ?Methods, section on “Methods
      for S3 Generic Functions” for recommendations and details.

    • format.pval() gains a ... argument to pass arguments such as
      nsmall to format().  (Wish of PR#9574)

    • legend() supports title.adj.  (Wish of PR#13415)

    • Added support for subsetting "raster" objects, plus assigning to
      a subset, conversion to a matrix (of colour strings), and
      comparisons (== and !=).

    • Added a new parseLatex() function (and related functions
      deparseLatex() and latexToUtf8()) to support conversion of
      bibliographic entries for display in R.

    • Text rendering of \itemize in help uses a Unicode bullet in UTF-8
      and most single-byte Windows locales.

    • Added support for polygons with holes to the graphics engine.
      This is implemented for the pdf(), postscript(),
      x11(type="cairo"), windows(), and quartz() devices (and
      associated raster formats), but not for x11(type="Xlib") or
      xfig() or pictex().  The user-level interface is the polypath()
      function in graphics and grid.path() in grid.

    • File NEWS is now generated at installation with a slightly
      different format: it will be in UTF-8 on platforms using UTF-8,
      and otherwise in ASCII.  There is also a PDF version, NEWS.pdf,
      installed at the top-level of the R distribution.

    • kmeans(x, 1) now works.  Further, kmeans now returns between and
      total sum of squares.

    • arrayInd() and which() gain an argument useNames.  For arrayInd,
      the default is now false, for speed reasons.

    • As is done for closures, the default print method for the formula
      class now displays the associated environment if it is not the
      global environment.

    • A new facility has been added for inserting code into a package
      without re-installing it, to facilitate testing changes which can
      be selectively added and backed out.  See ?insertSource.

    • New function readRenviron to (re-)read files in the format of
      ~/.Renviron and Renviron.site.

    • require() will now return FALSE (and not fail) if loading the
      package or one of its dependencies fails.

    • aperm() now allows argument perm to be a character vector when
      the array has named dimnames (as the results of table() calls
      do).  Similarly, array() allows MARGIN to be a character vector.
      (Based on suggestions of Michael Lachmann.)

    • Package utils now exports and documents functions
      aspell_package_Rd_files() and aspell_package_vignettes() for
      spell checking package Rd files and vignettes using Aspell,
      Ispell or Hunspell.

    • Package news can now be given in Rd format, and news() prefers
      these inst/NEWS.Rd files to old-style plain text NEWS or
      inst/NEWS files.

    • New simple function packageVersion().

    • The PCRE library has been updated to version 8.10.

    • The standard Unix-alike terminal interface declares its name to
      readline as 'R', so that can be used for conditional sections in
      ~/.inputrc files.

    • ‘Writing R Extensions’ now stresses that the standard sections in
      .Rd files (other than \alias, \keyword and \note) are intended to
      be unique, and the conversion tools now drop duplicates with a
      warning.

      The .Rd conversion tools also warn about an unrecognized type in
      a \docType section.

    • ecdf() objects now have a quantile() method.

    • format() methods for date-time objects now attempt to make use of
      a "tzone" attribute with "%Z" and "%z" formats, but it is not
      always possible.  (Wish of PR#14358.)

    • tools::texi2dvi(file, clean = TRUE) now works in more cases (e.g.
      where emulation is used and when file is not in the current
      directory).

    • New function droplevels() to remove unused factor levels.

    • system(command, intern = TRUE) now gives an error on a Unix-alike
      (as well as on Windows) if command cannot be run.  It reports a
      non-success exit status from running command as a warning.

      On a Unix-alike an attempt is made to return the actual exit
      status of the command in system(intern = FALSE): previously this
      had been system-dependent but on POSIX-compliant systems the
      value return was 256 times the status.

    • system() has a new argument ignore.stdout which can be used to
      (portably) ignore standard output.

    • system(intern = TRUE) and pipe() connections are guaranteed to be
      avaliable on all builds of R.

    • Sys.which() has been altered to return "" if the command is not
      found (even on Solaris).

    • A facility for defining reference-based S4 classes (in the OOP
      style of Java, C++, etc.) has been added experimentally to
      package methods; see ?ReferenceClasses.

    • The predict method for "loess" fits gains an na.action argument
      which defaults to na.pass rather than the previous default of
      na.omit.

      Predictions from "loess" fits are now named from the row names of
      newdata.

    • Parsing errors detected during Sweave() processing will now be
      reported referencing their original location in the source file.

    • New adjustcolor() utility, e.g., for simple translucent color
      schemes.

    • qr() now has a trivial lm method with a simple (fast) validity
      check.

    • An experimental new programming model has been added to package
      methods for reference (OOP-style) classes and methods.  See
      ?ReferenceClasses.

    • bzip2 has been updated to version 1.0.6 (bug-fix release).
      --with-system-bzlib now requires at least version 1.0.6.

    • R now provides jss.cls and jss.bst (the class and bib style file
      for the Journal of Statistical Software) as well as RJournal.bib
      and Rnews.bib, and R CMD ensures that the .bst and .bib files are
      found by BibTeX.

    • Functions using the TAR environment variable no longer quote the
      value when making system calls.  This allows values such as tar
      --force-local, but does require additional quotes in, e.g., TAR =
      "'/path with spaces/mytar'".

  DEPRECATED & DEFUNCT:

    • Supplying the parser with a character string containing both
      octal/hex and Unicode escapes is now an error.

    • File extension .C for C++ code files in packages is now defunct.

    • R CMD check no longer supports configuration files containing
      Perl configuration variables: use the environment variables
      documented in ‘R Internals’ instead.

    • The save argument of require() now defaults to FALSE and save =
      TRUE is now deprecated.  (This facility is very rarely actually
      used, and was superseded by the Depends field of the DESCRIPTION
      file long ago.)

    • R CMD check --no-latex is deprecated in favour of --no-manual.

    • R CMD Sd2Rd is formally deprecated and will be removed in R
      2.13.0.

  PACKAGE INSTALLATION:

    • install.packages() has a new argument libs_only to optionally
      pass --libs-only to R CMD INSTALL and works analogously for
      Windows binary installs (to add support for 64- or 32-bit
      Windows).

    • When sub-architectures are in use, the installed architectures
      are recorded in the Archs field of the DESCRIPTION file.  There
      is a new default filter, "subarch", in available.packages() to
      make use of this.

      Code is compiled in a copy of the src directory when a package is
      installed for more than one sub-architecture: this avoid problems
      with cleaning the sources between building sub-architectures.

    • R CMD INSTALL --libs-only no longer overrides the setting of
      locking, so a previous version of the package will be restored
      unless --no-lock is specified.

  UTILITIES:

    • R CMD Rprof|build|check are now based on R rather than Perl
      scripts.  The only remaining Perl scripts are the deprecated R
      CMD Sd2Rd and install-info.pl (used only if install-info is not
      found) as well as some maintainer-mode-only scripts.

      *NB:* because these have been completely rewritten, users should
      not expect undocumented details of previous implementations to
      have been duplicated.

      R CMD no longer manipulates the environment variables PERL5LIB
      and PERLLIB.

    • R CMD check has a new argument --extra-arch to confine tests to
      those needed to check an additional sub-architecture.

      Its check for “Subdirectory 'inst' contains no files” is more
      thorough: it looks for files, and warns if there are only empty
      directories.

      Environment variables such as R_LIBS and those used for
      customization can be set for the duration of checking _via_ a
      file ~/.R/check.Renviron (in the format used by .Renviron, and
      with sub-architecture specific versions such as
      ~/.R/check.Renviron.i386 taking precedence).

      There are new options --multiarch to check the package under all
      of the installed sub-architectures and --no-multiarch to confine
      checking to the sub-architecture under which check is invoked.
      If neither option is supplied, a test is done of installed
      sub-architectures and all those which can be run on the current
      OS are used.

      Unless multiple sub-architectures are selected, the install done
      by check for testing purposes is only of the current
      sub-architecture (_via_ R CMD INSTALL --no-multiarch).

      It will skip the check for non-ascii characters in code or data
      if the environment variables _R_CHECK_ASCII_CODE_ or
      _R_CHECK_ASCII_DATA_ are respectively set to FALSE.  (Suggestion
      of Vince Carey.)

    • R CMD build no longer creates an INDEX file (R CMD INSTALL does
      so), and --force removes (rather than overwrites) an existing
      INDEX file.

      It supports a file ~/.R/build.Renviron analogously to check.

      It now runs build-time \Sexpr expressions in help files.

    • R CMD Rd2dvi makes use of tools::texi2dvi() to process the
      package manual.  It is now implemented entirely in R (rather than
      partially as a shell script).

    • R CMD Rprof now uses utils::summaryRprof() rather than Perl.  It
      has new arguments to select one of the tables and to limit the
      number of entries printed.

    • R CMD Sweave now runs R with --vanilla so the environment setting
      of R_LIBS will always be used.

  C-LEVEL FACILITIES:

    • lang5() and lang6() (in addition to pre-existing lang[1-4]())
      convenience functions for easier construction of eval() calls.
      If you have your own definition, do wrap it inside #ifndef lang5
      .... #endif to keep it working with old and new R.

    • Header R.h now includes only the C headers it itself needs, hence
      no longer includes errno.h.  (This helps avoid problems when it
      is included from C++ source files.)

    • Headers Rinternals.h and R_ext/Print.h include the C++ versions
      of stdio.h and stdarg.h respectively if included from a C++
      source file.

  INSTALLATION:

    • A C99 compiler is now required, and more C99 language features
      will be used in the R sources.

    • Tcl/Tk >= 8.4 is now required (increased from 8.3).

    • System functions access, chdir and getcwd are now essential to
      configure R.  (In practice they have been required for some
      time.)

    • make check compares the output of the examples from several of
      the base packages to reference output rather than the previous
      output (if any).  Expect some differences due to differences in
      floating-point computations between platforms.

    • File NEWS is no longer in the sources, but generated as part of
      the installation.  The primary source for changes is now
      doc/NEWS.Rd.

    • The popen system call is now required to build R.  This ensures
      the availability of system(intern = TRUE), pipe() connections and
      printing from postscript().

    • The pkg-config file libR.pc now also works when R is installed
      using a sub-architecture.

    • R has always required a BLAS that conforms to IE60559 arithmetic,
      but after discovery of more real-world problems caused by a BLAS
      that did not, this is tested more thoroughly in this version.

  BUG FIXES:

    • Calls to selectMethod() by default no longer cache inherited
      methods.  This could previously corrupt methods used by as().

    • The densities of non-central chi-squared are now more accurate in
      some cases in the extreme tails, e.g. dchisq(2000, 2, 1000), as a
      series expansion was truncated too early.  (PR#14105)

    • pt() is more accurate in the left tail for ncp large, e.g.
      pt(-1000, 3, 200).  (PR#14069)

    • The default C function (R_binary) for binary ops now sets the S4
      bit in the result if either argument is an S4 object.  (PR#13209)

    • source(echo=TRUE) failed to echo comments that followed the last
      statement in a file.

    • S4 classes that contained one of "matrix", "array" or "ts" and
      also another class now accept superclass objects in new().  Also
      fixes failure to call validObject() for these classes.

    • Conditional inheritance defined by argument test in
      methods::setIs() will no longer be used in S4 method selection
      (caching these methods could give incorrect results).  See
      ?setIs.

    • The signature of an implicit generic is now used by setGeneric()
      when that does not use a definition nor explicitly set a
      signature.

    • A bug in callNextMethod() for some examples with "..." in the
      arguments has been fixed.  See file
      src/library/methods/tests/nextWithDots.R in the sources.

    • match(x, table) (and hence %in%) now treat "POSIXlt" consistently
      with, e.g., "POSIXct".

    • Built-in code dealing with environments (get(), assign(),
      parent.env(), is.environment() and others) now behave
      consistently to recognize S4 subclasses; is.name() also
      recognizes subclasses.

    • The abs.tol control parameter to nlminb() now defaults to 0.0 to
      avoid false declarations of convergence in objective functions
      that may go negative.

    • The standard Unix-alike termination dialog to ask whether to save
      the workspace takes a EOF response as n to avoid problems with a
      damaged terminal connection.  (PR#14332)

    • Added warn.unused argument to hist.default() to allow suppression
      of spurious warnings about graphical parameters used with
      plot=FALSE.  (PR#14341)

    • predict.lm(), summary.lm(), and indeed lm() itself had issues
      with residual DF in zero-weighted cases (the latter two only in
      connection with empty models). (Thanks to Bill Dunlap for
      spotting the predict() case.)

    • aperm() treated resize = NA as resize = TRUE.

    • constrOptim() now has an improved convergence criterion, notably
      for cases where the minimum was (very close to) zero; further,
      other tweaks inspired from code proposals by Ravi Varadhan.

    • Rendering of S3 and S4 methods in man pages has been corrected
      and made consistent across output formats.

    • Simple markup is now allowed in \title sections in .Rd files.

    • The behaviour of as.logical() on factors (to use the levels) was
      lost in R 2.6.0 and has been restored.

    • prompt() did not backquote some default arguments in the \usage
      section.  (Reported by Claudia Beleites.)

    • writeBin() disallows attempts to write 2GB or more in a single
      call. (PR#14362)

    • new() and getClass() will now work if Class is a subclass of
      "classRepresentation" and should also be faster in typical calls.

    • The summary() method for data frames makes a better job of names
      containing characters invalid in the current locale.

    • [[ sub-assignment for factors could create an invalid factor
      (reported by Bill Dunlap).

    • Negate(f) would not evaluate argument f until first use of
      returned function (reported by Olaf Mersmann).

    • quietly=FALSE is now also an optional argument of library(), and
      consequently, quietly is now propagated also for loading
      dependent packages, e.g., in require(*, quietly=TRUE).

    • If the loop variable in a for loop was deleted, it would be
      recreated as a global variable.  (Reported by Radford Neal; the
      fix includes his optimizations as well.)

    • Task callbacks could report the wrong expression when the task
      involved parsing new code. (PR#14368)

    • getNamespaceVersion() failed; this was an accidental change in
      2.11.0. (PR#14374)

    • identical() returned FALSE for external pointer objects even when
      the pointer addresses were the same.

    • L$a@x[] <- val did not duplicate in a case it should have.

    • tempfile() now always gives a random file name (even if the
      directory is specified) when called directly after startup and
      before the R RNG had been used.  (PR#14381)

    • quantile(type=6) behaved inconsistently.  (PR#14383)

    • backSpline(.) behaved incorrectly when the knot sequence was
      decreasing.  (PR#14386)

    • The reference BLAS included in R was assuming that 0*x and x*0
      were always zero (whereas they could be NA or NaN in IEC 60559
      arithmetic).  This was seen in results from tcrossprod, and for
      example that log(0) %*% 0 gave 0.

    • The calculation of whether text was completely outside the device
      region (in which case, you draw nothing) was wrong for screen
      devices (which have [0, 0] at top-left).  The symptom was (long)
      text disappearing when resizing a screen window (to make it
      smaller).  (PR#14391)

    • model.frame(drop.unused.levels = TRUE) did not take into account
      NA values of factors when deciding to drop levels. (PR#14393)

    • library.dynam.unload required an absolute path for libpath.
      (PR#14385)

      Both library() and loadNamespace() now record absolute paths for
      use by searchpaths() and getNamespaceInfo(ns, "path").

    • The self-starting model NLSstClosestX failed if some deviation
      was exactly zero.  (PR#14384)

    • X11(type = "cairo") (and other devices such as png using
      cairographics) and which use Pango font selection now work around
      a bug in Pango when very small fonts (those with sizes between 0
      and 1 in Pango's internal units) are requested.  (PR#14369)

    • Added workaround for the font problem with X11(type = "cairo")
      and similar on Mac OS X whereby italic and bold styles were
      interchanged.  (PR#13463 amongst many other reports.)

    • source(chdir = TRUE) failed to reset the working directory if it
      could not be determined - that is now an error.

    • Fix for crash of example(rasterImage) on x11(type="Xlib").

    • Force Quartz to bring the on-screen display up-to-date
      immediately before the snapshot is taken by grid.cap() in the
      Cocoa implementation. (PR#14260)

    • model.frame had an unstated 500 byte limit on variable names.
      (Example reported by Terry Therneau.)

    • The 256-byte limit on names is now documented.    • Subassignment by [, [[ or $ on an expression object with value
      NULL coerced the object to a list.

 

 

John Sall sets JMP 9 free to tango with R

 

Diagnostic graphs produced by plot.lm() functi...
Image via Wikipedia

 

John Sall, founder SAS AND JMP , has released the latest blockbuster edition of flagship of JMP 9 (JMP Stands for John’s Macintosh Program).

To kill all birds with one software, it is integrated with R and SAS, and the brochure frankly lists all the qualities. Why am I excited for JMP 9 integration with R and with SAS- well it integrates bigger datasets manipulation (thanks to SAS) with R’s superb library of statistical packages and a great statistical GUI (JMP). This makes JMP the latest software apart from SAS/IML, Rapid Miner,Knime, Oracle Data Miner to showcase it’s R integration (without getting into the GPL compliance need for showing source code– it does not ship R- and advises you to just freely download R). I am sure Peter Dalgaard, and Frankie Harell are all overjoyed that R Base and Hmisc packages would be used by fellow statisticians  and students for JMP- which after all is made in the neighborhood state of North Carolina.

Best of all a JMP 30 day trial is free- so no money lost if you download JMP 9 (and no they dont ask for your credit card number, or do they- but they do have a huuuuuuge form to register before you download. Still JMP 9 the software itself is more thoughtfully designed than the email-prospect-leads-form and the extra functionality in the free 30 day trial is worth it.

Also see “New Features  in JMP 9  http://www.jmp.com/software/jmp9/pdf/new_features.pdf

which has this regarding R.

Working with R

R is a programming language and software environment for statistical computing and graphics. JMP now  supports a set of JSL functions to access R. The JSL functions provide the following options:

• open and close a connection between JMP and R

• exchange data between JMP and R

•submit R code for execution

•display graphics produced by R

JMP and R each have their own sets of computational methods.

R has some methods that JMP does not have. Using JSL functions, you can connect to R and use these R computational methods from within JMP.

Textual output and error messages from R appear in the log window.R must be installed on the same computer as JMP.

JMP is not distributed with a copy of R. You can download R from the Comprehensive R Archive Network Web site:http://cran.r-project.org

Because JMP is supported as both a 32-bit and a 64-bit Windows application, you must install the corresponding 32-bit or 64-bit version of R.

For details, see the Scripting Guide book.

and the download trial page ( search optimized URL) –

http://www.sas.com/apps/demosdownloads/jmptrial9_PROD__sysdep.jsp?packageID=000717&jmpflag=Y

In related news (Richest man in North Carolina also ranks nationally(charlotte.news14.com) , Jim Goodnight is now just as rich as Mark Zuckenberg, creator of Facebook-

though probably they are not creating a movie on Jim yet (imagine a movie titled “The Statistical Software” -not just the same dude feel as “The Social Network”)

See John’s latest interview :

The People Behind the Software: John Sall

http://blogs.sas.com/jmp/index.php?/archives/352-The-People-Behind-the-Software-John-Sall.html

Interview John Sall Founder JMP/SAS Institute

https://decisionstats.com/2009/07/28/interview-john-sall-jmp/

SAS Early Days

https://decisionstats.com/2010/06/02/sas-early-days/