From maechler at stat.math.ethz.ch  Tue Apr  1 11:48:02 1997
From: maechler at stat.math.ethz.ch (Martin Maechler)
Date: Tue, 1 Apr 97 11:48:02 +0200
Subject: "R-announce", "R-help", "R-devel" : 3 mailing lists for R
Message-ID: <9704010948.AA00412@>


Upon proposal by Robert Gentleman,
and given the ``immediate'' release of R 0.50  beta (instead of alpha),
I have created three mailing lists concerned with R
where the 2nd one, "R-help", is the replacement for the current "R-testers".
For a while, "r-testers" will be kept as synonymous to "r-help".


The 3 mailing lists are

1) R-announce	: Only announcement of new versions / important patches
2) R-help	: Questions / Answers about ("released versions of") R
3) R-devel	: The alpha-/ pre-testers list of "R-hackers".
		  People who get the newest release, try out patches,....
		  (maybe not much more than the R 0.50 prerelease testers)

where   1) is gatewayed to 2)  i.e.  all "R-announce" is forwarded to "R-help".

The intent is that "R-announce" would have less than one message per day,
typically rather only a few messages per month.

Note that I am sending this to  "R-announce" and 
everyone who has been on the "R-testers" list is getting this e-mail
since everything that is sent to "r-announce" is automatically forwarded to
"r-help".

- -------
AGAIN:  For most of you nothing changes, you'll get all the announcements
	and everything from "R-help".

However, if you are only interested in important announcements and new
releases, you should 
	- unsubscribe from "R-help"
	-   subscribe  to  "R-announce"
((by sending	unsubscribe  to     R-help-request at stat.math.ethz.ch
		  subscribe  to R-announce-request at stat.math.ethz.ch	
  in the message "body", not as "subject ..))	

PS. Yes, this is the first post to any of these lists,
    so there may be problems, even though I don't hope so.

PPS. In spite of the date, this is no "April's fool" joke....

Martin Maechler <maechler at stat.math.ethz.ch>		 <><
Seminar fuer Statistik, SOL F5
ETH (Federal Inst. Technology)	8092 Zurich	 SWITZERLAND
phone: x-41-1-632-3408		fax: ...-1086
http://www.stat.math.ethz.ch/~maechler/


From Kurt.Hornik at ci.tuwien.ac.at  Wed Apr 23 08:40:28 1997
From: Kurt.Hornik at ci.tuwien.ac.at (Kurt Hornik)
Date: Wed, 23 Apr 1997 08:40:28 +0200
Subject: ANNOUNCE:  CRAN
Message-ID: <199704230640.IAA03837@aragorn.ci.tuwien.ac.at>

This is the first announcement of the

		   Comprehensive R Archive Network 
			        (CRAN)

CRAN is a collection of sites which carry identical material, consisting
of the R&R R distribution(s), the contributed extensions, documentation
for R, and binaries.

The CRAN master site can be found at the URL

	ftp://ftp.ci.tuwien.ac.at/pub/R		(Austria)

and is currently being mirrored daily at

	http://lib.stat.cmu.edu/R/CRAN		(U.S.A.)
	ftp://franz.stat.wisc.edu/pub/R		(U.S.A.)
	ftp://ftp.stat.math.ethz.ch/R-CRAN	(Switzerland)

This list should grow within soon.  If you want to become an official
CRAN mirror, please send me a note (Kurt.Hornik at ci.tuwien.ac.at).

Please use the CRAN site closest to you to reduce network load.

The structure of the CRAN tree is as follows.

	src/base		# Source distribution
	src/contrib		# Source for extensions
	doc/			# Documentation
	bin/<platform>		# Binaries

`src/base' contains the official R source distribution as provided by
Ross Ihaka and Robert Gentleman.

`src/contrib' contains code for extension packages.  Currently, there
are acepack, bootstrap, ctest, date, e1071, fracdiff, gee, jpn, oz,
snns, splines, and survival4.  Look at the INDEX file in this directory
for more specific information.  More packages are expected for the near
future.

`bin' is for prebuilt R binaries (the base distribution and extensions),
grouped according to platforms.  Currently, there are only experimental
packages for Debian GNU/Linux.  I hope that `.tar.gz' files with
contents relative to an installation tree (e.g. `bin/', `lib/R/', and
`man/man1/R.1') can be made available soon for all major supported Unix
platforms.

`doc' is for additional documentation and information on R.

In the short run, the process of `submitting' to CRAN is very simple:
upload to 

	ftp://ftp.ci.tuwien.ac.at/incoming

and drop me a note (Kurt.Hornik at ci.tuwien.ac.at).  Please indicate the
copyright situation (GPL, ...) in your submission.

In the long run, there will be a form to fill in, and some requirement
of authentication (PGPish, ...), and submission could maybe be done via
WWW.  I am open to suggestions here.

*****************************************************************************
*          Kurt Hornik         *                                            *
*  Dept of Statistics TU Wien  * tel:    +43 (1) 58801-4542                 *
*  Wiedner Hauptstr 8-10/1071  * fax:    +43 (1) 504-1498                   *
*          A-1040 Wien         * email:  Kurt.Hornik at ci.tuwien.ac.at        *
*            Austria           * WWW:    http://www.ci.tuwien.ac.at/~hornik *
*****************************************************************************
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From ihaka at stat.auckland.ac.nz  Wed Apr 23 11:25:23 1997
From: ihaka at stat.auckland.ac.nz (Ross Ihaka)
Date: Wed, 23 Apr 1997 21:25:23 +1200 (NZST)
Subject: Version 0.49 Released
Message-ID: <199704230925.VAA24453@stat1.stat.auckland.ac.nz>

The newest version of R for Unix (version 0.49) is now available
(or soon will be) from the following sites.

    NORTH AMERICA:
	http://lib.stat.cmu.edu/R/Alpha

    EUROPE:
        ftp://ftp.stat.math.ethz.ch/R/
        ftp://statlab.uni-heidelberg.de/pub/mirrors/auckland/R/

    JAPAN:
        ftp://ftp.u-aizu.ac.jp/pub/lang/R/

    NEW ZEALAND:
        ftp://stat.auckland.ac.nz/pub/R/

Please obtain a copy from site close to you.  Note that New Zealand
is not close to anywhere other than itself :-).

This version adds considerable functionality and (we hope) stability
to R.  Most notably, the object system is now very close to that
of S and we now have a fairly full implementation of complex
arithmetic.

The jump in version number (from 0.16) relects the fact that we
feel that this version of R represents quite a jump toward what we
hope will be in our eventual 1.0 release.  However the version is
numbered 0.49 rather than the 0.50 we touted because it falls a
little short of what we really want for 0.50.

Immediate development of R will focus on creating a coherent way
of loading and unloading "libraries" and creating a good framework
for documentation (can you say "SGML" - I knew you could).

A (partial) list of changes from 0.16.1 follows.  A list of known
problems is kept in the file "TASKS" in the distribution and a list
of problems we think are solved is in "TASKS.OLD"

	R + R

-------------------------------------------------------------------

		  CHANGES IN VERSION R VERSION 0.49 ALPHA

NEW FEATURES

 o  The ``object'' system has been changed substantially.  The
    behavior of both "UseMethod" and "NextMethod" should match that
    in S.  Group methods for "Math", "Ops" and "Summary" are
    available.

 o  Complex arithmetic is now implemented.  Many mathematical
    functions are now defined for complex arguments (e.g. sqrt,
    exp, log, sin, cos, tan, asin, acos, atan).  There is no
    complex gamma function or log gamma function yet.  The summary
    functions "mean", "sum", "prod", "cumsum" and "cumprod" work
    correctly when (some of) their arguments are complex.  Other
    functions such as "solve" are not "complex aware" yet, but do
    print warning messages about coercion of complex values
    to real by the dropping of imaginary parts.
    S and R do not return identical results in all cases:
	S> atan(tan(1i))
	[1] 0-1i
	R> atan(tan(1i))
	[1] 0+1i
    [ Is this just a difference on the branch cut boundary? ]

 o  The full set of S graphics symbols is now available with pch=0:18.
    In addition, there is a special set of R plotting symbols which
    can be obtained with pch=29:25.
	pch=19  solid circle
	pch=20	bullet
	pch=21	circle
	pch=22	square
	pch=23	diamond
	pch=24	triangle point-up
	pch=25	triangle point down
    The symbols 21:25 can be colored and filled with different colors.
    For example, the expression
	points(x, y, pch=21, col="red", bg="yellow")
    will plot the points using a symbol consisting of a red circle with
    a yellow interior.

 o  There is a new "family" argument to the postscript graphics driver
    which can be set to any of "AvantGarde", "Bookman", "Courier",
    "Helvetica", "Helvetica-Narrow", "NewCenturySchoolbook", "Palatino"
    or "Times".  In addition, setting font=5 will cause the "Symbol"
    family to be used.  This is still experimental and it is hard to
    see it being useful without some sort of math capability.
    [ Such a facility is "on the drawing board". ]

 o  The graphics parameter "las" is now implemented and can be used
    to rotate axis labels.  E.g.  plot(1:10, las=1) .

 o  The hyperbolic and inverse hyperbolic functions cosh, sinh, tanh,
    acosh, asinh and atanh are now implemented for both real and complex
    arguments.  (Q: are the underlying functions available on all
    platforms, or do we need compatibility fixes?)

 o  "log" has changed so that it will accept an optional "base" argument.
    "log2" and "log10" are implemented this way.

 o  "atan" can now either be invoked as atan(x) or atan(x,y).

 o  The behavior of "fft" has been modified to match that of S
    (i.e.  it returns a complex value.  There is also a function
    "mvfft" which performs a "vector transform" when passed a matrix
    (i.e.  it applies the fft to each column, rather than doing a
    2d spatial transform).

 o  A new functon "polyroot" can be used to find the roots of
    polynomials with (real or) complex coefficients.

 o  Vectors and lists are now "stretchy".  This means that the
    following is legal
	x <- 1:10
	x[20] <- 12
    [ Note that there is a bug in S.  When you try this kind of
    extension - the "dim" and "dimname" attributes are not dropped,
    leading to "invisible" elements in the result. ]

 o  Symbolic differention is now available using the functions "D"
    and "deriv".  The results are slightly difference in appearance
    from those of S (which tends to put in a few too many parentheses),
    but should provide identical semantics.  To see the nature of the
    difference, try the expression
	D(expression(tan(x)/x^2),"x")
    in both systems.
    These functions are implemented as internal code in R (what's the
    point in having a nice little underlying lisp if you don't use it
    for obvious list processing applications?).

 o  A new function "grep" has been implemented.  It performs regular
    expression matching based on POSIX 1003.3.  The function uses the
    "regex" library written by Henry Spencer (the same one that Perl
    uses).  Grep is now used to provide a pattern matching facility
    in "objects" and "ls".  In addition, there are functions "sub"
    and "gsub" which operate the same way as those in "nawk".  Note
    that "\" must be escaped to get it into a string, so if you want
    a literal "\" you must type "\\\\" :-(.

 o  A new "methods" function written by Martin Maechler replaces the
    older less sophisticated one.  This has also been converted to use
    the new "grep" function.

 o  A new version of model.frame from Thomas Lumley is included.

 o  Factors and ordered factors are now "objects" with class attributes
    which match those in S.  This change is primarily so that applications
    written for S will work in R.  The underlying implementation for
    factors and ordered factors still uses special underlying types.

 o  R will now do conditioning plots as described in the S "Models" book.
    Some thought is going into "doing trellis".

 o  Thomas Lumley's "require" and "provide" functions for library
    organization added.

 o  There is a new graphical parameter "gamma" which is designed to
    let users apply a ``gamma'' correction for their graphics
    displays.  Most monitors produce a color intensity which is
    related to voltage by the equation
			intensity = voltage ^ gamma
    with gamma about 2.5 for most PC monitors.  A typical symptom
    of this non-linearity is that a colorwheel produced by
			piechart(rep(1,48), col=48)
    shows a marked over representation of the red, green and blue
    and blue primaries.  If this is the case try
			par(gamma=1/2.5)
    and redraw the color wheel.  Vary gamma till you have a "nice"
    spectrum.  This is experimental and feedback would be welcome.
    (If this is useful we will do the same for postscript).


BUG FIXES & ENHANCEMENTS

 o  The internals of the postscript device drivers have been rewritten
    in preparation for "doing equations".  The Adobe font metrics
    are no longer processed during the build process.  The original
    files are now processed directly by the driver.  The postscript
    emitted by the drivers should conform to the Adobe 3.0 standard.

 o  Typos and logic errors in "crossprod" pointed out by Arne Kovac
    have been fixed.

 o  "round" now works using IEEE rounding on platforms which support it.
    E.g.
	> round(.5 + -3:7)
	 [1] -2 -2  0  0  2  2  4  4  6  6  8

 o  A change to the PostScript graphics device driver change should
    stop plots from rotating unexpectedly when viewed with ghostview
    and other postscript viewers.

 o  Patches from Kurt Hornik for "structure" and "help" have been applied.

 o  "names" applied to 1-d arrays now does the "right thing" i.e. it
    returns the first component of the "dimnames" attribute rather than
    NULL.  Subsetting a 1-d array as a vector will produce a "names"
    attribute on the result.
    
 o  "atan" will now accept either 1 or 2 arguments ("atan2" still exists).

 o  "nchar" and "format" preserve "dim" and "dimname" attributes where
    possible.

 o  There were problems with arguments in "switch".  These have been
    fixed.  There appears to be a bug in S with the expression
		z <- switch(1, a=, b=10, 20)
		mode(z)
    They appear to return a "missing argument".  We die with an error
    message.  What is the right thing to do here?

 o  Problems deparsing arguments of the form "a"= and a=, have been fixed.

 o  The default graphics line width is now 1 rather than 0.

 o  "as.is" problem in "read.table" pointed out by Peter Dalgaard fixed.

 o  paste(list(character(0),""), 1:2) no longer causes a segfault.
    "as.character" applied to a list now returns the vector obtained
    by deparsing each element of the list.

 o  "attach" now attaches objects at pos=2 when directed.

 o  Memory usage was measured in 1000 byte chunks with the -v flag.
    This has been changed to 1024 byte chunks.

 o  Dependency on the "OSF sprintf bug" has been removed.

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From ihaka at stat.auckland.ac.nz  Wed Apr 23 11:36:33 1997
From: ihaka at stat.auckland.ac.nz (Ross Ihaka)
Date: Wed, 23 Apr 1997 21:36:33 +1200 (NZST)
Subject: Version 0.49 Addendum
Message-ID: <199704230936.VAA24493@stat1.stat.auckland.ac.nz>

I should mention that this version of R has been verified to configure
and compile on the following platforms:

        PLATFORM                        COMPILER

        alpha-dec-osf3.2                cc
        hppa1.1-hp-hpux9.07             gcc or c89
        i386-unknown-freebsd2.1.5       gcc
        i686-unknown-linux              gcc
        mips-sgi-irix6.2                cc
        sparc-sun-solaris2.5.1          gcc
        sparc-sun-sunos4.1.4            gcc

We'd be interested in any other successes.
	Ross
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From Kurt.Hornik at ci.tuwien.ac.at  Wed Apr 23 17:13:22 1997
From: Kurt.Hornik at ci.tuwien.ac.at (Kurt Hornik)
Date: Wed, 23 Apr 1997 17:13:22 +0200
Subject: R-FAQ v0.1-0
Message-ID: <199704231513.RAA14629@aragorn.ci.tuwien.ac.at>

A much updated R FAQ is now available at the URL

	http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

The plain text version is appended below.

-k

*****************************************************************************
*          Kurt Hornik         *                                            *
*  Dept of Statistics TU Wien  * tel:    +43 (1) 58801-4542                 *
*  Wiedner Hauptstr 8-10/1071  * fax:    +43 (1) 504-1498                   *
*          A-1040 Wien         * email:  Kurt.Hornik at ci.tuwien.ac.at        *
*            Austria           * WWW:    http://www.ci.tuwien.ac.at/~hornik *
*****************************************************************************

*****************************************************************************
  R FAQ
  Kurt Hornik
  v0.1-0, 1997/04/23

  This document contains answers to some of the most frequently asked
  questions about R.  Feedback is welcome.
  ______________________________________________________________________

  Table of Contents:

  1. Introduction

     1.1 Legalese
     1.2 Obtaining this Document
     1.3 Notation
     1.4 Feedback

  2. R Basics

     2.1 What Is R?
     2.2 What Machines Does R Run on?
     2.3 What Is the Current Version of R?
     2.4 How Can R Be Obtained?
     2.5 How Can R Be Installed?
        2.5.1 How Can R Be Installed (Unix)
        2.5.2 How Can R Be Installed (Windows)
        2.5.3 How Can R Be Installed (Macintosh)
     2.6 Are there Unix Binaries for R?
     2.7 Which Documentation Exists for R?
     2.8 Which Mailing Lists Exist for R?
     2.9 What is CRAN?

  3. R and S

     3.1 What Is S?
     3.2 What Is S-PLUS?
     3.3 What Are the Differences between R and S?

  4. R Add-On Packages

     4.1 Which Add-on Packages Exist for R?
     4.2 How Can Add-on Packages Be Installed?
     4.3 How Can Add-on Packages Be Used?
     4.4 How Can I Contribute to R?

  5. R and Emacs

     5.1 Is there Emacs Support for R?
     5.2 Should I Run R from Within Emacs?

  6. R Miscellania

     6.1 How Can I Read a Large Data Set into R?
     6.2 Why Can't R Source a `Correct' File?
     6.3 How Can I Set Components of a List to NULL?
     6.4 How Can I Save My Workspace?
     6.5 How Can I Clean Up My Workspace?

  7. Acknowledgments

  ______________________________________________________________________

  11..

  This document contains answers to some of the most frequently asked
  questions about R.

  11..11..

  This document is free software; you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation; either version 2, or (at your option)
  any later version.

  This program is distributed in the hope that it will be useful, but
  WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  General Public License for more details.

  If you do not have a copy of the GNU General Public License, write to
  the Free Software Foundation, 59 Temple Place - Suite 330, Boston, MA
  02111-1307, USA.

  11..22..

  The latest version of this document is always available from
  http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html.

  From there, you can also obtain versions converted to plain ASCII
  text, GNU info, DVI, and PostScript, as well as the SGML source used
  for creating all these formats using the SGML-Tools (formerly
  Linuxdoc-SGML) system.  (Note that right now cross-references don't
  work in the translation to plain ASCII.)

  11..33..

  Everything should be pretty standard.  `R>' is used for the R prompt,
  and a `$' for the shell prompt (where applicable).

  11..44..

  Feedback is of course most welcome.

  In particular, note that I do not have access to Windows or Mac
  systems.  If you have information on these systems that you think
  should be added to this document, please let me know.

  22..

  22..11..

  R is a system for statistical computation and graphics.  It consists
  of a language plus a run-time environment with graphics, a debugger,
  access to certain system functions, and the ability to run programs
  stored in script files.

  The design of R has been heavily influenced by two existing languages:
  Becker, Chambers & Wilks' S (see question ``What is S?'') and
  Sussman's Scheme.  Whereas the resulting language is very similar in
  appearance to S, the underlying implementation and semantics are
  derived from Scheme.  See question ``What Are the Differences between
  R and S?'' for a discussion of the differences between R and S.

  R is being developed by Ross Ihaka and Robert Gentleman, who are
  Senior Lecturers at the Department of Statistics of the University of
  Auckland in Auckland, New Zealand.

  R is free software distributed under a GNU-style copyleft.

  22..22..  WWhhaatt MMaacchhiinneess DDooeess RR RRuunn oonn??

  R is being developed for the Unix, Windows and Mac platforms.

  R will configure and build under a number of common Unix platforms
  including dec-alpha-osf, freebsd, hpux, linux-elf, sgi-irix, solaris,
  and sunos.  If you know about other platforms, please drop me a note.

  22..33..  WWhhaatt IIss tthhee CCuurrrreenntt VVeerrssiioonn ooff RR??

  The current Unix version is 0.49, the previous one was 0.16.1.  The
  jump in the version number reflects the fact that this version
  represents quite a jump towards what is hoped to be in the eventual
  1.0 release.  (However, it fell a little short of what was wanted for
  0.50.)  Version 0.49 added group methods and complex numbers, and
  hence more or less provides a full implementation of S as described in
  ``The New S Language''.

  The versions for Windows and Mac are pre-alpha.  With some good luck,
  the Windows version will soon catch up with the Unix version.

  22..44..

  Sources, binaries and documentation for R can be obtained via CRAN,
  the ``Comprehensive R Archive Network'' (see question ``What is
  CRAN?'').

  22..55..  HHooww CCaann RR BBee IInnssttaalllleedd??

  22..55..11..

  If binaries are available for your platform (see question ``Are there
  Unix Binaries for R?''), you can use these, following the instructions
  that come with them.

  Otherwise, you can compile and install R yourself, which can be done
  very easily under a number of common Unix platforms (see question
  ``What Machines Does R Run on?'').  The file INSTALL that comes with
  the R distribution contains instructions.

  Choose a place to install the R tree (R is not just a binary, but has
  additional data sets, help files, font metrics etc).  Let's call this
  place RHOME (given appropriate permissions, a natural choice would be
  `/usr/local/lib/R').  Untar the source code, and issue the following
  commands (at the shell prompt):

       $ ./configure
       $ make
       $ make install-help

  You can also build a LaTeX version of the manual entries with

       $ make install-latex

  and an HTML version of the manual with

       $ make install-html

  If these commands execute successfully, the R binary will be copied to
  the `$RHOME/bin' directory.  In addition, a shell script font-end
  called `R' will be created and copied to the same directory.  You can
  copy this script to a place where users can invoke it, for example to
  `/usr/local/bin'.  You could also copy the man page `R.1' to a place
  where your man reader finds it, such as `/usr/local/man/man1'.

  22..55..22..

  Get the file `Rexe.zip' from the `bin/ms-windows' directory of a CRAN
  site.  This archive contains a binary Windows 3.xx distribution for R
  and installation instructions.

  Robert Gentleman has recently made an updated pre-alpha Windows
  executable file available for ftp at
  ftp://stat.auckland.ac.nz/pub/research/rgentlem/rbeta.zip.  This
  binary should be more compatible with Windows 95 than the other (he
  does not know about 3.1).  You still need all the other extra files
  from the previous Windows distribution, it is only an executable.

  22..55..33..

  CRAN sites have a directory `bin/macintosh' which contains
  `R.sea.hqx', a binhexed self-extracting archive, and installation
  instructions in `README.MACINTOSH'.

  22..66..  AArree tthheerree UUnniixx BBiinnaarriieess ffoorr RR??

  Experimental `.deb' packages for installation under Debian GNU/Linux
  can be found in `bin/ix86-linux'.  No other binaries distributions for
  Unix systems have thus far been made publically available.

  22..77..  WWhhiicchh DDooccuummeennttaattiioonn EExxiissttss ffoorr RR??

  Currently, there is no R manual.  Online documentation for most of the
  functions and variables in R exists, and can be printed on-screen by
  typing help(_n_a_m_e) (or ?_n_a_m_e) at the R prompt, where _n_a_m_e is the name
  of the R object help is sought for.  (In the case of unary and binary
  operators and control-flow special forms, the name may need to be be
  quoted.)

  This documentation can also be made available as HTML, and as hardcopy
  via LaTeX, see question ``How Can R Be Installed?''.  An up-to-date
  HTML version is always available for web browsing at

       http://www.stat.math.ethz.ch/R-manual

  In the absence of a systematic introduction to R, one can mostly get
  along with introductions to S or S-PLUS, such as ``Notes on S-PLUS:  A
  Programming Environment for Data Analysis and Graphics'' by Bill
  Venables <venables at stats.adelaide.edu.au> and David Smith
  <D.M.Smith at lancaster.ac.uk>.  This document talks mostly about plain S
  features, and does not concentrate on features specific to S-PLUS, and
  is available from the Statlib S repository at
  http://lib.stat.cmu.edu/S/SplusNotes/ (LaTeX source and PostScript).
  An introduction to R based on it will soon be available.

  Last, but not least, Ross' and Robert's experience in designing and
  implementing R is described in:

       @Article{,
         author =       {Ross Ihaka and Robert Gentleman},
         title =        {R: A Language for Data Analysis and Graphics},
         journal =      {Journal of Computational and Graphical Statistics},
         year =         1996,
         volume =       5,
         number =       3,
         pages =        {299--314}
       }

  This is also the reference for R to use in publications.

  22..88..

  Thanks to Martin Maechler <maechler at stat.math.ethz.ch>, there are
  three mailing lists devoted to R.

     rr--aannnnoouunnccee
        This list is for announcements about the development of R and
        the availability of new code.

     rr--ddeevveell
        This list is for discussions about the future of R and pre-
        testing of new versions.  It is meant for those who maintain an
        active position in the development of R.

     rr--hheellpp
        The `main' R mailing list, for announcements about the
        development of R and the availability of new code, questions and
        answers about problems and solutions using R, enhancements and
        patches to the source code and documentation of R, comparison
        and compatibility with S and S-plus, and for the posting of nice
        examples and, benchmarks.

  To send a message to everyone on the r-help mailing list, send email
  to

       r-help at stat.math.ethz.ch

  To subscribe (or unsubscribe) to this list send subscribe (or unsub-
  scribe) in the BODY of the message (not in the subject!)  to r-help-
  request at stat.math.ethz.ch.  Information about the list can be obtained
  by sending an email with info as its contens to r-help-
  request at stat.math.ethz.ch.

  Subscription and posting to the other lists is done analogously, with
  `r-help' replaced by `r-announce' and `r-devel', respectively.  Note
  that the r-announce list is gatewayed into r-help, so you don't need
  to subscribe to both of them.

  It is recommended that you send mail to r-help rather than only to the
  R developers (who are also subscribed to the list, of course).  This
  may save them precious time they can use for constantly improving R,
  and will typically also result in much quicker feedback for yourself.

  Of course, in the case of bug reports it would be very helpful to have
  code which reliably reproduces the problem.

  22..99..  WWhhaatt iiss CCRRAANN??

  The ``Comprehensive R Archive Network'' (CRAN) is a collection of
  sites which carry identical material, consisting of the R
  distribution(s), the contributed extensions, documentation for R, and
  binaries.

  The CRAN master site can be found at the URL

       ftp://ftp.ci.tuwien.ac.at/pub/R/

  and is currently being mirrored daily at

       http://lib.stat.cmu.edu/R/CRAN/
       ftp://franz.stat.wisc.edu/pub/R/
       ftp://ftp.stat.math.ethz.ch/R-CRAN/

  Please use the CRAN site closest to you to reduce network load.

  The structure of the CRAN tree is as follows.

     ``src/base'
        contains the official R distribution as provided by Ross Ihaka
        and Robert Gentleman.

     ``src/contrib'
        contains code for extension packages.

     ``doc'
        is for additional documentation and information on R.
     ``bin'
        is for prebuilt R binaries (the base distribution and
        extensions), grouped according to platforms.  Currently, there
        are only experimental packages for Debian GNU/Linux.  I hope
        that `.tar.gz' files with contents relative to an installation
        tree (e.g. `bin', `lib/R/', and `man/man1/R.1') can be made
        available soon for all major supported Unix platforms.

  The process of ``submitting'' to CRAN currently is very simple: upload
  to ftp://ftp.ci.tuwien.ac.at/incoming and send email to Kurt Hornik
  <Kurt.Hornik at ci.tuwien.ac.at>.  Please indicate the copyright
  situation (GPL, ...) in your submission.

  33..

  33..11..  WWhhaatt IIss SS??

  S is a very high level language and an environment for data analysis
  and graphics.  S was written by Richard A. Becker, John M. Chambers,
  and Allan R. Wilks of AT&T Bell Laboratories Statistics Research
  Department.

  The primary references for S are two books by the creators of S.

  +o  Richard A. Becker, John M. Chambers and Allan R. Wilks (1988),
     ``The New S Language,'' Chapman & Hall, London.

     This book is often called the ``_B_l_u_e _B_o_o_k''.

  +o  John M. Chambers and Trevor J. Hastie (1992), ``Statistical Models
     in S,'' Chapman & Hall, London.

     This is also called the ``_W_h_i_t_e _B_o_o_k''.

  There is a huge amount of user-contributed code for S, available at
  the S Repository at CMU.

  See the ``Frequently Asked Questions about S''
  (http://lib.stat.cmu.edu/S/faq) for further information about S.

  33..22..  WWhhaatt IIss SS--PPLLUUSS??

  S-PLUS is a value-added version of S sold by Statistical Sciences,
  Inc. (now a division of Mathsoft, Inc.)  S is a subset of S-PLUS, and
  hence anything which may be done in S may be done in S-PLUS.  In
  addition S-PLUS has extended functionality in a wide variety areas,
  including robust regression, modern nonparametric regression, time
  series, survival analysis, multivariate analysis, classical
  statistical tests, quality control, and graphics drivers.  Add-on
  modules add additional capabilities for wavelet analysis, spatial
  statistics, and design of experiments.

  See the MathSoft S-PLUS page (http://www.mathsoft.com/splus.html) for
  further information.

  33..33..  WWhhaatt AArree tthhee DDiiffffeerreenncceess bbeettwweeeenn RR aanndd SS??

  Whereas the developers of R have tried to stick to the S language as
  defined in ``The New S Language'' (Blue Book, see question ``What is
  S?''), they have adopted the evaluation model of Scheme.

  This difference becomes manifest when _f_r_e_e variables occur in a
  function.  Free variables are those which are neither formal
  parameters (occurring in the argument list of the function) nor local
  variables (created by assigning to them in the body of the function).
  Whereas S (like C) by default uses _s_t_a_t_i_c scoping, R (like Scheme) has
  adopted _l_e_x_i_c_a_l scoping.  This means the values of free variables are
  determined by a set of global variables in S, but in R by the bindings
  that were in effect at the time the function was created.

  Consider the following function:

       cube <- function(n) {
         sq <- function() n * n
         n * sq()
       }

  Under S, sq() does not ``know'' about the variable n unless it is
  defined globally:

       S> cube(2)
       Error in sq():  Object "n" not found
       Dumped
       S> n <- 3
       S> cube(2)
       [1] 18

  In R, the ``environment'' created when cube() was invoked is also
  looked in:

       R> cube(2)
       [1] 8

  Lexical scoping allows using function closures and maintaining local
  state.  A simple example (taken from Abelson and Sussman) can be found
  in the `demos/language' subdirectory of the R distribution.  Further
  information is provided in the standard R reference ``R: A Language
  for Data Analysis and Graphics'' (see question ``Which Documentation
  Exists for R?'') and a paper on ``Lexical Scope and Statistical
  Computing'' by Robert Gentleman and Ross Ihaka which can be obtained
  from the `doc/misc' directory of a CRAN site.

  Lexical scoping also implies a further major difference.  Whereas S
  stores all objects as separate files in a directory somewhere (usually
  `.Data' under the current directory), R does not.  All objects in R
  are stored internally.  When R is started up it grabs a very large
  piece of memory and uses it to store the objects.  R performs its own
  memory management of this piece of memory.  Having everything in
  memory is necessary because it is not really possible to externally
  maintain all relevant ``environments'' of symbol/value pairs.  This
  difference also seems to make R _m_u_c_h _f_a_s_t_e_r than S.
  The down side is that if R crashes you will lose all the work for the
  current session.  Saving and restoring the memory ``images'' (the
  functions and data stored in R's internal memory at any time) can be a
  bit slow, especially if they are big.  In S this does not happen,
  because everything is saved in disk files and if you crash nothing is
  likely to happen to them.  R is still in an alpha stage, and does
  crash from time to time.  Hence, for important work you should
  consider saving often, see question ``How Can I Save My Workspace?''
  (other possibilities are logging your sessions, or have your R
  commands stored in text files which can be read in using source()).
  (Note that if you run R from within Emacs (see question ``R and
  Emacs''), you can save the contents of the interaction buffer to a
  file and conveniently manipulate it using S-transcript-mode, as well
  as save source copies of all functions and data used.)

  Apart from lexical scoping and its implications, R follows the S
  language definition in the Blue Book as much as possible, and hence
  really is an ``implementation'' of S.  There are some intentional
  differences where the behavior of S is considered ``not clean''.  In
  general, the rationale is that R should help you detect programming
  errors, while at the same time being as compatible as possible with S.

  Some known differences are the following.

  +o  In R, if x is a list, then x[sub] <- NULL and x[[sub]] <- NULL
     remove the specified elements from x.  The first of these is
     incompatible with S, where it is a no-op.

  +o  In S, the functions named .First and .Last in the `.Data' directory
     can be used for customizing, as they are executed at the very
     beginning and end of a session, respectively.  R looks for files
     called `.Rprofile' in the user's home directory and the current
     directory, and sources these.  (It also loads a saved image from
     `.RData' in case there is one.)  If a .First function exists then,
     it is executed.  The .Last mechanism is not supported yet.

  +o  Attaching library sections works differently.  In S, library(_n_a_m_e)
     adds the data directory for the library section _n_a_m_e to the search
     list.  If a function object named `.First.lib' exists in the
     directory, it is executed; this is typically used to dynamically
     load compiled code required by the functions in the section.  In R,
     library(_n_a_m_e) currently simply sources the file
     $RHOME/library/_n_a_m_e, and compiled code can be loaded by calling
     library.dynam() in this file.  The .First.lib mechanism is not
     really supported.  (Note that a library file is only loaded once,
     so that any code in the library that is not in a function is
     executed the first time the library is loaded.)

  +o  R does not try as hard as S to preserve dimnames attributes
     (examples are apply, rbind, and cbind, but also arithmetic ops).

  +o  R presently does not support IEEE Inf and NaN.

  +o  In R, attach currently only works for lists and data frames (not
     for directories).

  +o  Categories do not exist in R, and never will as they are deprecated
     now in S.  Use factors instead.

  +o  In R, For() loops are not necessary and hence not supported.

  +o  In R, assign() uses the argument envir= rather than where= as in S.

  +o  The random number generators are different, and the seeds have
     different length.

  +o  The glm family objects are implemented differently in R and S.  The
     same functionality is available but the components have different
     names.

  +o  terms objects are stored differently.  In S a terms object is an
     expression with attributes, in R it is a formula with attributes.
     The attributes have the same names but are mostly stored
     differently.  The major difference in functionality is that a terms
     object is subscriptable in S but not in R.  If you can't imagine
     why this would matter then you don't need to know.

  There are also differences which are not intentional, and result from
  missing or incorrect code in R.  The developers would appreciate
  hearing about any deficiencies you may find (in a written report fully
  documenting the difference as you see it).  Of course, it would be
  useful if you were to implement the change yourself and make sure it
  works.

  44..

  44..11..

  The R distribution comes with the following extra libraries:

     eeddaa
        Exploratory Data Analysis.  Currently only contains functions
        for robust line fitting, and median polish and smoothing.

     mmvvaa
        Multivariate Analysis.  Currently contains code for principal
        components (prcomp), canonical correlations (cancor),
        hierarchichal clustering (hclust), and metric multidimensional
        scaling (cmdscale).  More functions for clustering and scaling,
        biplots, profile and star plots, and code for ``real''
        discriminant analysis will be added soon.

  The following packages are available from the CRAN `src/contrib' area.

     aacceeppaacckk
        ace (Alternating Conditional Expectations) and avas (Additivity
        and VAriance Stabilization for regression) for selecting
        regression transformations.

     bboooottssttrraapp
        Software (bootstrap, cross-validation, jackknife), data and
        errata for the book ``An Introduction to the Bootstrap'' by B.
        Efron and R. Tibshirani, 1993, Chapman and Hall.

     cctteesstt
        A library of classical tests, including the Bartlett, Fisher,
        Kruskal-Wallis, Kolmogorov-Smirnov, and Wilcoxon tests.

     ddaattee
        Functions for dealing with dates.  The most useful of them
        accepts a vector of input dates in any of the forms 8/30/53,
        30Aug53, 30 August 1953, ..., August 30 53, or any mixture of
        these.

     ee11007711
        Miscellaneous functions used at the Department of Statistics at
        TU Wien (E1071).

     ffrraaccddiiffff
        Maximum likelihood estimation of the parameters of a
        fractionally differenced ARIMA(p,d,q) model (Haslett and
        Raftery, Applied Statistics, 1989).

     ggeeee
        An implementation of the Liang/Zeger generalized estimating
        equation approach to GLMs for dependent data.

     jjppnn
        A function to plot Japan's coast-line and prefecture boundaries.

     oozz Functions for plotting Australia's coastline and state
        boundaries.

     ssnnnnss
        An R interface to the Stuttgart Neural Networks Simulator
        (SNNS).

     sspplliinneess
        Regression spline functions.

     ssuurrvviivvaall44
        Functions for survival analysis (requires sspplliinneess).

  See CRAN `src/contrib/INDEX' for more information.

  Paul Gilbert <pgilbert at bank-banque-canada.ca> has a written a
  multivariate time series library for S called ttiimmee..sseerriieess that is
  mostly converted to run in R.  He will make this port generally
  available when complex numbers are implemented (see question ``What is
  the current version of R?'').

  According to Paul, the PADI interface from the Bank of Canada also
  works with minor changes.  PADI can be used to access Fame time series
  data bases and potentially other databases, even remotely over the
  Internet.  For further information see http://www.bank-banque-
  canada.ca/pgilbert.

  According to Arne Kovac <A.Kovac at Bristol.ac.uk>, Guy Nason's
  WaveThresh package for S worked with only minor modifications under R
  version 0.12.

  More code has been posted to the r-help mailing list, and can be
  obtained from the mailing list archive.

  44..22..

  (Unix only.)  Untar the add-on packages in $RHOME/src/library/ and
  type

       $ make libs
       $ cd ../..
       $ ./etc/install-libhelp

  at the shell prompt.

  44..33..

  To find out which add-ons have already been installed, type

       R> library()

  at the R prompt.  This produces something like

       NAME         DESCRIPTION
       acepack      ace() and avas() for selecting regression transformations
       bootstrap    Functions for the book "An Introduction to the Bootstrap"
       ctest        Classical Tests
       date         Functions for handling dates
       eda          Exploratory Data Analysis
       fracdiff     Fractionally differenced ARIMA (p,d,q) models
       gee          Generalized Estimating Equation models
       mva          Classical Multivariate Analysis
       splines      Regression spline functions
       survival4    Survival analysis [needs library(splines)]

  You can ``load'' an add-on with name _n_a_m_e by

       R> library(_n_a_m_e)

  You can then find out which functions it provides by typing

       R> help(library = _n_a_m_e)

  44..44..

  R is currently still in alpha (or pre-alpha) state, so simply using it
  and communicating problems is certainly of great value.

  One place where functionality is still missing is the modeling
  software as described in ``Statistical Models in S'' (see question
  ``What is S?''.  The functions

        add1 kappa alias labels drop1 proj

  are missing; many of these are interpreted functions so anyone that is
  bored and wants to have a go at implementing them it would be appreci-
  ated.  In addition, only linear and generalized linear models are cur-
  rently available, aov, gam, loess, tree, and the nonlinear modelling
  code are not there yet.

  Many of the packages available at the Statlib S Repository might be
  worth porting to R.

  If you are interested in working on any of these projects, please
  notify Kurt Hornik.

  55..  RR aanndd EEmmaaccss

  55..11..  IIss tthheerree EEmmaaccss SSuuppppoorrtt ffoorr RR??

  There is an Emacs-Lisp interface to S/S-PLUS called S-mode.  Its
  current version is 4.8 and can be obtained at
  http://www.maths.lancs.ac.uk:2080/~maa036/elisp/S-mode/.  The earlier
  versions which can be found at the Statlib S repository (gnuemacs3 and
  gnuemacs4) are outdated.

  It contains code for interacting with an inferior S process from
  within Emacs including an interface to the help system, editing S
  source code, and transcript manipulation, and comes with detailed
  instructions for installation.

  Martin Maechler <maechler at stat.math.ethz.ch> and Tony Rossini
  <rossini at math.sc.edu> have integrated support for R into this package.
  The current version is at

       ftp://ftp.math.sc.edu/rossini/S-mode-4.8.MM6.XE2.tar.gz

  and runs under both GNU Emacs and XEmacs.

  To install, put the byte-compiled `.el' files into a place where Emacs
  can find them, and add

       (if (not (assoc "\\.R$" auto-mode-alist)
           (add-to-list 'auto-mode-alist (cons "\\.R$" 'R-mode))))
       (autoload 'R "S" "Run an inferior R process" t)
       (autoload 'R-mode "S" "Mode for editing R source" t)
       (autoload 'r-mode "S" "Mode for editing R source" t)

  to one of your Emacs startup files, typically `~/.emacs'.  You can
  then fire up R from within Emacs by typing `M-x R' (note however that
  many interface functions will not work), and if you use the extension
  `.R' for your files with R code, Emacs will automagically turn on R
  edit mode whenever you visit such a file.

  Tony Rossini, Martin Maechler and Kurt Hornik have officially taken
  over the development of S-mode.  Version 4.9 (based on the current
  4.8.MM series) will be released shortly;  version 5.0 (codenamed
  ``istat'') should be out by the end of 1997.

  55..22..

  Yes.  Inferior R mode provides a readline/history mechanism, object
  name completion, and syntax-based highlighting of the interaction
  buffer using Font Lock mode, as well as a very convenient interface to
  the R help system.

  Of course, it also integrates nicely with the mechanisms for editing R
  source using Emacs.  One can write code in one Emacs buffer and send
  whole or parts of it for execution to R; this is helpful for both data
  analysis and programming.  One can also seamlessly integrate with a
  revision control system, in order to maintain a log of changes in your
  programs and data, as well as to allow for the retrieval of past
  versions of the code.

  In addition, it allows you to keep a record of your session, which can
  also be used for error recovery through the use of the transcript
  mode.

  66..

  66..11..

  R (currently) uses a _s_t_a_t_i_c memory model.  This means that when it
  starts up, it asks the operating system to reserve a fixed amount of
  memory for it.  The size of this chunk cannot be changed subsequently.
  Hence, it can happen that not enough memory was allocated.

  In these cases, you should restart R with more memory available, using
  the command line options -n and -v.  To understand these options, one
  needs to know that R maintains separate areas for fixed and variable
  sized objects.  The first of these is allocated as an array of
  SEXPRECs assembled in a list using ``cons cells'' (ordered pairs each
  containing an element of the list and a pointer to the next cell), and
  the second as an array of VECRECs.  The -n option can be used to
  specify the number of cons cells (each occupying 16 bytes) which R is
  to use (the default is 200000), and the -v option to specify the size
  of the vector heap in megabytes (the default is 2).  Only integers are
  allowed for both options.

  E.g., to read in a table of 5000 observations on 40 numeric variables,
  R -v 6 should do.

  Note that the information where to find vectors and strings on the
  heap is stored using cons cells.  Thus, it may also be necessary to
  allocate more space for cons cells in order to perform computations
  with very ``large'' variable-size objects.

  You can find out the current memory comsumption by typing gc() at the
  R prompt.

  66..22..

  R sometimes has problems parsing a file which does not end in a
  newline.  This can happen for example when Emacs is used for editing
  the file and next-line-add-newlines is set to nil.  To avoid the
  problem, either set require-final-newline to a non-nil value in one of
  your Emacs startup files, or make sure R-mode (see question ``Is there
  Emacs Support for R?'') is used for editing R source files (which
  locally ensures this setting).

  Earlier R versions had a similar problem when reading in data files,
  but this should have been taken care of now.

  66..33..

  You can use

       x[i] <- list(NULL)

  to set component i of the list x to NULL, similarly for named compo-
  nents.  Do not set x[i] or x[[i]] to NULL, because this will remove
  the corresponding component from the list.

  For dropping the row names of a matrix x, it may be easier to use
  rownames(x) <- NULL, similarly for column names.

  66..44..  HHooww CCaann II SSaavvee MMyy WWoorrkkssppaaccee??

  The expression

       save(list = ls(), file = ".RData")

  saves the objects in the currently active environment (typically the
  user's .GlobalEnv) to the file `.RData' in the R startup directory.

  66..55..

  To remove all objects in the currently active environment (typically
  the user's .GlobalEnv), you can do

       rm(list = ls())

  77..

  Of course, many many thanks to Robert and Ross for the R system, and
  to the package writers and porters for adding to it.

  Special thanks go to Peter Dalgaard, Paul Gilbert, Martin Maechler,
  and Anthony Rossini for their comments which helped me improve this
  FAQ.

  More to some soon ...


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From rgentlem at stat.auckland.ac.nz  Sun Apr 27 05:21:15 1997
From: rgentlem at stat.auckland.ac.nz (Robert Gentleman)
Date: Sun, 27 Apr 1997 15:21:15 +1200 (NZST)
Subject: R for Windows95
Message-ID: <199704270321.PAA22468@stat13.stat.auckland.ac.nz>

There is a zip (RApril.zip) which should now be at the mirrors
in R/Windows. Please have a try and let me know of any problems.
I haven't been able to test it on very many machines here so
I really don't know about 3.11 or NT.

robert

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From A.Kovac at Bristol.ac.uk  Tue Apr 29 16:06:19 1997
From: A.Kovac at Bristol.ac.uk (Arne Kovac)
Date: Tue, 29 Apr 1997 15:06:19 +0100 (BST)
Subject: WaveThresh
Message-ID: <Pine.GSO.3.95.970429144610.21981B-100000@hannan>

WaveThresh is now available from the CRAN. WaveThresh is a wavelet package
which is used by many authors in the wavelet literature. A description of
the functions can be found in

   Nason, G. P. and Silverman, B. W. (1994), 
   "The discrete wavelet transform in S", 
   Journal of Computational and Graphical Statistics, Vol. 3, pp 163-191.

Another source of information is the home page for WaveThresh:

http://www.stats.bris.ac.uk/pub/software/wavethresh/WaveThresh.html.

The original S-Plus version is developed by Guy Nason and available from
the Statlib archive. The R version supports nearly all the functionality
except some restrictions with the use of the two-dimensional wavelet
transform. A new version will be released soon and (hopefully) ported to R
as well.

Have fun 

Arne Kovac

-- 
Arne Kovac
School of Mathematics                    Phone: +44 (0117) 942 7551
University of Bristol                    A.Kovac at bristol.ac.uk    
University Walk, Bristol, BS8 1TW, U.K.  http://www.stats.bris.ac.uk/~maak

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From hfe at math.uio.no  Wed Apr 30 00:01:17 1997
From: hfe at math.uio.no (Harald Fekjaer)
Date: Wed, 30 Apr 1997 00:01:17 +0200
Subject: New R package for survival analysis
Message-ID: <33666FAD.181869A4@ulrik.uio.no>

Dear people at r-announce

At "http://www.med.uio.no/imb/stat/addreg/",
you can now find the new package addreg (version 1.0).

It performs additive hazards regression, which is an alternative (or
supplement) to the Cox model. It results in plots that are informative
regarding the effect of covariates on survival.

I wrote it as a S-plus package, but has made some small adjustments to
make it work on R. This can now be down loaded in a separte file. 

In version 1.1 of addreg (last quarter 1997?), I'll plan to give a full
R version with the same functionality as the S-plus version.


Harald Fekjaer, Section of Medical Statistice, University of Oslo
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From Friedrich.Leisch at ci.tuwien.ac.at  Mon Jun  9 18:21:45 1997
From: Friedrich.Leisch at ci.tuwien.ac.at (Friedrich Leisch)
Date: Mon, 9 Jun 1997 18:21:45 +0200
Subject: mlbench-0.1 --- machine learning benchmark problems 
Message-ID: <199706091621.SAA16397@galadriel.ci.tuwien.ac.at>


I've made a package from some benchmark datasets for use with R and
uploaded it to CRAN. 

Here's the Index entry:

mlbench-0.1.tar.gz:
	A collection of artificial and real-world machine learning
	benchmark problems, including, e.g., the boston housing
	data from the UCI repository.

	Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at>
	Original data sets from various sources.
	[1997/06/09]


This (naturally) includes some data files ... hence you have to do
some parts of the installation manually, see the INSTALL file for
details (IMHO R has no clear concept for data: one flat directory is
certainly not sufficient). 

R&R: Any news on this front?

Best,
Fritz

-- 
-------------------------------------------------------------------
                        Friedrich  Leisch                             
Institut f?r Statistik                      Tel: (+43 1) 58801 4541
Technische Universit?t Wien                 Fax: (+43 1)  504 14 98
Wiedner Hauptstra?e 8-10/1071      Friedrich.Leisch at ci.tuwien.ac.at
A-1040 Wien, Austria             http://www.ci.tuwien.ac.at/~leisch
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From thomas at biostat.washington.edu  Tue Jul  8 18:41:22 1997
From: thomas at biostat.washington.edu (Thomas Lumley)
Date: Tue, 8 Jul 1997 09:41:22 -0700 (PDT)
Subject: R-beta: integration, subset selection
In-Reply-To: <199707030527.HAA05571@aragorn.ci.tuwien.ac.at>
Message-ID: <Pine.SUN.3.95.970708093641.4305B-100000@zen>

Two new libraries:
leaps:  replaces and improves on S leaps() function for regression subset
selection.

integrate:  Integration by adaptive quadrature in 1-20 dimensions.

Both available from http://www.biostat.washington.edu/~thomas/R.html and
soon from CRAN

Thomas Lumley
------------------------------------------------------+------
Biostatistics		: "Never attribute to malice what  :
Uni of Washington	:  can be adequately explained by  :
Box 357232		:  incompetence" - Hanlon's Razor  :
Seattle WA 98195-7232	:				   :
------------------------------------------------------------

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From Friedrich.Leisch at ci.tuwien.ac.at  Thu Jul 10 15:10:35 1997
From: Friedrich.Leisch at ci.tuwien.ac.at (Friedrich Leisch)
Date: Thu, 10 Jul 1997 15:10:35 +0200
Subject: New Packages in CRAN
Message-ID: <199707101310.PAA03881@galadriel.ci.tuwien.ac.at>


The following packages have been contributed to CRAN by Thomas Lumley:

integrate-1.0.tar.gz:
	S function and supporting C and Fortran code for adaptive
	quadrature.  The underlyling fortran code is purported to
	work in from 2 to 20 dimensions.

	S original by Michael Meyer (mikem at andrew.cmu.edu).
	R port by Thomas Lumley <thomas at biostat.washington.edu>.
	[1997/07/10]

leaps-1.0.tar.gz:
	This library performs an exhaustive search for the best
	subsets of a given set of potential regressors, using a
	branch-and-bound algorithm, and also performs searches using
	a number of less time-consuming techniques. It is designed to
	replace the "leaps()" command in S. 

	Packaged for R by Thomas Lumley <thomas at biostat.washington.edu>
	and based on FORTRAN77 code by Alan Miller
	<Alan.Miller at vic.cmis.csiro.au>.
	[1997/07/10]
	

The tar files are available from the CRAN main site at

	ftp://ftp.ci.tuwien.ac.at/pub/R/

in the directory

	src/contrib

and from the mirror sites (tomorrow):

    http://lib.stat.cmu.edu/R/CRAN/                     (U.S./Pennsylvania)
    ftp://franz.stat.wisc.edu/pub/R/                    (U.S./Wisconsin)
    ftp://ftp.stat.math.ethz.ch/R-CRAN/                 (Switzerland)
    http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/   (Italy)

If you want your server being added to the list of mirrors, please let
us know.

Best,
Fritz

-- 
-------------------------------------------------------------------
                        Friedrich  Leisch                             
Institut f?r Statistik                      Tel: (+43 1) 58801 4541
Technische Universit?t Wien                 Fax: (+43 1)  504 14 98
Wiedner Hauptstra?e 8-10/1071      Friedrich.Leisch at ci.tuwien.ac.at
A-1040 Wien, Austria             http://www.ci.tuwien.ac.at/~leisch
     PGP public key http://www.ci.tuwien.ac.at/~leisch/pgp.key
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From takafumi at u-aizu.ac.jp  Thu Jul 10 16:30:15 1997
From: takafumi at u-aizu.ac.jp (Takafumi Hayashi)
Date: Thu, 10 Jul 1997 23:30:15 +0900
Subject: R-beta: New Packages in CRAN 
In-Reply-To: Your message of "Thu, 10 Jul 1997 15:10:35 +0200."
             <199707101310.PAA03881@galadriel.ci.tuwien.ac.at> 
Message-ID: <199707101430.XAA04737@tansei.u-aizu.ac.jp>

Dear Sirs,

> and from the mirror sites (tomorrow):
> 
>     http://lib.stat.cmu.edu/R/CRAN/                     (U.S./Pennsylvani=
> a)
>     ftp://franz.stat.wisc.edu/pub/R/                    (U.S./Wisconsin)
>     ftp://ftp.stat.math.ethz.ch/R-CRAN/                 (Switzerland)
>     http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/   (Italy)

We are mirroring CARN daily to our ftp-server.
      ftp://ftp.u-aizu.ac.jp/pub/lang/R/CRAN              (Japan)
Best Regards,

---
Takafumi Hayashi             takafumi at u-aizu.ac.jp
The University of Aizu       phone : +81-242-37-2614
FCS Lab.                     fax   : +81-242-37-2734

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From Friedrich.Leisch at ci.tuwien.ac.at  Thu Jul 17 10:43:22 1997
From: Friedrich.Leisch at ci.tuwien.ac.at (Friedrich Leisch)
Date: Thu, 17 Jul 1997 10:43:22 +0200
Subject: new version 0.1 of e1071 in CRAN
Message-ID: <199707170843.KAA09296@galadriel.ci.tuwien.ac.at>


The following functions have been added to the e1071 package

	skewness
	kurtosis
	hamming.distance


The tar files are available from the CRAN main site at

	ftp://ftp.ci.tuwien.ac.at/pub/R/

in the directory

	src/contrib

and from the mirror sites (tomorrow):

    http://lib.stat.cmu.edu/R/CRAN/                     (U.S./Pennsylvania)
    ftp://franz.stat.wisc.edu/pub/R/                    (U.S./Wisconsin)
    ftp://ftp.stat.math.ethz.ch/R-CRAN/                 (Switzerland)
    http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/   (Italy)
    ftp://ftp.u-aizu.ac.jp/pub/lang/R/CRAN/             (Japan)

If you want your server being added to the list of mirrors, please let
us know.

Best,
Fritz

-- 
-------------------------------------------------------------------
                        Friedrich  Leisch                             
Institut f?r Statistik                      Tel: (+43 1) 58801 4541
Technische Universit?t Wien                 Fax: (+43 1)  504 14 98
Wiedner Hauptstra?e 8-10/1071      Friedrich.Leisch at ci.tuwien.ac.at
A-1040 Wien, Austria             http://www.ci.tuwien.ac.at/~leisch
     PGP public key http://www.ci.tuwien.ac.at/~leisch/pgp.key
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From plummer at iarc.fr  Tue Jul 29 17:30:11 1997
From: plummer at iarc.fr (Martyn Plummer)
Date: Tue, 29 Jul 1997 17:30:11 +0200
Subject: RPM package for R
Message-ID: <3.0.1.16.19970729173011.31175978@droopy.iarc.fr>

A precompiled "RPM" binary of R-0.50.a1 is now available on CRAN for
installation on Linux systems which use the RedHat Package
Manager. The package also includes all contributed libraries
currently in the CRAN archive. Source code is not included in the
package, but the source RPM file is available on CRAN. 

The package is not PGP signed as PGP is illegal in France.

This is the first public release of the RPM package and may
have a few rough edges. Feedback is welcome.

Martyn
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Hi all,

thanks help by Thomas Lumley (who provided us also with a beautiful
logo!) I've finally made a start for an html frontend for CRAN. It
should be available on all mirror sites tomorrow.

The master site is at

	http://www.ci.tuwien.ac.at/R

and the usual mirror sites are

http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/ (IASC archive, Italy)
ftp://ftp.u-aizu.ac.jp/pub/lang/R/CRAN/           (University of Aizu, Japan)
ftp://ftp.stat.math.ethz.ch/R-CRAN/               (ETH Z?rich, Switzerland)
http://lib.stat.cmu.edu/R/CRAN/                   (Statlib, CMU, USA)
ftp://ftp.biostat.washington.edu/mirrors/R/CRAN/  (University of Washington, USA)
ftp://franz.stat.wisc.edu/pub/R/                  (University of Wisconsin, USA)


Best,
Fritz

--
-------------------------------------------------------------------
                        Friedrich  Leisch
Institut f?r Statistik                      Tel: (+43 1) 58801 4541
Technische Universit?t Wien                 Fax: (+43 1)  504 14 98
Wiedner Hauptstra?e 8-10/1071      Friedrich.Leisch at ci.tuwien.ac.at
A-1040 Wien, Austria             http://www.ci.tuwien.ac.at/~leisch
     PGP public key http://www.ci.tuwien.ac.at/~leisch/pgp.key
-------------------------------------------------------------------







From ihaka at stat.auckland.ac.nz  Fri Dec  5 09:30:56 1997
From: ihaka at stat.auckland.ac.nz (Ross Ihaka)
Date: Fri, 5 Dec 1997 21:30:56 +1300 (NZDT)
Subject: New R Version for Unix
Message-ID: <199712050830.VAA18123@stat1.stat.auckland.ac.nz>


Version 0.60 of R for Unix is now available.  Release of this version
has been delayed for a variety of reasons, but we hope that it will
provide a good deal more stability and functionality than previous
versions.  However it should be regarded as of "alpha" quality for a
short shakedown period.

We would like user feedback to help us improve the quality of R.
You can do this by subscribing to the r-devel mailing list by sending
a message containing
	 subscribe
(in the "body", not the subject!) to:
        r-devel-request at stat.math.ethz.ch

Version 0.60 or R may be obtained from the following sites:

	TU Wein, Austria
	ftp://ftp.ci.tuwien.ac.at/pub/R

	IASC archive, Italy
	http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/contents.html

	University of Aizu, Japan
	ftp://ftp.u-aizu.ac.jp/pub/lang/R/CRAN/contents.html

	ETH Zurich, Switzerland
	ftp://ftp.stat.math.ethz.ch/R-CRAN/contents.html

	Statlib, Carnegie Mellon University, USA
	http://lib.stat.cmu.edu/R/CRAN/contents.html

	University of Washington, USA
	ftp://ftp.biostat.washington.edu/mirrors/R/CRAN/contents.html

	University of Wisconsin, USA
	http://franz.stat.wisc.edu/pub/R/contents.html

There are some large changes to R with this version (additional large
changes are planned for further versions).  In particular, the
documentation format has changed and the general standard of
documentation improved a good deal.  Perl version 5 is now required to
process the documentation, but pre-formatted versions will be
available.

R is now being developed by a larger group than its original authors.
In particular, this release represents the direct efforts of the
following group:

	Peter Dalgaard, Robert Gentleman, Kurt Hornik, Ross Ihaka,
	Thomas Lumley, Friedrich Leisch, Martin Maechler, Paul Murrell,
	Heiner Schwarte and Luke Tierney.

In addition, hundreds of contributions, in the form of code, patches
and bug reports, have been made by a much larger group of indiviuals. 

R is now a GNU project and will be making changes to meet GNU coding
and installation standards.

A detailed list of changes follows.


NEW FEATURES

    o	There has been a major change in directory structure masterminded
	by Kurt Hornik.
	library(.) now attaches ``package''s which are better
	integrated, see "?library".  Packages may be available from
	outside the RHOME path via the .lib.loc variable.

    o	The documentation format (of files in src/library/<package>/man/ )
	has changed to a more easily parsable LaTeX like format.
	The doc files now all end in `.Rd'.
	etc/Rman2Rd  can be used to translate old-style documentation to
	the new one.
	The translation to *roff, LaTeX and HTML is now done using
	etc/Rdconv, written in Perl by Fritz Leisch.

	The HTML online help produced has now links which work.
	The manual (in doc/manual/) now includes a section on the
	documentation format and on mathematical text in graphs.

	etc/ further contains `Sd2Rd' for (partial) translation of S `.d'
	documentation to Rd, and
	`Rd2txt' and `Rd2dvi' for easy previewing of single Rd files.

    o	The use of "names" on one dimensional arrays will now produce
	sensible results.  This means that for most purposes,
	one dimensional arrays can be treated like vectors.

    o	We have a applied a patch from mward at wolf.hip.berkeley.edu
	which should substantially improve the speed of (vector)
	arithmetic.

    o	The modeling formula handler has been expanded so that it
	accepts y ~ 0 + x as a "through the origin" specification.
	models with no parameters are now acceptable.

    o	"cov", "cor" and "var" now produce a matrix result if either
	of their x or y arguments is a matrix.	Dimnames are propagated
	in a sensible fashion.

    o   New chisq.test(.) and prop.test() from Kurt Hornik.

    o   New read.fwf(.) for reading fixed width format (KH).

    o   New str(.) [alternative to summary(.) for programmers] (MM).

    o   New example data sets "esoph", "infert" and "anscombe" (TL),
	"iris3" (KH) and "stackloss" (MM).

    o   source(.) has several new arguments, notably ``echo = FALSE''.
	This is applied in the new function  demo(.) which runs all the
	code in demos/ (but dynload).

    o   strheight(.) is new, accompanying strwidth(.).
	Both now work for mathematical expressions (Paul Murrell).

    o   The LaTeX version of the manual (-> doc/manual/) now has an index.

    o   EVERY *.Rd file in src/library/base/man/ has now at least one \keyword

    o	New package (`library(.)') "stepfun" for step functions, incl.
	empirical distributions.


BUG FIXES

    o   Regular expression matching is now done with system versions
	of the regexp library.  This should fix compilation problems
	on some platforms.

    o	"approx" and "approxfun", have had some minor adjustments.
	which fix the interpretation of the rule= argument.  The
	code for piecewise constant case is now internal C code
	that than interpreted.	This should boost performance in
	this case.

    o	There has been a minor fixup of "model.frame" to ensure that
	subsets, weights, etc are handled properly.

    o	Model fitting of the form

		 lm(y~., data=df)
		glm(y~., data=df)

	will now work.	The RHS of the model will consist of an
	additive model containing all (non-respose) variabels in the
	given data frame.

    o	The following type of assignment to data frame subsets

		z <- data.frame(x=rnorm(10),y=rnorm(10),z=rnorm(10))
		z[,1:2] <- matrix(1:20,nc=2)

	was producing incorrect results.  The solution was to wrap
	an implicit "as.data.frame" around the RHS.

    o	"[.data.frame" no longer has a default drop=TRUE argument.
	This means that subsetting a data frame with "[" will always
	yield a data frame.

    o	There was a swap of coordinates internally in "mtext" which
	meant that labels were coming out in the wrong place.  Fixed.

    o	Syntax errors in parse(text="...") would cause R to terminate
	with a segmentation violation.	This no longer happens, although
	the result is still not perfect (the parse() returns).
	This will be fixed by a future parse rewrite.

    o   rainbow, topo.colors, etc., now also work with n in {1,2};
	don't return duplicate neighbor colors anymore.

    o   legend has new `text.width' argument and now also works with
	mathematical expressions as text.

    o   hist() now works better, has a `plot = TRUE' argument, and returns
	something useful.

    o   barplot() improved for `names', now returns vector of midpoints.

    o   lm(), lm.fit, lm.wfit (was `lm.w.fit'):  Made more compatible.
	Dealing with (close to) collinear situations is still not flexible
	enough.

    o   internal postcript() improved (missing lines in boxplot(.)).

    o   Improvement to many (even most ?) documentation (.Rd) files.

    o   Numerous other fixes of minor things ...


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From ihaka at stat.auckland.ac.nz  Sun Dec  7 20:13:41 1997
From: ihaka at stat.auckland.ac.nz (Ross Ihaka)
Date: Mon, 8 Dec 1997 08:13:41 +1300 (NZDT)
Subject: First Patch to R-0.60 for Unix
Message-ID: <199712071913.IAA27288@stat1.stat.auckland.ac.nz>

You can find a first patch to R-0.60 at the same archive sites as you
obtained your R sources.  The patch is called

	R-0.60-patch1.gz.

Alternatively, you can pick a patched distribution which is in the file

	R-0.60.1.tgz

The patch fixes a multiple file closing problem which causes core dumps
on some versions of Unix.

	Ross
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
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From Kurt.Hornik at ci.tuwien.ac.at  Tue Dec  9 18:48:20 1997
From: Kurt.Hornik at ci.tuwien.ac.at (Kurt Hornik)
Date: Tue, 9 Dec 1997 18:48:20 +0100
Subject: R FAQ v0.60
Message-ID: <199712091748.SAA29807@aragorn.ci.tuwien.ac.at>

An updated version of the R FAQ to accompany the new 0.60 release is now
available at the usual site,

	http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

A plain text version of the FAQ is appended below.

-kh

****** snip snip snip **************************************************
  R FAQ
  Kurt Hornik
  v0.60-6, 1997/12/08

  This document contains answers to some of the most frequently asked
  questions about R.  Feedback is welcome.
  ______________________________________________________________________

  Table of Contents:

  1. Introduction

     1.1 Legalese
     1.2 Obtaining this Document
     1.3 Notation
     1.4 Feedback

  2. R Basics

     2.1 What Is R?
     2.2 What Machines Does R Run on?
     2.3 What Is the Current Version of R?
     2.4 How Can R Be Obtained?
     2.5 How Can R Be Installed?
        2.5.1 How Can R Be Installed (Unix)
        2.5.2 How Can R Be Installed (Windows)
        2.5.3 How Can R Be Installed (Macintosh)
     2.6 Are there Unix Binaries for R?
     2.7 Which Documentation Exists for R?
     2.8 Which Mailing Lists Exist for R?
     2.9 What is CRAN?

  3. R and S

     3.1 What Is S?
     3.2 What Is S-PLUS?
     3.3 What Are the Differences between R and S?
        3.3.1 Lexical Scoping
        3.3.2 Models
        3.3.3 Others

  4. R Add-On Packages

     4.1 Which Add-on Packages Exist for R?
     4.2 How Can Add-on Packages Be Installed?
     4.3 How Can Add-on Packages Be Used?
     4.4 How Can Add-on Packages Be Removed?
     4.5 How Can I Create an R Package?
     4.6 How Can I Contribute to R?

  5. R and Emacs

     5.1 Is there Emacs Support for R?
     5.2 Should I Run R from Within Emacs?

  6. R Miscellania

     6.1 How Can I Read a Large Data Set into R?
     6.2 Why Can't R Source a `Correct' File?
     6.3 How Can I Set Components of a List to NULL?
     6.4 How Can I Save My Workspace?
     6.5 How Can I Clean Up My Workspace?
     6.6 How Can I Get `eval' and `D' to Work?
     6.7 Why Do My Matrices Lose Dimensions?
     6.8 How Does Autoloading Work?
     6.9 How Should I Set Options?

  7. Acknowledgments

  ______________________________________________________________________

  1.  Introduction

  This document contains answers to some of the most frequently asked
  questions about R.

  1.1.  Legalese

  This document is free software; you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation; either version 2, or (at your option)
  any later version.

  This document is distributed in the hope that it will be useful, but
  WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  General Public License for more details.

  A copy of the GNU General Public License is available via WWW at
  http://www.gnu.org/copyleft/gpl.html.  You can also obtain it by
  writing to the Free Software Foundation, Inc., 675 Mass Ave,
  Cambridge, MA 02139, USA.

  1.2.  Obtaining this Document

  The latest version of this document is always available from

             http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

  From there, you can also obtain versions converted to plain ASCII
  text, GNU info, DVI, and PostScript, as well as the SGML source used
  for creating all these formats using the SGML-Tools (formerly
  Linuxdoc-SGML) system.

  1.3.  Notation

  Everything should be pretty standard.  `R>' is used for the R prompt,
  and a `$' for the shell prompt (where applicable).

  1.4.  Feedback

  Feedback is of course most welcome.

  In particular, note that I do not have access to Windows or Mac
  systems.  If you have information on these systems that you think
  should be added to this document, please let me know.

  2.  R Basics

  2.1.  What Is R?

  R is a system for statistical computation and graphics.  It consists
  of a language plus a run-time environment with graphics, a debugger,
  access to certain system functions, and the ability to run programs
  stored in script files.

  The design of R has been heavily influenced by two existing languages:
  Becker, Chambers & Wilks' S (see question ``What is S?'') and
  Sussman's Scheme.  Whereas the resulting language is very similar in
  appearance to S, the underlying implementation and semantics are
  derived from Scheme.  See question ``What Are the Differences between
  R and S?'' for a discussion of the differences between R and S.

  R was initially written by Robert Gentleman and Ross Ihaka, who are
  Senior Lecturers at the Department of Statistics of the University of
  Auckland in Auckland, New Zealand.  In addition, a large group of
  individuals has contributed to R by sending code and bug reports.

  Since mid-1997 there has been a core group who can modify the R source
  code CVS archive.  The group currently consists of Peter Dalgaard,
  Robert Gentleman, Kurt Hornik, Ross Ihaka, Thomas Lumley, Martin
  Maechler, Paul Murrell, Heiner Schwarte, and Luke Tierney.

  R has a home page at http://stat.auckland.ac.nz/r/r.html.  It is free
  software distributed under a GNU-style copyleft, and an official part
  of the GNU project (``GNU S'').

  2.2.  What Machines Does R Run on?

  R is being developed for the Unix, Windows and Mac platforms.

  R will configure and build under a number of common Unix platforms
  including dec-alpha-osf, freebsd, hpux, i386-linux (ELF), sgi-irix,
  solaris, and sunos, and according to Jim Lindsey <jlindsey at luc.ac.be>
  also on Mac, Amiga and Atari under m68k-linux.

  If you know about other platforms, please drop me a note.

  2.3.  What Is the Current Version of R?

  The current Unix version is 0.60, the previous version was 0.50.  The
  ``jump'' is due to both a major reorganization of the directory
  structure and the conversion to a new, TeX-like documentation format.
  See the file `CHANGES' in the R distribution for more information.

  With some good luck, the Windows version will soon catch up with the
  Unix version.  The version for the Mac is pre-alpha.

  2.4.  How Can R Be Obtained?

  Sources, binaries and documentation for R can be obtained via CRAN,
  the ``Comprehensive R Archive Network'' (see question ``What is
  CRAN?'').

  2.5.  How Can R Be Installed?

  2.5.1.  How Can R Be Installed (Unix)

  If binaries are available for your platform (see question ``Are there
  Unix Binaries for R?''), you can use these, following the instructions
  that come with them.

  Otherwise, you can compile and install R yourself, which can be done
  very easily under a number of common Unix platforms (see question
  ``What Machines Does R Run on?'').  The file INSTALL that comes with
  the R distribution contains instructions.

  Choose a place to install the R tree (R is not just a binary, but has
  additional data sets, help files, font metrics etc).  Let's call this
  place RHOME (given appropriate permissions, a natural choice would be
  `/usr/local/lib/R').  Untar the source code, and issue the following
  commands (at the shell prompt):

       $ ./configure
       $ make

  If these commands execute successfully, the R binary will be copied to
  the `$RHOME/bin' directory.  In addition, a shell script font-end
  called `R' will be created and copied to the same directory.  You can
  copy this script to a place where users can invoke it, for example to
  `/usr/local/bin'.  You could also copy the man page `R.1' to a place
  where your man reader finds it, such as `/usr/local/man/man1'.

  Using

       $ make docs

  will build preformatted plain text help pages as well as HTML and
  LaTeX versions of the documentation (the three kinds can also be gen-
  erated separately using make help, make html and make latex).  Note
  that as of R version 0.60, you need Perl version 5 to build the docu-
  mentation.  If this is not available on your system, you can obtain
  precompiled documentation files via CRAN.

  If everything (including docs) built properly (and you do not want to
  apply patches in the future), you can safely do rm -rf src to free
  disk space.

  2.5.2.  How Can R Be Installed (Windows)

  The file `rsept.zip' from the `bin/ms-windows' directory of a CRAN
  site contains a binary Windows 95 distribution for R which should be
  about a 0.50a4 release (plus a few features from 0.60).  This version
  is quite limited in Windows-specific features, although it has been
  reported to work rather nicely.

  The file `rseptbeta.zip' contains the same version with a few bugs
  fixed and some experimental code for dynamic loading of DLL files.
  The survival4 packages is included but it currently does not work.

  These versions also work on NT4.0, both server and workstation.

  The file `rsept31.zip' contains a version compiled for Windows 3.11.
  There have been mixed reports regarding this one, some get it going
  with a few inconsequential error messages on startup, others seem to
  be getting absolutely nowhere with it.  It will definitely not run
  without a version of Win32s installed, available free of charge from
  Microsoft (ftp://ftp.microsoft.com/Softlib/MSLFILES/pw1118.exe).  For
  reasons related to the lack of long filenames, the HTML help files
  cannot work and are not included.

  Note that when uncompressing the zip files, the pkunzip program needs
  to be invoked with the -D flag to create subdirectories.  Also, be
  aware that some decompression programs do not preserve long file names
  properly.

  2.5.3.  How Can R Be Installed (Macintosh)

  The CRAN `bin/macintosh' directory contains `R.sea.hqx', a binhexed
  self-extracting archive, and installation instructions in
  `README.MACINTOSH'.  Note that the version in it is nowhere near the
  quality of the current Unix version.

  The Power Macintosh port is temporarily on hold.

  2.6.  Are there Unix Binaries for R?

  Packages ready for installation under the i386 versions of Debian
  GNU/Linux and Red Hat Linux, respectively, can be found at CRAN in
  `bin/i386-linux'.  There are also `tar' distributions for NEXTSTEP on
  the i386 and m68k platforms in `bin/i386-nextstep' and `bin/m68k-
  nextstep'.  No others binary distributions have thus far been made
  publically available.

  2.7.  Which Documentation Exists for R?

  Online documentation for most of the functions and variables in R
  exists, and can be printed on-screen by typing help(name) (or ?name)
  at the R prompt, where name is the name of the topic help is sought
  for.  (In the case of unary and binary operators and control-flow
  special forms, the name may need to be be quoted.)

  This documentation can also be made available as HTML, and as hardcopy
  via LaTeX, see question ``How Can R Be Installed?''.  An up-to-date
  HTML version is always available for web browsing at

                  http://www.stat.math.ethz.ch/R/manual/

  An R manual (``Notes on R:  A Programming Environment for Data
  Analysis and Graphics'') is currently being written, based on the
  ``Notes on S-PLUS'' by Bill Venables <venables at stats.adelaide.edu.au>
  and David Smith <D.M.Smith at lancaster.ac.uk>.  The current version can
  be obtained as `Rnotes.tgz' (LaTeX source) in a CRAN `doc' directory.
  Note that the ``conversion'' from S(-PLUS) to R is not complete yet.

  Last, but not least, Ross' and Robert's experience in designing and
  implementing R is described in:

  @Article{,
    author =       {Ross Ihaka and Robert Gentleman},
    title =        {R: A Language for Data Analysis and Graphics},
    journal =      {Journal of Computational and Graphical Statistics},
    year =         1996,
    volume =       5,
    number =       3,
    pages =        {299--314}
  }

  This is also the reference for R to use in publications.

  2.8.  Which Mailing Lists Exist for R?

  Thanks to Martin Maechler <maechler at stat.math.ethz.ch>, there are
  three mailing lists devoted to R.

     r-announce
        This list is for announcements about the development of R and
        the availability of new code.

     r-devel
        This list is for discussions about the future of R and pre-
        testing of new versions.  It is meant for those who maintain an
        active position in the development of R.

     r-help
        The `main' R mailing list, for announcements about the
        development of R and the availability of new code, questions and
        answers about problems and solutions using R, enhancements and
        patches to the source code and documentation of R, comparison
        and compatibility with S and S-plus, and for the posting of nice
        examples and benchmarks.

  Note that the r-announce list is gatewayed into r-help, so you don't
  need to subscribe to both of them.

  To send a message to everyone on the r-help mailing list, send email
  to

                         r-help at stat.math.ethz.ch

  To subscribe (or unsubscribe) to this list send subscribe (or unsub-
  scribe) in the BODY of the message (not in the subject!)  to r-help-
  request at stat.math.ethz.ch.  Information about the list can be obtained
  by sending an email with info as its contens to r-help-
  request at stat.math.ethz.ch.

  Subscription and posting to the other lists is done analogously, with
  `r-help' replaced by `r-announce' and `r-devel', respectively.

  It is recommended that you send mail to r-help rather than only to the
  R developers (who are also subscribed to the list, of course).  This
  may save them precious time they can use for constantly improving R,
  and will typically also result in much quicker feedback for yourself.

  Of course, in the case of bug reports it would be very helpful to have
  code which reliably reproduces the problem.  Also, make sure that you
  include information on the system and version of R being used.

  Archives of the above three mailing lists are made available on the
  net in a monthly schedule at ftp://ftp.stat.math.ethz.ch/Mail-
  archives/ (which is a directory of mail archive files).  Archives of
  the r-help mailing list (including the previous r-testers lists back
  to March 1996), are also available in HTML format at
  http://www.ens.gu.edu.au/robertk/rhelp/about.htm.

  The developers of R can be reached for comments and reports at
  R at stat.auckland.ac.nz.

  2.9.  What is CRAN?

  The ``Comprehensive R Archive Network'' (CRAN) is a collection of
  sites which carry identical material, consisting of the R
  distribution(s), the contributed extensions, documentation for R, and
  binaries.

  The CRAN master site can be found at the URL

                http://www.ci.tuwien.ac.at/R/    (Austria)

  and is currently being mirrored daily at

       http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/    (Italy)
       ftp://ftp.u-aizu.ac.jp/pub/lang/R/CRAN/              (Japan)
       ftp://ftp.stat.math.ethz.ch/R-CRAN/                  (Switzerland)
       http://lib.stat.cmu.edu/R/CRAN/                      (USA/Pennsylvania)
       ftp://ftp.biostat.washington.edu/mirrors/R/CRAN/     (USA/Washington)
       ftp://franz.stat.wisc.edu/pub/R/                     (USA/Wisconsin)

  Please use the CRAN site closest to you to reduce network load.

  The structure of the CRAN tree is as follows.

     `src/base'
        contains the official R distribution as provided by Ross Ihaka
        and Robert Gentleman.

     `src/contrib'
        contains code for extension packages.

     `doc'
        is for additional documentation and information on R.

     `bin'
        is for prebuilt R binaries (the base distribution and
        extensions), grouped according to platforms.  Currently, there
        are experimental `.deb' and `.rpm' packages for i386-linux, and
        tar files for i386-nextstep and m68k-nextstep.  I hope that
        `.tar.gz' files with contents relative to an installation tree
        (e.g. `bin', `lib/R/', and `man/man1/R.1') can be made available
        soon for all major supported Unix platforms.

  To ``submit'' something to CRAN, simply upload it to
  ftp://ftp.ci.tuwien.ac.at/incoming and send an email to
  <wwwadmin at ci.tuwien.ac.at>.  Please indicate the copyright situation
  (GPL, ...) in your submission.

  3.  R and S

  3.1.  What Is S?

  S is a very high level language and an environment for data analysis
  and graphics.  S was written by Richard A. Becker, John M. Chambers,
  and Allan R. Wilks of AT&T Bell Laboratories Statistics Research
  Department.

  The primary references for S are two books by the creators of S.

  o  Richard A. Becker, John M. Chambers and Allan R. Wilks (1988),
     ``The New S Language,'' Chapman & Hall, London.

     This book is often called the ``Blue Book''.

  o  John M. Chambers and Trevor J. Hastie (1992), ``Statistical Models
     in S,'' Chapman & Hall, London.

     This is also called the ``White Book''.

  There is a huge amount of user-contributed code for S, available at
  the S Repository at CMU.

  See the ``Frequently Asked Questions about S''
  (http://lib.stat.cmu.edu/S/faq) for further information about S.

  3.2.  What Is S-PLUS?

  S-PLUS is a value-added version of S sold by Statistical Sciences,
  Inc. (now a division of Mathsoft, Inc.)  S is a subset of S-PLUS, and
  hence anything which may be done in S may be done in S-PLUS.  In
  addition S-PLUS has extended functionality in a wide variety areas,
  including robust regression, modern nonparametric regression, time
  series, survival analysis, multivariate analysis, classical
  statistical tests, quality control, and graphics drivers.  Add-on
  modules add additional capabilities for wavelet analysis, spatial
  statistics, and design of experiments.

  See the MathSoft S-PLUS page (http://www.mathsoft.com/splus.html) for
  further information.

  3.3.  What Are the Differences between R and S?

  3.3.1.  Lexical Scoping

  Whereas the developers of R have tried to stick to the S language as
  defined in ``The New S Language'' (Blue Book, see question ``What is
  S?''), they have adopted the evaluation model of Scheme.

  This difference becomes manifest when free variables occur in a
  function.  Free variables are those which are neither formal
  parameters (occurring in the argument list of the function) nor local
  variables (created by assigning to them in the body of the function).
  Whereas S (like C) by default uses static scoping, R (like Scheme) has
  adopted lexical scoping.  This means the values of free variables are
  determined by a set of global variables in S, but in R by the bindings
  that were in effect at the time the function was created.

  Consider the following function:

       cube <- function(n) {
         sq <- function() n * n
         n * sq()
       }

  Under S, sq() does not ``know'' about the variable n unless it is
  defined globally:

       S> cube(2)
       Error in sq():  Object "n" not found
       Dumped
       S> n <- 3
       S> cube(2)
       [1] 18

  In R, the ``environment'' created when cube() was invoked is also
  looked in:

       R> cube(2)
       [1] 8

  The following more `realistic' example illustrating the differences in
  scoping is due to Thomas Lumley <thomas at biostat.washington.edu>.  The
  function

       jackknife.lm <- function(lmobj) {
         n <- length(resid(lmobj))
         jval <- t(apply(as.matrix(1:n), 1,
                         function(i) coef(update(lmobj, subset = -i))))
         (n - 1) * (n - 1) * var(jval) / n
       }

  does something useful in R, but does not work in S.  In order to make
  it work in S you need to explicitly pass the linear model object into
  the function nested in apply().  If you don't and you are lucky you
  will get ``Error: Object "lmobj" not found''.  If you are unlucky
  enough to have a linear model called lmobj in your global environment
  you will get the wrong answer with no warning.

  The following version works in S.

       jackknife.S.lm <- function(lmobj) {
         n <- length(resid(lmobj))
         jval <- t(apply(as.matrix(1:n), 1,
                         function(i, lmobj) coef(update(lmobj, subset = -i)),
                         lmobj = lmobj))
         (n - 1) * (n - 1) * var(jval) / n
       }

  (The S version was written independently by Thomas and at least three
  of his fellow students over the past couple of years, causing liter-
  ally hours of confusion on each occasion.)

  Similarly, most optimization (or zero-finding) routines need some
  arguments to be optimized over and have other parameters that depend
  on the data but are fixed with respect to optimization.  With R
  scoping rules, this is a trivial problem; simply make up the function
  with the required definitions in the same environment and scoping
  takes care of it.  With S, one solution is to add an extra parameter
  to the function and to the optimizer to pass in these extras, which
  however can only work if the optimizer supports this (and typically,
  the builtin ones do not).

  Lexical scoping allows using function closures and maintaining local
  state.  A simple example (taken from Abelson and Sussman) can be found
  in the `demos/language' subdirectory of the R distribution.  Further
  information is provided in the standard R reference ``R: A Language
  for Data Analysis and Graphics'' (see question ``Which Documentation
  Exists for R?'') and a paper on ``Lexical Scope and Statistical
  Computing'' by Robert Gentleman and Ross Ihaka which can be obtained
  from the `doc/misc' directory of a CRAN site.

  Lexical scoping also implies a further major difference.  Whereas S
  stores all objects as separate files in a directory somewhere (usually
  `.Data' under the current directory), R does not.  All objects in R
  are stored internally.  When R is started up it grabs a very large
  piece of memory and uses it to store the objects.  R performs its own
  memory management of this piece of memory.  Having everything in
  memory is necessary because it is not really possible to externally
  maintain all relevant ``environments'' of symbol/value pairs.  This
  difference also seems to make R much faster than S.

  The down side is that if R crashes you will lose all the work for the
  current session.  Saving and restoring the memory ``images'' (the
  functions and data stored in R's internal memory at any time) can be a
  bit slow, especially if they are big.  In S this does not happen,
  because everything is saved in disk files and if you crash nothing is
  likely to happen to them.  R is still in a beta stage, and may crash
  from time to time.  Hence, for important work you should consider
  saving often, see question ``How Can I Save My Workspace?'' (other
  possibilities are logging your sessions, or have your R commands
  stored in text files which can be read in using source()).  (Note that
  if you run R from within Emacs (see question ``R and Emacs''), you can
  save the contents of the interaction buffer to a file and conveniently
  manipulate it using ess-transcript-mode, as well as save source copies
  of all functions and data used.)

  3.3.2.  Models

  There are some differences in the modeling code, such as

  o  Whereas in S, you would use lm(y ~ x^3) to regress y on x^3 and
     lm(y ~ poly(x, 3)) to perform ``cubic'' regression, in R, you have
     to insulate powers of numeric vectors (using I()), i.e., you have
     to use lm(y ~ I(x^3)) and lm(y ~ x + I(x^2) + I(x^3)),
     respectively.

  o  The glm family objects are implemented differently in R and S.  The
     same functionality is available but the components have different
     names.

  o  terms objects are stored differently.  In S a terms object is an
     expression with attributes, in R it is a formula with attributes.
     The attributes have the same names but are mostly stored
     differently.  The major difference in functionality is that a terms
     object is subscriptable in S but not in R.  If you can't imagine
     why this would matter then you don't need to know.

     Also, attr(terms(y~x), "response") give 1 in S and TRUE in R.  In S
     the attribute indicates which column of the model frame will
     contain the response.  In R this always column 1.

  Finally, in R y~x+0 is an alternative to y~x-1 for specifying a model
  with no intercept.  Models with no parameters at all can be specified
  by y~0.

  3.3.3.  Others

  Apart from lexical scoping and its implications, R follows the S
  language definition in the Blue Book as much as possible, and hence
  really is an ``implementation'' of S.  There are some intentional
  differences where the behavior of S is considered ``not clean''.  In
  general, the rationale is that R should help you detect programming
  errors, while at the same time being as compatible as possible with S.

  Some known differences are the following.

  o  In R, if x is a list, then x[sub] <- NULL and x[[sub]] <- NULL
     remove the specified elements from x.  The first of these is
     incompatible with S, where it is a no-op.

  o  In S, the functions named .First and .Last in the `.Data' directory
     can be used for customizing, as they are executed at the very
     beginning and end of a session, respectively.

     R looks for files called `.Rprofile' in the user's home directory
     and the current directory, and sources these.  It also loads a
     saved image from `.RData' in case there is one.  If a .First()
     function exists then, it is executed.  The .Last mechanism is not
     supported yet.

  o  In R, the .First.lib mechanism when loading add-on packages using
     library() is not yet supported.

  o  In R, dyn.load() can only load shared libraries, as created for
     example by `R SHLIB'.

  o  R presently does not support IEEE Inf and NaN.

  o  Whereas in S, abs(z) is the same as Mod(z) for complex z, in R you
     must use Mod(z), since abs() is a function of real numbers only.

  o  In R, attach() currently only works for lists and data frames (not
     for directories).  Also, you cannot attach at position 1.

  o  Categories do not exist in R, and never will as they are deprecated
     now in S.  Use factors instead.

  o  In R, For() loops are not necessary and hence not supported.

  o  In R, assign() uses the argument envir= rather than where= as in S.

  o  The random number generators are different, and the seeds have
     different length.

  o  R uses only double precision and so can only pass numeric arguments
     to C/FORTRAN subroutines as double * or DOUBLE PRECISION,
     respectively.

  o  R does not allow indexing beyond the end of an array.  E.g., if x
     is a vector of length 5, both x[6] and x[-6] return an error
     (``subscript out of bounds'').  This is a feature, as the R
     developers feel that indexing beyond array bounds causes bugs in
     code that are hard to find and in lots of cases only subtly wrong,
     and typically manifest themselves when least needed.

     As another example, suppose that DF is a data frame and you want to
     add a new variable VAR named x to it.  In S, you can do DF[["x"]]
     <- VAR.  In R, this is not possible; you can use DF$"x" <- VAR or
     DF <- cbind(DF, x = VAR).

  o  R currently does not allow recycling when subscripting with
     logicals.  E.g., x <- 1:5; x[c(F, T)] currently gives an error.
     This is a bug and will be fixed soon.

  There are also differences which are not intentional, and result from
  missing or incorrect code in R.  The developers would appreciate
  hearing about any deficiencies you may find (in a written report fully
  documenting the difference as you see it).  Of course, it would be
  useful if you were to implement the change yourself and make sure it
  works.

  4.  R Add-On Packages

  4.1.  Which Add-on Packages Exist for R?

  The R distribution comes with the following extra packages:

     eda
        Exploratory Data Analysis.  Currently only contains functions
        for robust line fitting, and median polish and smoothing.

     mva
        Multivariate Analysis.  Currently contains code for principal
        components (prcomp), canonical correlations (cancor),
        hierarchichal clustering (hclust), and metric multidimensional
        scaling (cmdscale).  More functions for clustering and scaling,
        biplots, profile and star plots, and code for ``real''
        discriminant analysis will be added soon.
  The following packages are available from the CRAN `src/contrib' area.
  Note that R 0.60 has brought a change in both organization of package
  sources and documentation format, and that some of the packages below
  may not yet have been updated accordingly.

     acepack
        ace (Alternating Conditional Expectations) and avas (Additivity
        and VAriance Stabilization for regression) for selecting
        regression transformations.

     bootstrap
        Software (bootstrap, cross-validation, jackknife), data and
        errata for the book ``An Introduction to the Bootstrap'' by B.
        Efron and R. Tibshirani, 1993, Chapman and Hall.

     class
        Functions for classification (k-nearest neighbor and LVQ).

     clus
        Functions for cluster analysis.

     ctest
        A collection of classical tests, including the Bartlett, Fisher,
        Kruskal-Wallis, Kolmogorov-Smirnov, and Wilcoxon tests.

     date
        Functions for dealing with dates.  The most useful of them
        accepts a vector of input dates in any of the forms 8/30/53,
        30Aug53, 30 August 1953, ..., August 30 53, or any mixture of
        these.

     e1071
        Miscellaneous functions used at the Department of Statistics at
        TU Wien (E1071).

     fracdiff
        Maximum likelihood estimation of the parameters of a
        fractionally differenced ARIMA(p,d,q) model (Haslett and
        Raftery, Applied Statistics, 1989).

     gee
        An implementation of the Liang/Zeger generalized estimating
        equation approach to GLMs for dependent data.

     integrate
        Code for adaptive quadrature.

     jpn
        A function to plot Japan's coast-line and prefecture boundaries.

     leaps
        A package which performs an exhaustive search for the best
        subsets of a given set of potential regressors, using a branch-
        and-bound algorithm, and also performs searches using a number
        of less time-consuming techniques.

     mlbench
        A collection of artificial and real-world machine learning
        benchmark problems, including the Boston housing data.

     nnet
        Software for feed-forward neural networks with a single hidden
        layer and for multinomial log-linear models.

     oz Functions for plotting Australia's coastline and state
        boundaries.

     polynom
        A collection of functions to implement a class for univariate
        polynomial manipulations.

     ratetables
        US national and state mortality data (requires survival4 and
        date).

     rational
        A few small functions to find numerical rational approximations
        using a continued fraction method.

     snns
        An R interface to the Stuttgart Neural Networks Simulator
        (SNNS).

     splines
        Regression spline functions.

     survival4
        Functions for survival analysis (requires splines).

     wavethresh
        Code for doing wavelet transforms and thresholding in 1 and 2D.

     xgobi
        Interface to the XGobi program for graphical data analysis.

  See CRAN `src/contrib/INDEX' for more information.

  Paul Gilbert <pgilbert at bank-banque-canada.ca> will make an R version
  of his package DSE (Dynamic Systems Estimation) shortly after the 0.60
  release.  The package provides state-space models and the Kalman
  filter, VARMA and cointegration models, and numerical differentiation.
  Also, it can do various rational expectation models via an interface
  to run Troll (a commercially available product) from R.  According to
  Paul, the PADI interface from the Bank of Canada also works with minor
  changes.  PADI can be used to access Fame time series data bases and
  potentially other databases, even remotely over the Internet.  For
  further information see http://www.bank-banque-canada.ca/pgilbert.

  Harald Fekjaer <hfe at math.uio.no> has written addreg, a package for
  additive hazards regression, which can be obtained from
  http://www.med.uio.no/imb/stat/addreg/.

  More code has been posted to the r-help mailing list, and can be
  obtained from the mailing list archive.

  4.2.  How Can Add-on Packages Be Installed?

  (Unix only.)  The add-on packages on CRAN come as gzipped tar files.
  ``Unpack'' the package (in a directory that you may write to).  If you
  have GNU tar, you can use tar zxf name, otherwise you can do something
  like gunzip -c name | tar xf -.  Let pkg be the name of the directory
  thus created.  To install the package to the default R directory tree
  (the `library' subdirectory of `RHOME'), type

       $ R INSTALL pkg

  at the shell prompt.  To install to another tree (e.g., your private
  one), use

       $ R INSTALL pkg lib

  where lib gives the path to the library tree to install to.

  You can use several library trees of add-on packages.  The easiest way
  to tell R to use these is via the environment variable RLIBS which
  should be a colon-separated list of directories at which R library
  trees are rooted.  You do not have to specify the default tree in
  RLIBS.  E.g., to use a private tree in `$HOME/lib/R' and a public
  site-wide tree in `/usr/local/lib/R/site', put

       RLIBS="$HOME/lib/R:/usr/local/lib/R/site"; export RLIBS

  into your (Bourne) shell profile.

  4.3.  How Can Add-on Packages Be Used?

  To find out which additional packages are available on your system,
  type

       library()

  at the R prompt.

  This produces something like

  Packages in `/home/me/lib/R':

  mystuff      My own R functions, nicely packaged and not documented

  Packages in `/usr/local/lib/R/library':

  acepack      ace() and avas() for selecting regression transformations
  bootstrap    Functions for the book "An Introduction to the Bootstrap"
  ctest        Classical Tests
  date         Functions for handling dates
  eda          Exploratory Data Analysis
  fracdiff     Fractionally differenced ARIMA(p,d,q) models
  gee          Generalized Estimating Equation models
  mva          Classical Multivariate Analysis
  splines      Regression spline functions
  survival4    Survival analysis (needs `splines')

  You can ``load'' the installed package name by

       library(name)

  You can then find out which functions it provides by typing one of

       help(package = name)
       library(help = name)

  You can unload the loaded package name by

       detach("package:name")

  4.4.  How Can Add-on Packages Be Removed?

  To remove the package pkg from the default library or the library lib,
  do

       $ R REMOVE pkg

  or

       $ R REMOVE pkg lib

  respectively.

  4.5.  How Can I Create an R Package?

  A package consists of a subdirectory containing a `TITLE' and `INDEX'
  file, and subdirectories `R', `man' and optionally `src', `src-c', and
  `data'.

  The `TITLE' file contains a line giving the name of the package and a
  brief description.  `INDEX' contains a line for each sufficiently
  interesting object in the package, giving its name and a description
  (functions such as print methods not usually called explicitly might
  not be included).
  The `R' subdirectory contains R code files with names beginning with
  lowercase letters.  One of these should use library.dynam() to load
  any necessary compiled code.  The `man' subdirectory should contain R
  documentation files for the objects in the package.

  Source and a Makefile for the compiled code is in `src', and a pure C
  version of the source should be in `src-c'.  In the common case when
  all the source is in C it may be convenient to make one of these
  directories a symbolic link to the other.  The `Makefile' will be
  passed various machine-dependent compile and link flags, examples of
  which can be seen in the `eda' package.

  Finally, the `data' subdirectory is for additional data files the
  package makes available for loading using data().  Note that (at least
  currently) all such files are in fact R code files, and must have the
  extension `.R'.

  See the documentation for library() for more information.

  The web page http://www.biostat.washington.edu/~thomas/Rlib.html
  maintained by Thomas Lumley provides information on porting S packages
  to R.

  4.6.  How Can I Contribute to R?

  R is currently still in alpha (or pre-alpha) state, so simply using it
  and communicating problems is certainly of great value.

  One place where functionality is still missing is the modeling
  software as described in ``Statistical Models in S'' (see question
  ``What is S?'').  The functions

        add1 kappa alias labels drop1 proj

  are missing; many of these are interpreted functions so anyone that is
  bored and wants to have a go at implementing them it would be appreci-
  ated.  In addition, only linear and generalized linear models are cur-
  rently available, aov, gam, loess, tree, and the nonlinear modelling
  code are not there yet.

  See also the `PROJECTS' file in the top level R source directory.

  Many of the packages available at the Statlib S Repository might be
  worth porting to R.

  If you are interested in working on any of these projects, please
  notify Kurt Hornik.

  5.  R and Emacs

  5.1.  Is there Emacs Support for R?

  There is an Emacs-Lisp interface for interactive statistical
  programming and data analysis called ESS (``Emacs Speaks
  Statistics'').  Languages supported include: S dialects (S 3/4, S-PLUS
  3.x, and R), LispStat dialects (XLispStat, ViSta), and SAS.  Stata and
  SPSS dialect (SPSS, Fiasco) support is being examined for possible
  future implementation (a preliminary Stata mode is distributed).

  ESS grew out of the desire for bug fixes and extensions to S-mode-4.8
  (which was a GNU Emacs interface to S/S-PLUS version 3 only).  In
  particular, XEmacs support as well as extensions to incorporate R were
  desired.  In addition, with new modes being developed for R, Stata,
  and SAS, it was felt that providing for a unifying framework would
  eliminate differences in the user interface, as well as to provide for
  faster development of production tools and statistical analysis.  5.0
  has, for its guts, the basic framework from S-mode.  However, it has
  been cleaned, streamlined, brought closer to conformance as a standard
  GNU Emacs package, and redesigned for modularity and reuse.

  R support contains code for editing R source code (syntactic
  indentation and highlighting of source code, partial evaluations of
  code, loading and error-checking of code, and source code revision
  maintenance) and documentation (including sending examples to a
  running R process and previewing), interacting with an inferior R
  process from within Emacs (command-line editing, searchable command
  history, command-line completion of R object and file names, quick
  access to object and search lists, transcript recording, and an
  interface to the help system), and transcript manipulation (in
  particular for re-evaluating commands from transcript files).

  The latest versions of ESS are always available by WWW from

                   http://franz.stat.wisc.edu/pub/ESS/

  or ftp://franz.stat.wisc.edu/pub/ESS/, or via CRAN.  The HTML version
  of the documentation can be found at
  http://www.stat.math.ethz.ch/ESS/.

  ESS comes with detailed installation instructions.

  5.2.  Should I Run R from Within Emacs?

  Yes, definitely.  Inferior R mode provides a readline/history
  mechanism, object name completion, and syntax-based highlighting of
  the interaction buffer using Font Lock mode, as well as a very
  convenient interface to the R help system.

  Of course, it also integrates nicely with the mechanisms for editing R
  source using Emacs.  One can write code in one Emacs buffer and send
  whole or parts of it for execution to R; this is helpful for both data
  analysis and programming.  One can also seamlessly integrate with a
  revision control system, in order to maintain a log of changes in your
  programs and data, as well as to allow for the retrieval of past
  versions of the code.

  In addition, it allows you to keep a record of your session, which can
  also be used for error recovery through the use of the transcript
  mode.

  6.  R Miscellania

  6.1.  How Can I Read a Large Data Set into R?

  R (currently) uses a static memory model.  This means that when it
  starts up, it asks the operating system to reserve a fixed amount of
  memory for it.  The size of this chunk cannot be changed subsequently.
  Hence, it can happen that not enough memory was allocated.

  In these cases, you should restart R with more memory available, using
  the command line options -n and -v.  To understand these options, one
  needs to know that R maintains separate areas for fixed and variable
  sized objects.  The first of these is allocated as an array of ``cons
  cells'' (Lisp programmers will know what they are, others may think of
  them as the building blocks of the language itself, parse trees,
  etc.), and the second are thrown on a ``heap''.  The -n option can be
  used to specify the number of cons cells (each occupying 16 bytes)
  which R is to use (the default is 200000), and the -v option to
  specify the size of the vector heap in megabytes (the default is 2).
  Only integers are allowed for both options.

  E.g., to read in a table of 5000 observations on 40 numeric variables,
  R -v 6 should do.

  Note that the information on where to find vectors and strings on the
  heap is stored using cons cells.  Thus, it may also be necessary to
  allocate more space for cons cells in order to perform computations
  with very ``large'' variable-size objects.

  You can find out the current memory consumption (the proportion of
  heap and cons cells used) by typing gc() at the R prompt.  This may
  help you in finding out whether to increase -v or -n.  Note that
  following gcinfo(TRUE), automatic garbage collection always prints
  memory use statistics.

  When using read.table(), the memory requirements are in fact higher
  than anticipated, because the file is first read in as one long string
  which is then split again.  Use scan() if possible in case you run out
  of memory when reading in a large table.

  6.2.  Why Can't R Source a `Correct' File?

  R sometimes has problems parsing a file which does not end in a
  newline.  This can happen for example when Emacs is used for editing
  the file and next-line-add-newlines is set to nil.  To avoid the
  problem, either set require-final-newline to a non-nil value in one of
  your Emacs startup files, or make sure R-mode (see question ``Is there
  Emacs Support for R?'') is used for editing R source files (which
  locally ensures this setting).

  Earlier R versions had a similar problem when reading in data files,
  but this should have been taken care of now.

  6.3.  How Can I Set Components of a List to NULL?

  You can use

       x[i] <- list(NULL)

  to set component i of the list x to NULL, similarly for named compo-
  nents.  Do not set x[i] or x[[i]] to NULL, because this will remove
  the corresponding component from the list.

  For dropping the row names of a matrix x, it may be easier to use
  rownames(x) <- NULL, similarly for column names.

  6.4.  How Can I Save My Workspace?

  The expression

       save(list = ls(), file = ".RData")

  saves the objects in the currently active environment (typically the
  user's .GlobalEnv) to the file `.RData' in the R startup directory.

  6.5.  How Can I Clean Up My Workspace?

  To remove all objects in the currently active environment (typically
  the user's .GlobalEnv), you can do

       rm(list = ls())

  6.6.  How Can I Get `eval' and `D' to Work?

  Strange things will happen if you use eval(print(x), envir = e) or
  D(x^2, "x").  The first one will either tell you that "x" is not
  found, or print the value of the wrong x.  The other one will likely
  return zero if x exists, and an error otherwise.

  This is because in both cases, the first argument is evaluated in the
  calling environment first.  The result (which should be an object of
  mode `expression' or `call') is then evaluated or differentiated.
  What you (most likely) really want is obtained by ``quoting'' the
  first argument upon surrounding it with expression().  For example,

         R> D(expression(x^2),"x")
         2 * x

  Although this behavior may initially seem to be rather strange, is
  perfectly logical.  The ``intuitive'' behaviour could easily be
  implemented, but problems would arise whenever the expression is
  contained in a variable, passed as a parameter, or is the result of a
  function call.  Consider for instance the semantics in cases like

         D2 <- function(e, n) D(D(e, n), n)

  or

         g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
         g(a * b)

  See the help pages for more examples.

  6.7.  Why Do My Matrices Lose Dimensions?

  When a matrix with a single row or column is created by a subscripting
  operation, e.g., row <- mat[2, ], it is by default turned into a
  vector.  In a similar way if an array with dimension, say, 2x3x1x4 is
  created by subscripting it will be coerced into a 2x3x4 array, losing
  the unnecessary dimension.  After much discussion this has been
  determined to be a feature.

  To prevent this happening, add the option `drop = FALSE' to the
  subscripting. For example,

         rowmatrix <- mat[2, , drop = F]  # creates a row matrix
         colmatrix <- mat[, 2, drop = F]  # creates a column matrix
         a <- b[1, 1, 1, drop = F]        # creates a 1x1x1 array

  The `drop = F' option should be used defensively when programming.
  For example, the statement

         somerows <- mat[index, ]

  will return a vector rather than a matrix if index happens to have
  length 1, causing errors later in the code.  It should probably be
  rewritten as

         somerows <- mat[index, , drop = F]

  6.8.  How Does Autoloading Work?

  R has a special environment called `.AutoloadEnv'.  Using
  autoload(name, pkg), where name and pkg are strings giving the names
  of an object and the package containing it, stores some information in
  this environment.  When R tries to evaluate name, it loads the
  corresponding package pkg and reevaluates name in the new package's
  environment.

  Using this mechanism makes R behave as if the package was loaded, but
  does not occupy memory (yet).

  See the help page for autoload() for a very nice example.

  6.9.  How Should I Set Options?

  The function options() allows setting and examining a variety of
  global ``options'' which affect the way in which R computes and
  displays its results.  The variable .Options holds the current values
  of these options, but should never directly be assigned to unless you
  want to drive yourself crazy---simply pretend that it is a ``read-
  only'' variable.

  For example, given

       test1 <- function(x = pi, dig = 3) {
         oo <- options(digits = dig); on.exit(options(oo));
         cat(.Options$digits, x, "\n")
       }
       test2 <- function(x = pi, dig = 3) {
         .Options$digits <- dig
         cat(.Options$digits, x, "\n")
       }

  we obtain:

       R> test1()
       3 3.14
       R> test2()
       3 3.141593

  What is really used is the global value of .Options, and using
  options(OPT = VAL) correctly updates it.  Local copies of .Options,
  either in .GlobalEnv or in a function environment (frame), are just
  silently disregarded.

  7.  Acknowledgments

  Of course, many many thanks to Robert and Ross for the R system, and
  to the package writers and porters for adding to it.

  Special thanks go to Peter Dalgaard, Paul Gilbert, Fritz Leisch, Jim
  Lindsey, Thomas Lumley, Martin Maechler, Anthony Rossini, and Andreas
  Weingessel for their comments which helped me improve this FAQ.

  More to some soon ...

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From Friedrich.Leisch at ci.tuwien.ac.at  Tue Dec  9 19:21:16 1997
From: Friedrich.Leisch at ci.tuwien.ac.at (Friedrich Leisch)
Date: Tue, 9 Dec 1997 19:21:16 +0100
Subject: R-beta: R FAQ v0.60
In-Reply-To: <x24t4i9yjz.fsf@blueberry.kubism.ku.dk>
References: <199712091748.SAA29807@aragorn.ci.tuwien.ac.at>
	<x24t4i9yjz.fsf@blueberry.kubism.ku.dk>
Message-ID: <199712091821.TAA08749@galadriel.ci.tuwien.ac.at>

>>>>> On 09 Dec 1997 19:11:28 +0100,
>>>>> Peter Dalgaard BSA (PDB) wrote:

PDB> Kurt Hornik <Kurt.Hornik at ci.tuwien.ac.at> writes:
>> code CVS archive.  The group currently consists of Peter Dalgaard,
>> Robert Gentleman, Kurt Hornik, Ross Ihaka, Thomas Lumley, Martin
>> Maechler, Paul Murrell, Heiner Schwarte, and Luke Tierney.

PDB> aaaand Beetlebum... (old Spike Jones number)

PDB> It seem Friedrich Leisch fell off the list - again!

Well, I'm just sitting next door to Kurt, so I'm easy to forget ... :-)

fritz
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From alchemy at inconnect.com  Thu Dec 11 08:53:27 1997
From: alchemy at inconnect.com (Anthony Chavez)
Date: Thu, 11 Dec 1997 00:53:27 -0700 (MST)
Subject: R-beta: time series structures
In-Reply-To: <348F139A.8032984A@stat.unipg.it>
Message-ID: <Pine.SOL.3.96.971211005155.12804C-100000@ultra1>

Please edit headers and keep non-announce topic off the R-announce list.  Some
of us are subscribed to R-announce for a *reason*.

--
-------------------------------------------------------------------------------
Anthony Chavez                 o   \o/  _ o          o    o-o  +===+  o   +===+
                              /|\   |    /\    __\o o_|   \ /  |   | /|\  |   |
alchemy at inconnect.com         / \  / \  | \   /) |   \\o  \|   |~~~| Co-"=|~~~|
Salt Lake City, Utah          o-o  o-o  o-o  o-o        \  o\  |___| / \  |___|

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From p.dalgaard at biostat.ku.dk  Sun Dec 21 21:00:08 1997
From: p.dalgaard at biostat.ku.dk (Peter Dalgaard BSA)
Date: 21 Dec 1997 21:00:08 +0100
Subject: New version available
Message-ID: <x2sormtqk7.fsf@blueberry.kubism.ku.dk>

A new version is available from
ftp.stat.auckland.ac.nz:/pub/R/R-0.61.tgz
and soon from CRAN mirrors everywhere.

As usual, there is at least one unfixed bug in the release, but this
time, we hope that we have set things up so that future bugfixes can
be released more or less immediately.

Merry Christmas!

The R core team.

**********************************************************************
Here's the top of the CHANGES file:

                CHANGES IN VERSION R VERSION 0.61

We try to make development more flexible by creating a "CVS branch".
This should make it easier to produce patches for obvious bugs in the
releases, without having to wait for changes in other areas to
stabilize.

NEW FEATURES

    o   New functions "all.vars" and "all.names" added.

    o   There has been a small change in the include file structure.
        All include files now live in RHOME/src/include and are
        copied to RHOME/include when needed.

    o   The "noquote" functions are now documented.

    o   A new `language' demo, "is.things", is provided.

    o   symnum(.) function

    o   The files in R/library/base/data have had a .R suffix added.

BUG_FIXES

    o   A nasty bug which showed when attempt was made to create
        a zero length call has been fixed.

    o   model.matrix(.) now allows a  contrasts argument.

    o   barplot(.) now also works for barplot(table(rpois(100,3))).

    o   make clean ; make    now should work;  ./Makefile.in eliminated

    o   format(.) is now generic;  the default method has a `digits' argument.


-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907

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