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File R-release.diff of Package R

# cd *inside* the ...../R-4.4.2/  directory, and do
#      zcat ..../R-release.diff.gz | patch -p1 -E
# ---------                          =====
# You need a "g[un]zip capable zcat", and canNOT use Solaris "patch"!
# Rather get ("gzip" and)  "diffutils" from any GNU mirror.
#
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/SVN-REVISION R-patched/SVN-REVISION
--- R-release-orig/SVN-REVISION	2024-10-31 09:02:38.000000000 +0100
+++ R-patched/SVN-REVISION	2024-11-25 01:32:23.873630939 +0100
@@ -1,2 +1,2 @@
-Revision: 87279
-Last Changed Date: 2024-10-31
+Revision: 87369
+Last Changed Date: 2024-11-24
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/VERSION R-patched/VERSION
--- R-release-orig/VERSION	2024-10-31 09:00:02.000000000 +0100
+++ R-patched/VERSION	2024-11-11 15:37:29.089761415 +0100
@@ -1 +1 @@
-4.4.2
+4.4.2 Patched
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/doc/FAQ R-patched/doc/FAQ
--- R-release-orig/doc/FAQ	2024-10-31 09:02:39.000000000 +0100
+++ R-patched/doc/FAQ	2024-11-25 01:32:24.832636338 +0100
@@ -85,7 +85,7 @@
   7.28 Why is read.table() so inefficient?
   7.29 What is the difference between package and library?
   7.30 I installed a package but the functions are not there
-  7.31 Why doesn’t R think these numbers are equal?
+  7.31 Why doesn't R think these numbers are equal?
   7.32 How can I capture or ignore errors in a long simulation?
   7.33 Why are powers of negative numbers wrong?
   7.34 How can I save the result of each iteration in a loop into a separate file?
@@ -121,8 +121,8 @@
 1.1 Legalese
 ============
 
-Copyright © 1998–2020 Kurt Hornik
-Copyright © 2021–2024 R Core Team
+Copyright © 1998-2020 Kurt Hornik
+Copyright © 2021-2024 R Core Team
 
    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
@@ -151,7 +151,7 @@
 ========================
 
 In publications, please refer to this FAQ as Hornik and R Core Team (2024),
-“The R FAQ”, and give the above, _official_ URL:
+"The R FAQ", and give the above, _official_ URL:
 
      @Misc{,
        author        = {Kurt Hornik and the R Core Team},
@@ -172,9 +172,9 @@
 Feedback via email to <R-devel@R-project.org> is most welcome.
 
    Features specific to the Windows and macOS ports of R are described in
-the “R for Windows FAQ”
-(https://CRAN.R-project.org/bin/windows/base/rw-FAQ.html) and the “R for Mac
-OS X FAQ” (https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html).  If you
+the "R for Windows FAQ"
+(https://CRAN.R-project.org/bin/windows/base/rw-FAQ.html) and the "R for Mac
+OS X FAQ" (https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html).  If you
 have information on Mac or Windows systems that you think should be added to
 this document, please let us know.
 
@@ -190,7 +190,7 @@
 files.
 
    The design of R has been heavily influenced by two existing languages:
-Becker, Chambers & Wilks’ S (*note What is S?::) and Sussman’s Scheme
+Becker, Chambers & Wilks' S (*note What is S?::) and Sussman's Scheme
 (http://community.schemewiki.org/?scheme-faq).  Whereas the resulting
 language is very similar in appearance to S, the underlying implementation
 and semantics are derived from Scheme.  *Note What are the differences
@@ -205,8 +205,8 @@
 models, nonlinear regression models, time series analysis, classical
 parametric and nonparametric tests, clustering and smoothing.  There is also
 a large set of functions which provide a flexible graphical environment for
-creating various kinds of data presentations.  Additional modules (“add-on
-packages”) are available for a variety of specific purposes (*note R Add-On
+creating various kinds of data presentations.  Additional modules ("add-on
+packages") are available for a variety of specific purposes (*note R Add-On
 Packages::).
 
    R was initially written by Ross Ihaka <Ross.Ihaka@R-project.org> and
@@ -215,7 +215,7 @@
 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
+   Since mid-1997 there has been a core group (the "R Core Team") who can
 modify the R source code archive, currently consisting of
      John Chambers,
      Peter Dalgaard,
@@ -243,7 +243,7 @@
    R has a home page at <https://www.R-project.org/>.  It is free software
 (https://www.gnu.org/philosophy/free-sw.html) distributed under a GNU-style
 copyleft (https://www.gnu.org/copyleft/copyleft.html), and an official part
-of the GNU (https://www.gnu.org/) project (“GNU S”).
+of the GNU (https://www.gnu.org/) project ("GNU S").
 
 2.2 What machines does R run on?
 ================================
@@ -265,11 +265,11 @@
 2.3 What is the current version of R?
 =====================================
 
-R uses a ‘major.minor.patchlevel’ numbering scheme.  Based on this, there
-are the current release version of R (‘r-release’) as well as two
+R uses a 'major.minor.patchlevel' numbering scheme.  Based on this, there
+are the current release version of R ('r-release') as well as two
 development versions of R, a patched version of the current release
-(‘r-patched’) and one working towards the next minor or eventually major
-(‘r-devel’) releases of R, respectively.  New features are typically
+('r-patched') and one working towards the next minor or eventually major
+('r-devel') releases of R, respectively.  New features are typically
 introduced in r-devel, while r-patched is for bug fixes mostly.
 
    See <https://CRAN.R-project.org/sources.html> for the current versions of
@@ -279,7 +279,7 @@
 ==========================
 
 Sources, binaries and documentation for R can be obtained via CRAN, the
-“Comprehensive R Archive Network” (see *note What is CRAN?::).
+"Comprehensive R Archive Network" (see *note What is CRAN?::).
 
    Sources are also available via <https://svn.R-project.org/R/>, the R
 Subversion repository, but currently not via anonymous rsync (nor CVS).
@@ -305,7 +305,7 @@
    Otherwise, you can compile and install R yourself, which can be done very
 easily under a number of common Unix-like platforms (see *note What machines
 does R run on?::).  The file ‘INSTALL’ that comes with the R distribution
-contains a brief introduction, and the “R Installation and Administration”
+contains a brief introduction, and the "R Installation and Administration"
 guide (*note What documentation exists for R?::) has full details.
 
    Note that you need a FORTRAN 90 compiler as well as a C compiler to build
@@ -321,7 +321,7 @@
 front-end called ‘R’ are created and copied to the ‘bin’ directory.  You can
 copy the script to a place where users can invoke it, for example to
 ‘/usr/local/bin’.  In addition, HTML versions of the R manuals (e.g.,
-‘R-exts.html’, the “Writing R Extensions” manual) are built in the
+‘R-exts.html’, the "Writing R Extensions" manual) are built in the
 ‘doc/manual’ subdirectory.
 
    Use ‘make pdf’ to build PDF (Portable Document Format) versions of the R
@@ -334,7 +334,7 @@
    Finally, use ‘make check’ to find out whether your R system works
 correctly.
 
-   You can also perform a “system-wide” installation using ‘make install’.
+   You can also perform a "system-wide" installation using ‘make install’.
 By default, this will install to the following directories:
 
 ‘${prefix}/bin’
@@ -342,8 +342,8 @@
 ‘${prefix}/man/man1’
      the man page
 ‘${prefix}/lib/R’
-     all the rest (libraries, on-line help system, ...).  This is the “R
-     Home Directory” (‘R_HOME’) of the installed system.
+     all the rest (libraries, on-line help system, ...).  This is the "R
+     Home Directory" (‘R_HOME’) of the installed system.
 
 In the above, ‘prefix’ is determined during configuration (typically
 ‘/usr/local’) and can be set by running ‘configure’ with the option
@@ -370,19 +370,19 @@
    The same directory has links to snapshots of the r-patched and r-devel
 versions of R.
 
-   See the “R for Windows FAQ”
+   See the "R for Windows FAQ"
 (https://CRAN.R-project.org/bin/windows/base/rw-FAQ.html) for more details.
 
 2.5.3 How can R be installed (Mac)
 ----------------------------------
 
 The ‘bin/macosx’ directory of a CRAN site contains a standard Apple
-installer package to run on macOS 10.13 (‘High Sierra’) or later, and
-another which runs only on ‘Apple Silicon’ Macs under macOS 11 (‘Big Sur’)
+installer package to run on macOS 10.13 ('High Sierra') or later, and
+another which runs only on 'Apple Silicon' Macs under macOS 11 ('Big Sur')
 or later.  Once downloaded and executed, the installer will install the
 current release of R and R.app, the macOS GUI.  This port of R for macOS is
 maintained by Simon Urbanek <Simon.Urbanek@R-project.org> (and previously by
-Stefano Iacus).  The “R for macOS FAQ”
+Stefano Iacus).  The "R for macOS FAQ"
 (https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html) has more details.
 
    Snapshots of the r-patched and r-devel versions of R are available as
@@ -392,7 +392,7 @@
 =======================================
 
 Binary distributions of R are available on many Unix-like OSes: only some
-can be mentioned here so check your OS’s search facilities to see if one is
+can be mentioned here so check your OS's search facilities to see if one is
 available for yours.
 
    The ‘bin/linux’ directory of a CRAN site contains R packages for Debian
@@ -403,7 +403,7 @@
 maintenance tool.  Use e.g. ‘apt-get install r-base r-recommended’ to
 install the R environment and recommended packages.  If you also want to
 build R packages from source, also run ‘apt-get install r-base-dev’ to
-obtain the additional tools required for this.  So-called “backports” of the
+obtain the additional tools required for this.  So-called "backports" of the
 current R packages for at least the “stable” distribution of Debian are
 provided by Johannes Ranke, and available from CRAN.  See
 <https://CRAN.R-project.org/bin/linux/debian/index.html> for details on R
@@ -412,12 +412,12 @@
 Michael Rutter, see <https://CRAN.R-project.org/bin/linux/ubuntu/index.html>
 for instructions.
 
-   R binaries for Fedora, maintained by Tom “Spot” Callaway, are provided as
+   R binaries for Fedora, maintained by Tom "Spot" Callaway, are provided as
 part of the Fedora distribution and can be accessed through ‘yum’, the RPM
-installer/updater.  Note that the “Software” application (gnome-software),
+installer/updater.  Note that the "Software" application (gnome-software),
 which is the default GUI for software installation in Fedora 20, cannot be
 used to install R. It is therefore recommended to use the yum command line
-tool.  The Fedora R RPM is a “meta-package” which installs all the user and
+tool.  The Fedora R RPM is a "meta-package" which installs all the user and
 developer components of R (available separately as ‘R-core’ and ‘R-devel’),
 as well as ‘R-java’, which ensures that R is configured for use with Java.
 The R RPM also installs the standalone R math library (‘libRmath’ and
@@ -451,19 +451,19 @@
 
    The R distribution also comes with the following manuals.
 
-   • “An Introduction to R” (‘R-intro’) includes information on data types,
+   • "An Introduction to R" (‘R-intro’) includes information on data types,
      programming elements, statistical modeling and graphics.  This document
-     is based on the “Notes on S-PLUS” by Bill Venables and David Smith.
-   • “Writing R Extensions” (‘R-exts’) currently describes the process of
-     creating R add-on packages, writing R documentation, R’s system and
+     is based on the "Notes on S-PLUS" by Bill Venables and David Smith.
+   • "Writing R Extensions" (‘R-exts’) currently describes the process of
+     creating R add-on packages, writing R documentation, R's system and
      foreign language interfaces, and the R API.
-   • “R Data Import/Export” (‘R-data’) is a guide to importing and exporting
+   • "R Data Import/Export" (‘R-data’) is a guide to importing and exporting
      data to and from R.
-   • “The R Language Definition” (‘R-lang’), a first version of the
-     “Kernighan & Ritchie of R”, explains evaluation, parsing, object
+   • "The R Language Definition" (‘R-lang’), a first version of the
+     "Kernighan & Ritchie of R", explains evaluation, parsing, object
      oriented programming, computing on the language, and so forth.
-   • “R Installation and Administration” (‘R-admin’).
-   • “R Internals” (‘R-ints’) is a guide to R’s internal structures.  (Added
+   • "R Installation and Administration" (‘R-admin’).
+   • "R Internals" (‘R-ints’) is a guide to R's internal structures.  (Added
      in R 2.4.0.)
 
    An annotated bibliography (BibTeX format) of R-related publications can
@@ -473,44 +473,44 @@
 
    Books on R by R Core Team members include
 
-     John M. Chambers (2008), “Software for Data Analysis: Programming with
-     R”.  Springer, New York, ISBN 978-0-387-75935-7,
+     John M. Chambers (2008), "Software for Data Analysis: Programming with
+     R". Springer, New York, ISBN 978-0-387-75935-7,
      <https://johnmchambers.su.domains/Rbook/>.
 
-     Peter Dalgaard (2008), “Introductory Statistics with R”, 2nd edition.
+     Peter Dalgaard (2008), "Introductory Statistics with R", 2nd edition.
      Springer, ISBN 978-0-387-79053-4,
      <http://publicifsv.sund.ku.dk/~pd/ISwR.html>.
 
-     Robert Gentleman (2008), “R Programming for Bioinformatics”.  Chapman &
+     Robert Gentleman (2008), "R Programming for Bioinformatics".  Chapman &
      Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7,
      <https://bioconductor.org/help/publications/books/r-programming-for-bioinformatics/>.
 
-     Stefano M. Iacus (2008), “Simulation and Inference for Stochastic
-     Differential Equations: With R Examples”.  Springer, New York, ISBN
+     Stefano M. Iacus (2008), "Simulation and Inference for Stochastic
+     Differential Equations: With R Examples".  Springer, New York, ISBN
      978-0-387-75838-1.
 
-     Deepayan Sarkar (2007), “Lattice: Multivariate Data Visualization with
-     R”.  Springer, New York, ISBN 978-0-387-75968-5.
+     Deepayan Sarkar (2007), "Lattice: Multivariate Data Visualization with
+     R". Springer, New York, ISBN 978-0-387-75968-5.
 
-     W. John Braun and Duncan J. Murdoch (2007), “A First Course in
-     Statistical Programming with R”.  Cambridge University Press,
-     Cambridge, ISBN 978-0521872652.
+     W. John Braun and Duncan J. Murdoch (2007), "A First Course in
+     Statistical Programming with R". Cambridge University Press, Cambridge,
+     ISBN 978-0521872652.
 
-     P. Murrell (2005), “R Graphics”, Chapman & Hall/CRC, ISBN
+     P. Murrell (2005), "R Graphics", Chapman & Hall/CRC, ISBN
      1-584-88486-X,
      <https://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html>.
 
-     William N. Venables and Brian D. Ripley (2002), “Modern Applied
-     Statistics with S” (4th edition).  Springer, ISBN 0-387-95457-0,
+     William N. Venables and Brian D. Ripley (2002), "Modern Applied
+     Statistics with S" (4th edition).  Springer, ISBN 0-387-95457-0,
      <https://www.stats.ox.ac.uk/pub/MASS4/>.
 
-     Jose C. Pinheiro and Douglas M. Bates (2000), “Mixed-Effects Models in
-     S and S-Plus”.  Springer, ISBN 0-387-98957-0.
+     Jose C. Pinheiro and Douglas M. Bates (2000), "Mixed-Effects Models in
+     S and S-Plus".  Springer, ISBN 0-387-98957-0.
 
-   Last, but not least, Ross’ and Robert’s experience in designing and
-implementing R is described in Ihaka & Gentleman (1996), “R: A Language for
-Data Analysis and Graphics”, _Journal of Computational and Graphical
-Statistics_, *5*, 299–314 (doi: 10.1080/10618600.1996.10474713
+   Last, but not least, Ross' and Robert's experience in designing and
+implementing R is described in Ihaka & Gentleman (1996), "R: A Language for
+Data Analysis and Graphics", _Journal of Computational and Graphical
+Statistics_, *5*, 299-314 (doi: 10.1080/10618600.1996.10474713
 (https://doi.org/10.1080/10618600.1996.10474713)).
 
 2.8 Citing R
@@ -546,10 +546,10 @@
      A moderated list for announcements on the availability of new or
      enhanced contributed packages.
 ‘R-help’
-     The ‘main’ R mailing list, for discussion about problems and solutions
+     The 'main' R mailing list, for discussion about problems and solutions
      encountered using R, including using R packages in the standard R
-     distribution and on CRAN; announcements (not covered by ‘R-announce’ or
-     ‘R-packages’); the availability of new functionality for R and
+     distribution and on CRAN; announcements (not covered by 'R-announce' or
+     'R-packages'); the availability of new functionality for R and
      documentation of R; and for posting nice examples and benchmarks.
 ‘R-devel’
      This list is for questions and discussion about code development in R.
@@ -595,7 +595,7 @@
 2.10 What is CRAN?
 ==================
 
-The “Comprehensive R Archive Network” (CRAN) is a collection of sites which
+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.
 
@@ -615,7 +615,7 @@
 Windows).  CRAN also provides access to documentation on R, existing mailing
 lists and the R Bug Tracking system.
 
-   Since March 2016, “old” material is made available from a central CRAN
+   Since March 2016, "old" material is made available from a central CRAN
 archive server (<https://CRAN-archive.R-project.org/>).
 
    Please always use the URL of the master site when referring to CRAN.
@@ -632,7 +632,7 @@
 purposes (e.g., in business or in consulting).  The GPL, like all Open
 Source licenses, permits all and any use of the package.  It only restricts
 distribution of R or of other programs containing code from R. This is made
-clear in clause 6 (“No Discrimination Against Fields of Endeavor”) of the
+clear in clause 6 ("No Discrimination Against Fields of Endeavor") of the
 Open Source Definition (https://opensource.org/osd):
 
      The license must not restrict anyone from making use of the program in
@@ -649,7 +649,7 @@
 
    Most add-on packages, including all recommended ones, also explicitly
 allow commercial use in this way.  A few packages are restricted to
-“non-commercial use”; you should contact the author to clarify whether these
+"non-commercial use"; you should contact the author to clarify whether these
 may be used or seek the advice of your legal counsel.
 
    None of the discussion in this section constitutes legal advice.  The R
@@ -660,7 +660,7 @@
 
 The name is partly based on the (first) names of the first two R authors
 (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell
-Labs language ‘S’ (*note What is S?::).
+Labs language 'S' (*note What is S?::).
 
 2.13 What is the R Foundation?
 ==============================
@@ -683,8 +683,8 @@
 to the best in SVN, daily built and checked packages, mailing lists, bug
 tracking, message boards/forums, site hosting, permanent file archival, full
 backups, and total web-based administration.  For more information, see the
-R-Forge web page and Stefan Theußl and Achim Zeileis (2009), “Collaborative
-software development using R-Forge”, _The R Journal_, *1*(1):9–14.
+R-Forge web page and Stefan Theußl and Achim Zeileis (2009), "Collaborative
+software development using R-Forge", _The R Journal_, *1*(1):9-14.
 
 3 R and S
 *********
@@ -707,53 +707,53 @@
    The evolution of the S language is characterized by four books by John
 Chambers and coauthors, which are also the primary references for S.
 
-   • Richard A. Becker and John M. Chambers (1984), “S. An Interactive
-     Environment for Data Analysis and Graphics,” Monterey: Wadsworth and
+   • Richard A. Becker and John M. Chambers (1984), "S. An Interactive
+     Environment for Data Analysis and Graphics," Monterey: Wadsworth and
      Brooks/Cole.
 
-     This is also referred to as the “_Brown Book_”, and of historical
+     This is also referred to as the "_Brown Book_", and of historical
      interest only.
 
-   • Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), “The New
-     S Language,” London: Chapman & Hall.
+   • Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), "The New
+     S Language," London: Chapman & Hall.
 
-     This book is often called the “_Blue Book_”, and introduced what is now
+     This book is often called the "_Blue Book_", and introduced what is now
      known as S version 2.
 
-   • John M. Chambers and Trevor J. Hastie (1992), “Statistical Models in
-     S,” London: Chapman & Hall.
+   • John M. Chambers and Trevor J. Hastie (1992), "Statistical Models in
+     S," London: Chapman & Hall.
 
-     This is also called the “_White Book_”, and introduced S version 3,
+     This is also called the "_White Book_", and introduced S version 3,
      which added structures to facilitate statistical modeling in S.
 
-   • John M. Chambers (1998), “Programming with Data,” New York: Springer,
+   • John M. Chambers (1998), "Programming with Data," New York: Springer,
      ISBN 0-387-98503-4 (<https://johnmchambers.su.domains/Sbook/>).
 
-     This “_Green Book_” describes version 4 of S, a major revision of S
+     This "_Green Book_" describes version 4 of S, a major revision of S
      designed by John Chambers to improve its usefulness at every stage of
      the programming process.
 
    See <https://johnmchambers.su.domains/papers/96.7.ps> for further
-information on the “Evolution of the S Language”.
+information on the "Evolution of the S Language".
 
 3.2 What is S-PLUS?
 ===================
 
 S-PLUS is a value-added version of S sold by TIBCO Software Inc
-(https://www.tibco.com/) as ‘TIBCO Spotfire S+’.  See
+(https://www.tibco.com/) as 'TIBCO Spotfire S+'.  See
 <https://en.wikipedia.org/wiki/S-PLUS> for more information.
 
 3.3 What are the differences between R and S?
 =============================================
 
 We can regard S as a language with three current implementations or
-“engines”, the “old S engine” (S version 3; S-PLUS 3.x and 4.x), the “new S
-engine” (S version 4; S-PLUS 5.x and above), and R. Given this
-understanding, asking for “the differences between R and S” really amounts
+"engines", the "old S engine" (S version 3; S-PLUS 3.x and 4.x), the "new S
+engine" (S version 4; S-PLUS 5.x and above), and R. Given this
+understanding, asking for "the differences between R and S" really amounts
 to asking for the specifics of the R implementation of the S language, i.e.,
 the difference between the R and S _engines_.
 
-   For the remainder of this section, “S” refers to the S engines and not
+   For the remainder of this section, "S" refers to the S engines and not
 the S language.
 
 3.3.1 Lexical scoping
@@ -778,7 +778,7 @@
        n * sq()
      }
 
-   Under S, ‘sq()’ does not “know” about the variable ‘n’ unless it is
+   Under S, ‘sq()’ does not "know" about the variable ‘n’ unless it is
 defined globally:
 
      S> cube(2)
@@ -788,13 +788,13 @@
      S> cube(2)
      [1] 18
 
-   In R, the “environment” created when ‘cube()’ was invoked is also looked
+   In R, the "environment" created when ‘cube()’ was invoked is also looked
 in:
 
      R> cube(2)
      [1] 8
 
-   As a more “interesting” real-world problem, suppose you want to write a
+   As a more "interesting" real-world problem, suppose you want to write a
 function which returns the density function of the r-th order statistic from
 a sample of size n from a (continuous) distribution.  For simplicity, we
 shall use both the distribution and density functions distribution as
@@ -816,7 +816,7 @@
        f
      }
 
-Rather tricky, isn’t it?  The code uses the fact that in S, functions are
+Rather tricky, isn't it?  The code uses the fact that in S, functions are
 just lists of special mode with the function body as the last argument, and
 hence does not work in R (one could make the idea work, though).
 
@@ -841,7 +841,7 @@
        }
      }
 
-This seems to be the “natural” implementation, and it works because the free
+This seems to be the "natural" implementation, and it works because the free
 variables in the returned function can be looked up in the defining
 environment (this is lexical scope).
 
@@ -849,7 +849,7 @@
 along with all variable bindings needed for evaluating it.  Since in the
 above version, the free variables in the value function are not modified,
 you can actually use it in S as well if you abstract out the closure
-operation into a function ‘MC()’ (for “make closure”):
+operation into a function ‘MC()’ (for "make closure"):
 
      dorder <- function(n, r, pfun, dfun) {
        con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
@@ -860,7 +860,7 @@
      }
 
    Given the appropriate definitions of the closure operator, this works in
-both R and S, and is much “cleaner” than a substitute/eval solution (or one
+both R and S, and is much "cleaner" than a substitute/eval solution (or one
 which overrules the default scoping rules by using explicit access to
 evaluation frames, as is of course possible in both R and S).
 
@@ -899,11 +899,11 @@
    Nested lexically scoped functions allow using function closures and
 maintaining local state.  A simple example (taken from Abelson and Sussman)
 is obtained by typing ‘demo("scoping")’ at the R prompt.  Further
-information is provided in the standard R reference “R: A Language for Data
-Analysis and Graphics” (*note What documentation exists for R?::) and in
-Robert Gentleman and Ross Ihaka (2000), “Lexical Scope and Statistical
-Computing”, _Journal of Computational and Graphical Statistics_, *9*,
-491–508 (doi: 10.1080/10618600.2000.10474895
+information is provided in the standard R reference "R: A Language for Data
+Analysis and Graphics" (*note What documentation exists for R?::) and in
+Robert Gentleman and Ross Ihaka (2000), "Lexical Scope and Statistical
+Computing", _Journal of Computational and Graphical Statistics_, *9*,
+491-508 (doi: 10.1080/10618600.2000.10474895
 (https://doi.org/10.1080/10618600.2000.10474895)).
 
    Nested lexically scoped functions also imply a further major difference.
@@ -913,12 +913,12 @@
 uses it to store the objects.  R performs its own memory management of this
 piece of memory, growing and shrinking its size as needed.  Having
 everything in memory is necessary because it is not really possible to
-externally maintain all relevant “environments” of symbol/value pairs.  This
+externally maintain all relevant "environments" of symbol/value pairs.  This
 difference also seems to make R _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,
+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.
 (In fact, one might conjecture that the S developers felt that the price of
@@ -958,8 +958,8 @@
 
 Apart from lexical scoping and its implications, R follows the S language
 definition in the Blue and White Books 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
+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.
 
@@ -1034,7 +1034,7 @@
      the ‘dim’ attribute vector can be 0).  This has been determined a
      useful feature as it helps reducing the need for special-case tests for
      empty subsets.  For example, if ‘x’ is a matrix, ‘x[, FALSE]’ is not
-     ‘NULL’ but a “matrix” with 0 columns.  Hence, such objects need to be
+     ‘NULL’ but a "matrix" with 0 columns.  Hence, such objects need to be
      tested for by checking whether their ‘length()’ is zero (which works in
      both R and S), and not using ‘is.null()’.
 
@@ -1056,7 +1056,7 @@
    • In S, ‘substitute()’ searches for names for substitution in the given
      expression in three places: the actual and the default arguments of the
      matching call, and the local frame (in that order).  R looks in the
-     local frame only, with the special rule to use a “promise” if a
+     local frame only, with the special rule to use a "promise" if a
      variable is not evaluated.  Since the local frame is initialized with
      the actual arguments or the default expressions, this is usually
      equivalent to S, until assignment takes place.
@@ -1077,7 +1077,7 @@
      ‘Sys.setlocale()’ function.
 
    • In S, ‘missing(ARG)’ remains ‘TRUE’ if ARG is subsequently modified; in
-     R it doesn’t.
+     R it doesn't.
 
    • From R version 1.3.0, ‘data.frame’ strips ‘I()’ when creating (column)
      names.
@@ -1100,9 +1100,9 @@
      ‘$’ allow partial matches).  In S, ‘[’ allows partial matches.
 
    • S has a two-argument version of ‘atan’ and no ‘atan2’.  A call in S
-     such as ‘atan(x1, x2)’ is equivalent to R’s ‘atan2(x1, x2)’.  However,
-     beware of named arguments since S’s ‘atan(x = a, y = b)’ is equivalent
-     to R’s ‘atan2(y = a, x = b)’ with the meanings of ‘x’ and ‘y’
+     such as ‘atan(x1, x2)’ is equivalent to R's ‘atan2(x1, x2)’.  However,
+     beware of named arguments since S's ‘atan(x = a, y = b)’ is equivalent
+     to R's ‘atan2(y = a, x = b)’ with the meanings of ‘x’ and ‘y’
      interchanged.  (R used to have undocumented support for a two-argument
      ‘atan’ with positional arguments, but this has been withdrawn to avoid
      further confusion.)
@@ -1121,7 +1121,7 @@
 
 Since almost anything you can do in R has source code that you could port to
 S-PLUS with little effort there will never be much you can do in R that you
-couldn’t do in S-PLUS if you wanted to.  (Note that using lexical scoping
+couldn't do in S-PLUS if you wanted to.  (Note that using lexical scoping
 may simplify matters considerably, though.)
 
    R offers several graphics features that S-PLUS does not, such as finer
@@ -1129,9 +1129,9 @@
 correction for color, and, most importantly, mathematical annotation in plot
 texts, via input expressions reminiscent of TeX constructs.  See the help
 page for ‘plotmath’, which features an impressive on-line example.  More
-details can be found in Paul Murrell and Ross Ihaka (2000), “An Approach to
-Providing Mathematical Annotation in Plots”, _Journal of Computational and
-Graphical Statistics_, *9*, 582–599 (doi: 10.1080/10618600.2000.10474900
+details can be found in Paul Murrell and Ross Ihaka (2000), "An Approach to
+Providing Mathematical Annotation in Plots", _Journal of Computational and
+Graphical Statistics_, *9*, 582-599 (doi: 10.1080/10618600.2000.10474900
 (https://doi.org/10.1080/10618600.2000.10474900)).
 
 3.5 What is R-plus?
@@ -1151,17 +1151,17 @@
 4 R Web Interfaces
 ******************
 
-Please refer to the CRAN task view on “Web Technologies and Services”
+Please refer to the CRAN task view on "Web Technologies and Services"
 (<https://CRAN.R-project.org/view=WebTechnologies>), specifically section
-“Web and Server Frameworks”, for up-to-date information on R web interface
+"Web and Server Frameworks", for up-to-date information on R web interface
 packages.
 
-   Early references on R web interfaces include Jeff Banfield (1999), “Rweb:
-Web-based Statistical Analysis” (doi: 10.18637/jss.v004.i01
-(https://doi.org/10.18637/jss.v004.i01)), David Firth (2003), “CGIwithR:
-Facilities for processing web forms using R” (doi: 10.18637/jss.v008.i10
+   Early references on R web interfaces include Jeff Banfield (1999), "Rweb:
+Web-based Statistical Analysis" (doi: 10.18637/jss.v004.i01
+(https://doi.org/10.18637/jss.v004.i01)), David Firth (2003), "CGIwithR:
+Facilities for processing web forms using R" (doi: 10.18637/jss.v008.i10
 (https://doi.org/10.18637/jss.v008.i10)), and Angelo Mineo and Alfredo
-Pontillo (2006), “Using R via PHP for Teaching Purposes: R-php”
+Pontillo (2006), "Using R via PHP for Teaching Purposes: R-php"
 (doi: 10.18637/jss.v017.i04 (https://doi.org/10.18637/jss.v017.i04)).
 
 5 R Add-On Packages
@@ -1216,15 +1216,15 @@
 
 *KernSmooth*
      Functions for kernel smoothing (and density estimation) corresponding
-     to the book “Kernel Smoothing” by M. P. Wand and M. C. Jones, 1995.
+     to the book "Kernel Smoothing" by M. P. Wand and M. C. Jones, 1995.
 *MASS*
      Functions and datasets from the main package of Venables and Ripley,
-     “Modern Applied Statistics with S”.
+     "Modern Applied Statistics with S".
 *Matrix*
      Support for spares and dense matrices
 *boot*
-     Functions and datasets for bootstrapping from the book “Bootstrap
-     Methods and Their Applications” by A. C. Davison and D. V. Hinkley,
+     Functions and datasets for bootstrapping from the book "Bootstrap
+     Methods and Their Applications" by A. C. Davison and D. V. Hinkley,
      1997, Cambridge University Press.
 *class*
      Functions for classification (k-nearest neighbor and LVQ).
@@ -1243,13 +1243,13 @@
 *nlme*
      Fit and compare Gaussian linear and nonlinear mixed-effects models.
 *nnet*
-     Software for single hidden layer perceptrons (“feed-forward neural
-     networks”), and for multinomial log-linear models.
+     Software for single hidden layer perceptrons ("feed-forward neural
+     networks"), and for multinomial log-linear models.
 *rpart*
      Recursive PARTitioning and regression trees.
 *spatial*
-     Functions for kriging and point pattern analysis from “Modern Applied
-     Statistics with S” by W. Venables and B. Ripley.
+     Functions for kriging and point pattern analysis from "Modern Applied
+     Statistics with S" by W. Venables and B. Ripley.
 *survival*
      Functions for survival analysis, including penalized likelihood.
    See the CRAN contributed packages page for more information.
@@ -1358,7 +1358,7 @@
           tools         Tools for Package Development
           utils         The R Utils Package
 
-   You can “load” the installed package PKG by
+   You can "load" the installed package PKG by
 
      library(PKG)
 
@@ -1398,7 +1398,7 @@
 contain files ‘INDEX’, ‘NAMESPACE’, ‘configure’, ‘cleanup’, ‘LICENSE’,
 ‘LICENCE’, ‘COPYING’ and ‘NEWS’.
 
-   See section “Creating R packages” in ‘Writing R Extensions’, for details.
+   See section "Creating R packages" in ‘Writing R Extensions’, for details.
 This manual is included in the R distribution, *note What documentation
 exists for R?::, and gives information on package structure, the configure
 and cleanup mechanisms, and on automated package checking and building.
@@ -1428,7 +1428,7 @@
 6.1 Is there Emacs support for R?
 =================================
 
-There is an Emacs package called ESS (“Emacs Speaks Statistics”) which
+There is an Emacs package called ESS ("Emacs Speaks Statistics") which
 provides a standard interface between statistical programs and statistical
 processes.  It is intended to provide assistance for interactive statistical
 programming and data analysis.  Languages supported include: S dialects (R,
@@ -1473,7 +1473,7 @@
 
 Yes, instead of just running it in a console, _definitely_.  As an
 alternative to other IDEs such as RStudio, _possibly_, notably if you are
-interested to use Emacs for other computer interaction.  You’d be using ESS,
+interested to use Emacs for other computer interaction.  You'd be using ESS,
 Emacs Speaks Statistics, see previous FAQ.
 
    Inferior R mode provides a readline/history mechanism, object name
@@ -1496,7 +1496,7 @@
 6.3 Debugging R from within Emacs
 =================================
 
-To debug R “from within Emacs”, there are several possibilities.  To use the
+To debug R "from within Emacs", there are several possibilities.  To use the
 Emacs GUD (Grand Unified Debugger) library with the recommended debugger
 GDB, type ‘M-x gdb’ and give the path to the R _binary_ as argument.  At the
 ‘gdb’ prompt, set ‘R_HOME’ and other environment variables as needed (using
@@ -1549,7 +1549,7 @@
 7.2 How can I save my workspace?
 ================================
 
-‘save.image()’ saves the objects in the user’s ‘.GlobalEnv’ to the file
+‘save.image()’ saves the objects in the user's ‘.GlobalEnv’ to the file
 ‘.RData’ in the R startup directory.  (This is also what happens after
 ‘q("yes")’.)  Using ‘save.image(FILE)’ one can save the image under a
 different name.
@@ -1576,14 +1576,14 @@
    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
+(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, it is
-perfectly logical.  The “intuitive” behavior could easily be implemented,
+perfectly logical.  The "intuitive" behavior 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
@@ -1633,7 +1633,7 @@
 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.
+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).
@@ -1644,10 +1644,10 @@
 =============================
 
 The function ‘options()’ allows setting and examining a variety of global
-“options” which affect the way in which R computes and displays its results.
+"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.
+crazy--simply pretend that it is a "read-only" variable.
 
    For example, given
 
@@ -1688,7 +1688,7 @@
 This is about a problem rarely seen with modern X11 installations.
 
    On an X11 device, plotting sometimes, e.g., when running ‘demo("image")’,
-results in “Error: color allocation error”.  This is an X problem, and only
+results in "Error: color allocation error".  This is an X problem, and only
 indirectly related to R. It occurs when applications started prior to R have
 used all the available colors.  (How many colors are available depends on
 the X configuration; sometimes only 256 colors can be used.)
@@ -1724,18 +1724,18 @@
    You could also look at ‘coplot()’ and ‘dotchart()’ which might do at
 least some of what you want.  Note also that the R version of ‘pairs()’ is
 fairly general and provides most of the functionality of ‘splom()’, and that
-R’s default plot method has an argument ‘asp’ allowing to specify (and fix
+R's default plot method has an argument ‘asp’ allowing to specify (and fix
 against device resizing) the aspect ratio of the plot.
 
-   (Because the word “Trellis” has been claimed as a trademark we do not use
-it in R. The name “lattice” has been chosen for the R equivalent.)
+   (Because the word "Trellis" has been claimed as a trademark we do not use
+it in R. The name "lattice" has been chosen for the R equivalent.)
 
 7.12 What are the enclosing and parent environments?
 ====================================================
 
 Inside a function you may want to access variables in two additional
-environments: the one that the function was defined in (“enclosing”), and
-the one it was invoked in (“parent”).
+environments: the one that the function was defined in ("enclosing"), and
+the one it was invoked in ("parent").
 
    If you create a function at the command line or load it in a package its
 enclosing environment is the global workspace.  If you define a function
@@ -1744,7 +1744,7 @@
 when the function is created.  You can find out the enclosing environment
 for a function ‘f()’ using ‘environment(f)’.
 
-   The “parent” environment, on the other hand, is defined when you invoke a
+   The "parent" environment, on the other hand, is defined when you invoke a
 function.  If you invoke ‘lm()’ at the command line its parent environment
 is the global workspace, if you invoke it inside a function ‘f()’ then its
 parent environment is the environment inside ‘f()’.  You can find out the
@@ -1760,7 +1760,7 @@
 
    Parent environments are important because things like model formulas need
 to be evaluated in the environment the function was called from, since
-that’s where all the variables will be available.  This relies on the parent
+that's where all the variables will be available.  This relies on the parent
 environment being potentially different with each invocation.
 
    Enclosing environments are important because a function can use variables
@@ -1806,8 +1806,8 @@
 argument ‘check.names’ to these functions controls whether variable names
 are checked and adjusted by ‘make.names()’ if needed.)
 
-   To understand what names are “valid”, one needs to take into account that
-the term “name” is used in several different (but related) ways in the
+   To understand what names are "valid", one needs to take into account that
+the term "name" is used in several different (but related) ways in the
 language:
 
   1. A _syntactic name_ is a string the parser interprets as this type of
@@ -1835,7 +1835,7 @@
   5. Finally, a _file name_ is a string identifying a file in the operating
      system for reading, writing, etc.  It really has nothing much to do
      with names in the language, but it is traditional to call these strings
-     file “names”.
+     file "names".
 
 7.15 Are GAMs implemented in R?
 ===============================
@@ -1870,7 +1870,7 @@
    If you type ‘1+1’ or ‘summary(glm(y~x+z, family=binomial))’ at the
 command line the returned value is automatically printed (unless it is
 ‘invisible()’), but in other circumstances, such as in a ‘source()’d file or
-inside a function it isn’t printed unless you specifically print it.
+inside a function it isn't printed unless you specifically print it.
 
    To print the value use
 
@@ -1927,7 +1927,7 @@
 
    Some packages report instead the sums of squares based on comparing the
 full model to the models with each factor removed one at a time (the famous
-‘Type III sums of squares’ from SAS, for example).  These do not depend on
+'Type III sums of squares' from SAS, for example).  These do not depend on
 the order of factors in the model.  The question of which set of sums of
 squares is the Right Thing provokes low-level holy wars on R-help from time
 to time.
@@ -1960,7 +1960,7 @@
 The Unix-like command-line interface to R can only provide the inbuilt
 command line editor which allows recall, editing and re-submission of prior
 commands provided that the GNU readline library is available at the time R
-is configured for compilation.  Note that the ‘development’ version of
+is configured for compilation.  Note that the 'development' version of
 readline including the appropriate headers is needed: users of Linux binary
 distributions will need to install packages such as ‘libreadline-dev’
 (Debian) or ‘readline-devel’ (Red Hat).
@@ -2065,8 +2065,8 @@
      getS3method("[", "terms")
 
 To see the code for an unexported function ‘foo()’ in the namespace of
-package ‘"bar"’ use ‘bar:::foo’.  Don’t use these constructions to call
-unexported functions in your own code—they are probably unexported for a
+package ‘"bar"’ use ‘bar:::foo’.  Don't use these constructions to call
+unexported functions in your own code--they are probably unexported for a
 reason and may change without warning.
 
 7.27 How can I create rotated axis labels?
@@ -2096,8 +2096,8 @@
 x axis.  See ‘?par’ for more information.
 
    Also see Figure 1 and associated code in Paul Murrell (2003),
-“Integrating grid Graphics Output with Base Graphics Output”, _R News_,
-*3/2*, 7–12.
+"Integrating grid Graphics Output with Base Graphics Output", _R News_,
+*3/2*, 7-12.
 
 7.28 Why is read.table() so inefficient?
 ========================================
@@ -2114,12 +2114,12 @@
 A “package” is a standardized collection of material extending R, e.g.
 providing code, data, or documentation.  A “library” is a place (directory)
 where R knows to find packages it can use (i.e., which were “installed”).  R
-is told to use a package (to “load” it and add it to the search path) via
+is told to use a package (to "load" it and add it to the search path) via
 calls to the function ‘library’.  I.e., ‘library()’ is employed to load a
 package from libraries containing packages.
 
    *Note R Add-On Packages::, for more details.  See also Uwe Ligges (2003),
-“R Help Desk: Package Management”, _R News_, *3/3*, 37–39.
+"R Help Desk: Package Management", _R News_, *3/3*, 37-39.
 
 7.30 I installed a package but the functions are not there
 ==========================================================
@@ -2129,10 +2129,10 @@
    See *note R Add-On Packages:: and *note What is the difference between
 package and library?:: for more information.
 
-7.31 Why doesn’t R think these numbers are equal?
+7.31 Why doesn't R think these numbers are equal?
 =================================================
 
-The only numbers that can be represented exactly in R’s numeric type are
+The only numbers that can be represented exactly in R's numeric type are
 integers and fractions whose denominator is a power of 2.  All other numbers
 are internally rounded to (typically) 53 binary digits accuracy.  As a
 result, two floating point numbers will not reliably be equal unless they
@@ -2152,15 +2152,15 @@
 you will need to consider error propagation carefully.
 
    A discussion with many easily followed examples is in Appendix G
-“Computational Precision and Floating Point Arithmetic”, pages 753–771 of
+"Computational Precision and Floating Point Arithmetic", pages 753-771 of
 _Statistical Analysis and Data Display: An Intermediate Course with Examples
 in R_, Richard M. Heiberger and Burt Holland (Springer 2015, second
 edition).  This appendix is a free download from
 <https://link.springer.com/content/pdf/bbm:978-1-4939-2122-5/1.pdf>.
 
-   For more information, see e.g. David Goldberg (1991), “What Every
-Computer Scientist Should Know About Floating-Point Arithmetic”, _ACM
-Computing Surveys_, *23/1*, 5–48, also available via
+   For more information, see e.g. David Goldberg (1991), "What Every
+Computer Scientist Should Know About Floating-Point Arithmetic", _ACM
+Computing Surveys_, *23/1*, 5-48, also available via
 <https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html>.
 
    Here is another example, this time using addition:
@@ -2365,8 +2365,8 @@
 
 In most cases, typing the name of the function will print its source code.
 However, code is sometimes hidden in a namespace, or compiled.  For a
-complete overview on how to access source code, see Uwe Ligges (2006), “Help
-Desk: Accessing the sources”, _R News_, *6/4*, 43–45
+complete overview on how to access source code, see Uwe Ligges (2006), "Help
+Desk: Accessing the sources", _R News_, *6/4*, 43-45
 (<https://CRAN.R-project.org/doc/Rnews/Rnews_2006-4.pdf>).
 
 7.41 Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?
@@ -2385,17 +2385,17 @@
      a line through the origin.
 
    All these come down to saying that if you know _a priori_ that E[Y]=0
-when x=0 then the ‘null’ model that you should compare to the fitted line,
-the model where x doesn’t explain any of the variance, is the model where
-E[Y]=0 everywhere.  (If you don’t know a priori that E[Y]=0 when x=0, then
-you probably shouldn’t be fitting a line through the origin.)
+when x=0 then the 'null' model that you should compare to the fitted line,
+the model where x doesn't explain any of the variance, is the model where
+E[Y]=0 everywhere.  (If you don't know a priori that E[Y]=0 when x=0, then
+you probably shouldn't be fitting a line through the origin.)
 
 7.42 Why is R apparently not releasing memory?
 ==============================================
 
-This question is often asked in different flavors along the lines of “I have
+This question is often asked in different flavors along the lines of "I have
 removed objects in R and run ‘gc()’ and yet ‘ps’/‘top’ still shows the R
-process using a lot of memory”, often on Linux machines.
+process using a lot of memory", often on Linux machines.
 
    This is an artifact of the way the operating system (OS) allocates
 memory.  In general it is common that the OS is not capable of releasing all
@@ -2421,7 +2421,7 @@
 regions.  For smaller allocations the system can expand the data segment of
 the process (historically using the ‘brk’ system call), but this whole area
 is always contiguous.  The OS can only move the end of this space, it cannot
-create any “holes”.  Since this operation is fairly cheap, it is used for
+create any "holes".  Since this operation is fairly cheap, it is used for
 allocations of small pieces of memory.  However, the side-effect is that
 even if there is just one byte that is in use at the end of the data
 segment, the OS cannot release any memory at all, because it cannot change
@@ -2458,7 +2458,7 @@
 Since March 2016, Windows and macOS binaries of CRAN packages for old
 versions of R (released more than 5 years ago) are made available from a
 central CRAN archive server instead of the CRAN mirrors.  To get these, one
-should set the CRAN “mirror” element of the ‘repos’ option accordingly, by
+should set the CRAN "mirror" element of the ‘repos’ option accordingly, by
 something like
      local({r <- getOption("repos")
             r["CRAN"] <- "http://CRAN-archive.R-project.org"
@@ -2479,7 +2479,7 @@
 This approach is preferred over having ‘summary.foo()’ print summary
 information and return something useful, as sometimes you need to grab
 something computed by ‘summary()’ inside a function or similar.  In such
-cases you don’t want anything printed.
+cases you don't want anything printed.
 
 8.2 How can I debug dynamically loaded code?
 ============================================
@@ -2487,7 +2487,7 @@
 Roughly speaking, you need to start R inside the debugger, load the code,
 send an interrupt, and then set the required breakpoints.
 
-   See section “Finding entry points in dynamically loaded code” in ‘Writing
+   See section "Finding entry points in dynamically loaded code" in ‘Writing
 R Extensions’.  This manual is included in the R distribution, *note What
 documentation exists for R?::.
 
@@ -2496,7 +2496,7 @@
 
 The most convenient way is to call ‘R_PV’ from the symbolic debugger.
 
-   See section “Inspecting R objects when debugging” in ‘Writing R
+   See section "Inspecting R objects when debugging" in ‘Writing R
 Extensions’.
 
 8.4 How can I change compilation flags?
@@ -2508,7 +2508,7 @@
 
    Starting with R 2.1.0, users can provide personal ‘Makevars’
 configuration files in ‘$HOME/.R’ to override the default flags.  See
-section “Add-on packages” in ‘R Installation and Administration’.
+section "Add-on packages" in ‘R Installation and Administration’.
 
 8.5 How can I debug S4 methods?
 ===============================
@@ -2525,31 +2525,31 @@
 
 If R executes an illegal instruction, or dies with an operating system error
 message that indicates a problem in the program (as opposed to something
-like “disk full”), then it is certainly a bug.  If you call ‘.C()’,
+like "disk full"), then it is certainly a bug.  If you call ‘.C()’,
 ‘.Fortran()’, ‘.External()’ or ‘.Call()’ (or ‘.Internal()’) yourself (or in
 a function you wrote), you can always crash R by using wrong argument types
 (modes).  This is not a bug.
 
    Taking forever to complete a command can be a bug, but you must make
-certain that it was really R’s fault.  Some commands simply take a long
+certain that it was really R's fault.  Some commands simply take a long
 time.  If the input was such that you _know_ it should have been processed
-quickly, report a bug.  If you don’t know whether the command should take a
+quickly, report a bug.  If you don't know whether the command should take a
 long time, find out by looking in the manual or by asking for assistance.
 
    If a command you are familiar with causes an R error message in a case
 where its usual definition ought to be reasonable, it is probably a bug.  If
 a command does the wrong thing, that is a bug.  But be sure you know for
-certain what it ought to have done.  If you aren’t familiar with the
-command, or don’t know for certain how the command is supposed to work, then
+certain what it ought to have done.  If you aren't familiar with the
+command, or don't know for certain how the command is supposed to work, then
 it might actually be working right.  For example, people sometimes think
-there is a bug in R’s mathematics because they don’t understand how
+there is a bug in R's mathematics because they don't understand how
 finite-precision arithmetic works.  Rather than jumping to conclusions, show
 the problem to someone who knows for certain.  Unexpected results of
 comparison of decimal numbers, for example ‘0.28 * 100 != 28’ or ‘0.1 + 0.2
 != 0.3’, are not a bug.  *Note Why doesn't R think these numbers are
 equal?::, for more details.
 
-   Finally, a command’s intended definition may not be best for statistical
+   Finally, a command's intended definition may not be best for statistical
 analysis.  This is a very important sort of problem, but it is also a matter
 of judgment.  Also, it is easy to come to such a conclusion out of ignorance
 of some of the existing features.  It is probably best not to complain about
@@ -2557,15 +2557,15 @@
 feel confident that you understand it, and know for certain that what you
 want is not available.  If you are not sure what the command is supposed to
 do after a careful reading of the manual this indicates a bug in the manual.
-The manual’s job is to make everything clear.  It is just as important to
+The manual's job is to make everything clear.  It is just as important to
 report documentation bugs as program bugs.  However, we know that the
-introductory documentation is seriously inadequate, so you don’t need to
+introductory documentation is seriously inadequate, so you don't need to
 report this.
 
    If the online argument list of a function disagrees with the manual, one
 of them must be wrong, so report the bug.
 
-   See also “Making sure it’s a bug” in Bug Reporting in R
+   See also "Making sure it's a bug" in Bug Reporting in R
 (https://www.r-project.org/bugs.html) for more information.
 
 9.2 How to report a bug
@@ -2599,7 +2599,7 @@
 guess that they should try a day of the week variable name.
 
    Or perhaps the command fails because the last command you used was a
-method for ‘"["()’ that had a bug causing R’s internal data structures to be
+method for ‘"["()’ that had a bug causing R's internal data structures to be
 corrupted and making the ‘data.frame()’ command fail from then on.  This is
 why others need to know what other commands you have typed (or read from
 your startup file).
@@ -2617,8 +2617,8 @@
 ensures that the site profile and saved data files are not read.
 
    Before you actually submit a bug report, you should check whether the bug
-has already been reported and/or fixed.  First, try the “Show open bugs
-new-to-old” or the search facility on <https://bugs.R-project.org/>.
+has already been reported and/or fixed.  First, try the "Show open bugs
+new-to-old" or the search facility on <https://bugs.R-project.org/>.
 Second, consult <https://svn.R-project.org/R/trunk/doc/NEWS.Rd>, which
 records changes that will appear in the _next_ release of R, including bug
 fixes that do not appear on the Bug Tracker.  Third, if possible try the
@@ -2632,10 +2632,10 @@
    A bug report can be generated using the function ‘bug.report()’.  For
 reports on R this will open the R Bugzilla page at
 <https://bugs.R-project.org/>: for a contributed package it will open the
-package’s bug tracker Web page or help you compose an email to the
-maintainer.  Since 2016, only “members” (including all who have previously
-submitted bugs) can submit new bugs on the R Bugzilla.  See “Where to submit
-bug reports and patches” on Bug Reporting in R
+package's bug tracker Web page or help you compose an email to the
+maintainer.  Since 2016, only "members" (including all who have previously
+submitted bugs) can submit new bugs on the R Bugzilla.  See "Where to submit
+bug reports and patches" on Bug Reporting in R
 (https://www.r-project.org/bugs.html) for more information.
 
    There is a section of the bug repository for suggestions for enhancements
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/doc/NEWS R-patched/doc/NEWS
--- R-release-orig/doc/NEWS	2024-10-31 09:06:07.000000000 +0100
+++ R-patched/doc/NEWS	2024-11-25 01:35:27.325970489 +0100
@@ -1,5 +1,25 @@
 R News
 
+CHANGES IN R 4.4.2 patched:
+
+  C-LEVEL FACILITIES:
+
+    * The functions R_strtod and R_atof now allow hexadecimal constants
+      without an exponent, for compatibility with their C99 versions
+      (PR#18805).
+
+  BUG FIXES:
+
+    * kappa(A, exact=TRUE) for singular A returns Inf more generally,
+      fixing PR#18817 reported by Mikael Jagan.
+
+    * Fixed URLs of the sun spots (sunspot.month etc) data sets and
+      mention future changes due to recalibration.
+
+    * The parser now accepts hexadecimal constants with a decimal point
+      without an exponent (taken as p0) as documented in
+      ?NumericConstants (PR#18819).
+
 CHANGES IN R 4.4.2:
 
   C-LEVEL FACILITIES:
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/doc/NEWS.Rd R-patched/doc/NEWS.Rd
--- R-release-orig/doc/NEWS.Rd	2024-10-22 00:15:01.000000000 +0200
+++ R-patched/doc/NEWS.Rd	2024-11-11 15:37:29.111917723 +0100
@@ -5,6 +5,30 @@
 \title{R News}
 \encoding{UTF-8}
 
+\section{\Rlogo CHANGES IN R 4.4.2 patched}{
+ \subsection{C-LEVEL FACILITIES}{
+    \itemize{
+      \item The functions \code{R_strtod} and \code{R_atof} now allow
+      hexadecimal constants without an exponent, for compatibility with
+      their C99 versions (\PR{18805}).
+    }
+  }
+  \subsection{BUG FIXES}{
+    \itemize{
+      \item \code{kappa(A, exact=TRUE)} for singular \code{A} returns
+      \code{Inf} more generally, fixing \PR{18817} reported by
+      \I{Mikael Jagan}.
+
+      \item Fixed URLs of the sun spots (\code{sunspot.month} etc) data
+      sets and mention future changes due to recalibration.
+
+      \item The parser now accepts hexadecimal constants with a decimal
+      point without an exponent (taken as \code{p0}) as documented in
+      \code{?NumericConstants} (\PR{18819}).
+    }
+  }
+}
+
 \section{\Rlogo CHANGES IN R 4.4.2}{
   \subsection{C-LEVEL FACILITIES}{
     \itemize{
Binary files R-release-orig/doc/NEWS.pdf and R-patched/doc/NEWS.pdf differ
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/doc/RESOURCES R-patched/doc/RESOURCES
--- R-release-orig/doc/RESOURCES	2024-10-31 09:02:39.000000000 +0100
+++ R-patched/doc/RESOURCES	2024-11-25 01:32:24.089146680 +0100
@@ -1,5 +1,5 @@
 
-                            AVAILABLE R RESOURCES
+ AVAILABLE R RESOURCES
 
 1 FAQ
 *****
@@ -9,9 +9,9 @@
 
      <https://CRAN.R-project.org/doc/FAQ/R-FAQ.html>
 
-A text version is in file 'FAQ' in this directory, and an HTML version is
-available as file 'doc/html/faq.html' and via the on-line help (on the index
-page given by 'help.start()').
+A text version is in file ‘FAQ’ in this directory, and an HTML version is
+available as file ‘doc/html/faq.html’ and via the on-line help (on the index
+page given by ‘help.start()’).
 
 2 Mailing lists
 ***************
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/doc/html/NEWS.html R-patched/doc/html/NEWS.html
--- R-release-orig/doc/html/NEWS.html	2024-10-31 09:06:07.000000000 +0100
+++ R-patched/doc/html/NEWS.html	2024-11-25 01:35:28.746857432 +0100
@@ -18,6 +18,45 @@
 
 <h2>R News</h2>
 
+<h3><img src="../help/figures/../../html/Rlogo.svg" class="toplogo" alt="[R logo]" /> CHANGES IN R 4.4.2 patched</h3>
+
+
+
+<h4>C-LEVEL FACILITIES</h4>
+
+
+<ul>
+<li><p> The functions <code>R_strtod</code> and <code>R_atof</code> now allow
+hexadecimal constants without an exponent, for compatibility with
+their C99 versions (<a href="https://bugs.R-project.org/show_bug.cgi?id=18805">PR#18805</a>).
+</p>
+</li></ul>
+
+
+
+
+<h4>BUG FIXES</h4>
+
+
+<ul>
+<li> <p><code>kappa(A, exact=TRUE)</code> for singular <code>A</code> returns
+<code>Inf</code> more generally, fixing <a href="https://bugs.R-project.org/show_bug.cgi?id=18817">PR#18817</a> reported by
+Mikael Jagan.
+</p>
+</li>
+<li><p> Fixed URLs of the sun spots (<code>sunspot.month</code> etc) data
+sets and mention future changes due to recalibration.
+</p>
+</li>
+<li><p> The parser now accepts hexadecimal constants with a decimal
+point without an exponent (taken as <code>p0</code>) as documented in
+<code>?NumericConstants</code> (<a href="https://bugs.R-project.org/show_bug.cgi?id=18819">PR#18819</a>).
+</p>
+</li></ul>
+
+
+
+
 <h3><img src="../help/figures/../../html/Rlogo.svg" class="toplogo" alt="[R logo]" /> CHANGES IN R 4.4.2</h3>
 
 
diff -ru -P -x .mirror -x ls_lR -x Old R-release-orig/doc/html/R-admin.html R-patched/doc/html/R-admin.html
--- R-release-orig/doc/html/R-admin.html	2024-10-31 09:08:46.000000000 +0100
+++ R-patched/doc/html/R-admin.html	2024-11-25 01:43:31.958766504 +0100
@@ -1,11 +1,11 @@
-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
+<!DOCTYPE html>
 <html>
-
+
 <head>
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
-
+
 <head>
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
 <title>R Resources</title>
@@ -18,21 +18,8 @@
 <link href="dir.html#Top" rel="prev" title="(dir)">
 <style type="text/css">
  everything "CET" [ <==> !length(w..) ]
+stopifnot(!length(w8) || identical(w8, 141:142),# exactly 1941:1942 had CEST on Jan.1
+          !length(w9) || identical(w9,  41: 42))
 ## for R-devel Jan.2016 to Mar.14 -- *AND* for R 3.2.4 -- the above gave
 ## integer(0)  and  c(41:42, 99:100, ..., 389:390)  respectively
-
+##
 ## the above gives 1:142 and 1:42 respectively on Solaris 10 when not using
 ## --with-internal-tzcode; R-Admin recommends --with-internal-tzcode.
 
+
 ## tsp<- did not remove mts class
 z <- ts(cbind(1:5,1:5))
 tsp(z) <- NULL

Places

File R-release.diff of Package R

Places