The R Project SVN – R – Diff – /branches/R-exp-API/FAQ

 R FAQ
 Frequently Asked Questions on R
-Version 0.65-11, 1999/11/10
+Version 0.65-12, 1999/11/15
 Kurt Hornik
 Table of Contents
 *****************
 resulting language is very similar in appearance to S, the underlying
 implementation and semantics are derived from Scheme.  *Note What are the
 differences between R and S?::, for a discussion of the differences between
 R and S.
-   R was initially written by Ross Ihaka <ihaka@stat.auckland.ac.nz> and
+   R was initially written by Ross Ihaka <Ross.Ihaka@r-project.org> and
-Robert Gentleman <rgentlem@stat.auckland.ac.nz>, who are Senior Lecturers
+Robert Gentleman <Robert.Gentleman@r-project.org>, 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 (the "R Core Team") who can
    The `bin/windows/windows-NT' directory of a CRAN site contains the
 latest binary distributions of R for 32 bit versions of MS Windows (i.e.,
 , 98 or NT), as well as binary distributions for a large number of add-on
 packages from CRAN.  The Windows version of R was created by Robert
 Gentleman, and is now being developed and maintained by Guido Masarotto
-<guido@sirio.stat.unipd.it> and Brian D. Ripley <ripley@stats.ox.ac.uk>.
+<Guido.Masarotto@r-project.org> and Brian D. Ripley
+<Brian.Ripley@r-project.org>.
    For most installations the installer `rwinst.exe' will be the easiest
 tool to use.
    See the R Windows FAQ (http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html) for
 Graphical Statistics_, *5*, 299-314.
 .9 What mailing lists exist for R?
 ===================================
-   Thanks to Martin Maechler <maechler@stat.math.ethz.ch>, there are three
+   Thanks to Martin Maechler <Martin.Maechler@r-project.org>, there are
-mailing lists devoted to R.
+three mailing lists devoted to R.
 `r-announce'
      This list is for announcements about the development of R and the
      availability of new code.
      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.
-   Send email to <r-help@stat.math.ethz.ch> to reach everyone on the r-help
+   Send email to <r-help@lists.r-project.org> to reach everyone on the
-mailing list.  To subscribe (or unsubscribe) to this list send `subscribe'
+r-help mailing list.  To subscribe (or unsubscribe) to this list send
-(or `unsubscribe') in the BODY of the message (not in the subject!)  to
+`subscribe' (or `unsubscribe') in the BODY of the message (not in the
-<r-help-request@stat.math.ethz.ch>.  Information about the list can be
+subject!)  to <r-help-request@lists.r-project.org>.  Information about the
-obtained by sending an email with `info' as its contents to
+list can be obtained by sending an email with `info' as its contents to
-<r-help-request@stat.math.ethz.ch>.
+<r-help-request@lists.r-project.org>.
    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
    Archives of the above three mailing lists are made available on the net
 in a monthly schedule via the `doc/mail/mail.html' file in CRAN.  An HTML
 archive of the lists are available via
 `http://www.ens.gu.edu.au/robertk/R/'.
-   The R Core Team can be reached at <r-core@stat.math.ethz.ch> for
+   The R Core Team can be reached at <r-core@lists.r-project.org> for
 comments and reports.
 .10 What is CRAN?
 ==================
    * In S, the index variable in a `for()' loop is local to the inside of
      the loop.  In R it is local to the environment where the `for()'
      statement is executed.
+   * In S, `tapply(simplify=TRUE)' returns a vector where R returns a
+     one-dimensional array (which can have named dimnames).
    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
      $ R INSTALL -l LIB PKGDIR_1 ... PKGDIR_N
 where LIB gives the path to the library tree to install to.
-   As of R version 0.65.1, the need for explicit unpacking for installing
+   As of R version 0.65.1, you no longer need to explicitly unpack for
-will be removed.
+installing; i.e., you can do `R INSTALL /path/to/pkg_ver.tar.gz'.
    You can use several library trees of add-on packages.  The easiest way
 to tell R to use these is via the environment variable `R_LIBS' 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 `R_LIBS'.
 E.g., to use a private tree in `$HOME/lib/R' and a public site-wide tree in
 `/usr/local/lib/R/site', put
      R_LIBS="$HOME/lib/R:/usr/local/lib/R/site"; export R_LIBS
-into your (Bourne) shell profile.
+into your (Bourne) shell profile or your `~/.Renviron' file.
 .3 How can add-on packages be used?
 ====================================
    To find out which additional packages are available on your system, type
    The `data' subdirectory is for additional data files the package makes
 available for loading using `data()'.  Currently, data files can have one
 of three types as indicated by their extension:  plain R code (`.R' or
 `.r'), tables (`.tab', `.txt', or `.csv'), or `save()' images (`.RData' or
+`.rda').  The subdirectory should contain a `00Index' file that describes
-`.rda').
+the datasets available.
    The contents of the `inst' subdirectory will be copied recursively to
 the installation directory.
    Finally, `exec' could contain additional executables the package needs,
 typically Shell or Perl scripts.  This mechanism is currently not used by
 any package, and still experimental.
-   You can also provide shell scripts `configure' and `cleanup' for
+   You can also provide (Bourne) shell scripts `configure' and `cleanup'
-configuration before installation (such as checking for additional software
+for configuration before installation (such as checking for additional
-that might be needed by the package), and removing the files created in
+software that might be needed by the package), and removing the files
-this process.  See the add-on package *e1071* on CRAN for an example of
+created in this process.  See the add-on package *e1071* on CRAN for an
-this mechanism.
+example of this mechanism.
    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.
    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 *Note What is S?::).
+as described in "Statistical Models in S" (see *Note What is S?::);
 Generalized Additive Models (*gam*) and some of the nonlinear modeling code
 are not there yet.
    See also the `PROJECTS' file in the top level R source directory.
    Many (more) 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 <Kurt.Hornik@ci.tuwien.ac.at>.
+Kurt Hornik <Kurt.Hornik@r-project.org>.
 R and Emacs
 *************
 .1 Is there Emacs support for R?
 =================================
-   There is an Emacs-Lisp interface for interactive statistical programming
+   There is an Emacs package which provides a standard interface between
-and data analysis called ESS ("Emacs Speaks Statistics").  Languages
+statistical programs and statistical processes called ESS ("Emacs Speaks
-supported include: S dialects (S 3/4, S-PLUS 3.x, and R), LispStat dialects
+Statistics").  It is intended to provide assistance for interactive
-(XLispStat, ViSta), and SAS.  Stata and SPSS dialect (SPSS, PSPP) support
+statistical programming and data analysis.  Languages supported include: S
-is being examined for possible future implementation (a preliminary Stata
+dialects (S 3/4, S-PLUS 3.x/4.x/5.x, and R), LispStat dialects (XLispStat,
-mode is distributed).
+ViSta), SAS, Stata, SPSS dialects (SPSS, Fiasco) and SCA.
    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
+(which was a GNU Emacs interface to S/S-PLUS version 3 only).  The current
-particular, XEmacs support as well as extensions to incorporate R were
+set of developers desired support for XEmacs, R, S4, and MS Windows.  In
-desired.  In addition, with new modes being developed for R, Stata, and
+addition, with new modes being developed for R, Stata, and SAS, it was felt
-SAS, it was felt that providing for a unifying framework would eliminate
+a unifying interface and framework for the user interface, would benefit
-differences in the user interface, as well as to provide for faster
+both the user and the developer, by helping both groups conform to standard
-development of production tools and statistical analysis.  ESS 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,
+Emacs usage.  The end result is an increase in efficiency for statistical
-and redesigned for modularity and reuse.
+programming and data analysis, over the usual tools.
    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
+documentation (syntactic indentation and highlighting of source code,
-previewing), interacting with an inferior R process from within Emacs
+sending Examples to running ESS process, and previewing), interacting with
+an inferior R process from within Emacs (command-line editing, searchable
-(command-line editing, searchable command history, command-line completion
+command history, command-line completion of R object and file names, quick
-of R object and file names, quick access to object and search lists,
+access to object and search lists, transcript recording, and an interface
-transcript recording, and an interface to the help system), and transcript
+to the help system), and transcript manipulation (recording and saving
-manipulation (in particular for re-evaluating commands from transcript
+transcript files, manipulating and editing saved transcripts, and
-files).
+re-evaluating commands from transcript files).
    The latest versions of ESS are available from
 `http://ess.stat.wisc.edu/pub/ESS/' or `ftp://ess.stat.wisc.edu/pub/ESS/',
 or via CRAN.  The HTML version of the documentation can be found at
 `http://stat.ethz.ch/ESS/'.
 ensures that the site profile and saved data files are not read.
    On Unix systems a bug report can be generated using the function
 `bug.report()'. This automatically includes the version information and
 sends the bug to the correct address.  Alternatively the bug report can be
-emailed to <r-bugs@biostat.ku.dk> or submitted to the Web page at
+emailed to <r-bugs@lists.r-project.org> or submitted to the Web page at
 `http://bugs.r-project.org'.
    Bug reports on contributed packages should perhaps be sent to the
 package maintainer rather than to r-bugs.

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