Binary files /tmp/tmpg76xwnyx/tQUtgCH3dW/r-cran-readxl-1.4.0/build/vignette.rds and /tmp/tmpg76xwnyx/roft5JXyx7/r-cran-readxl-1.4.1/build/vignette.rds differ
diff -Nru r-cran-readxl-1.4.0/debian/changelog r-cran-readxl-1.4.1/debian/changelog
--- r-cran-readxl-1.4.0/debian/changelog	2022-03-28 19:18:13.000000000 +0000
+++ r-cran-readxl-1.4.1/debian/changelog	2022-08-20 23:24:16.000000000 +0000
@@ -1,3 +1,12 @@
+r-cran-readxl (1.4.1-1) unstable; urgency=medium
+
+  * New upstream release
+  
+  * debian/control: Set Standards-Version: to current version 
+  * debian/control: Set Build-Depends: to current R version
+
+ -- Dirk Eddelbuettel <edd@debian.org>  Sat, 20 Aug 2022 18:24:16 -0500
+
 r-cran-readxl (1.4.0-1) unstable; urgency=medium
 
   * New upstream release
diff -Nru r-cran-readxl-1.4.0/debian/control r-cran-readxl-1.4.1/debian/control
--- r-cran-readxl-1.4.0/debian/control	2022-03-28 19:18:13.000000000 +0000
+++ r-cran-readxl-1.4.1/debian/control	2022-08-20 23:24:02.000000000 +0000
@@ -2,8 +2,8 @@
 Section: gnu-r
 Priority: optional
 Maintainer: Dirk Eddelbuettel <edd@debian.org>
-Build-Depends: debhelper-compat (= 11), r-base-dev (>= 3.6.1), dh-r, r-cran-cpp11, r-cran-tibble, r-cran-cellranger, r-cran-progress
-Standards-Version: 4.4.0
+Build-Depends: debhelper-compat (= 11), r-base-dev (>= 4.2.1), dh-r, r-cran-cpp11, r-cran-tibble, r-cran-cellranger, r-cran-progress
+Standards-Version: 4.6.1
 Vcs-Browser: https://salsa.debian.org/edd/r-cran-readxl
 Vcs-Git: https://salsa.debian.org/edd/r-cran-readxl.git
 Homepage: https://cran.r-project.org/package=readxl
diff -Nru r-cran-readxl-1.4.0/debian/source.lintian-overrides r-cran-readxl-1.4.1/debian/source.lintian-overrides
--- r-cran-readxl-1.4.0/debian/source.lintian-overrides	1970-01-01 00:00:00.000000000 +0000
+++ r-cran-readxl-1.4.1/debian/source.lintian-overrides	2022-08-20 23:24:16.000000000 +0000
@@ -0,0 +1,2 @@
+r-cran-readxl source: source-is-missing [inst/doc/cell-and-column-types.html]
+r-cran-readxl source: source-is-missing [inst/doc/sheet-geometry.html]
diff -Nru r-cran-readxl-1.4.0/DESCRIPTION r-cran-readxl-1.4.1/DESCRIPTION
--- r-cran-readxl-1.4.0/DESCRIPTION	2022-03-28 18:30:02.000000000 +0000
+++ r-cran-readxl-1.4.1/DESCRIPTION	2022-08-17 15:50:02.000000000 +0000
@@ -1,6 +1,6 @@
 Package: readxl
 Title: Read Excel Files
-Version: 1.4.0
+Version: 1.4.1
 Authors@R: c(
     person("Hadley", "Wickham", , "hadley@rstudio.com", role = "aut",
            comment = c(ORCID = "0000-0003-4757-117X")),
@@ -37,10 +37,10 @@
 Config/testthat/edition: 3
 Encoding: UTF-8
 Note: libxls v1.6.2 4482400
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.1
 SystemRequirements: C++11
 NeedsCompilation: yes
-Packaged: 2022-03-28 17:49:29 UTC; jenny
+Packaged: 2022-08-17 14:30:18 UTC; jenny
 Author: Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>),
   Jennifer Bryan [aut, cre] (<https://orcid.org/0000-0002-6983-2759>),
   RStudio [cph, fnd] (Copyright holder of all R code and all C/C++ code
@@ -53,4 +53,4 @@
   Evan Miller [ctb, cph] (Author of included libxls code)
 Maintainer: Jennifer Bryan <jenny@rstudio.com>
 Repository: CRAN
-Date/Publication: 2022-03-28 18:30:02 UTC
+Date/Publication: 2022-08-17 15:50:02 UTC
diff -Nru r-cran-readxl-1.4.0/inst/doc/cell-and-column-types.html r-cran-readxl-1.4.1/inst/doc/cell-and-column-types.html
--- r-cran-readxl-1.4.0/inst/doc/cell-and-column-types.html	2022-03-28 17:49:23.000000000 +0000
+++ r-cran-readxl-1.4.1/inst/doc/cell-and-column-types.html	2022-08-17 14:30:12.000000000 +0000
@@ -38,6 +38,7 @@
     </style>
 
 
+
 <style type="text/css">
   code {
     white-space: pre;
@@ -368,13 +369,14 @@
 which ships with readxl.</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">read_excel</span>(<span class="fu">readxl_example</span>(<span class="st">&quot;deaths.xlsx&quot;</span>), <span class="at">range =</span> <span class="fu">cell_rows</span>(<span class="dv">5</span><span class="sc">:</span><span class="dv">15</span>))</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 10 × 6</span></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   Name       Profession   Age `Has kids` `Date of birth`     `Date of death`    </span></span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;      &lt;chr&gt;      &lt;dbl&gt; &lt;lgl&gt;      &lt;dttm&gt;              &lt;dttm&gt;             </span></span>
-<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 David Bow… musician      69 TRUE       1947-01-08 00:00:00 2016-01-10 00:00:00</span></span>
-<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2 Carrie Fi… actor         60 TRUE       1956-10-21 00:00:00 2016-12-27 00:00:00</span></span>
-<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3 Chuck Ber… musician      90 TRUE       1926-10-18 00:00:00 2017-03-18 00:00:00</span></span>
-<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4 Bill Paxt… actor         61 TRUE       1955-05-17 00:00:00 2017-02-25 00:00:00</span></span>
-<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 6 more rows</span></span></code></pre></div>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   Name          Profession   Age Has k…¹ `Date of birth`     `Date of death`    </span></span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;         &lt;chr&gt;      &lt;dbl&gt; &lt;lgl&gt;   &lt;dttm&gt;              &lt;dttm&gt;             </span></span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 David Bowie   musician      69 TRUE    1947-01-08 00:00:00 2016-01-10 00:00:00</span></span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2 Carrie Fisher actor         60 TRUE    1956-10-21 00:00:00 2016-12-27 00:00:00</span></span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3 Chuck Berry   musician      90 TRUE    1926-10-18 00:00:00 2017-03-18 00:00:00</span></span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4 Bill Paxton   actor         61 TRUE    1955-05-17 00:00:00 2017-02-25 00:00:00</span></span>
+<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 6 more rows, and abbreviated variable name ¹​`Has kids`</span></span>
+<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # ℹ Use `print(n = ...)` to see more rows</span></span></code></pre></div>
 </div>
 <div id="excel-types-r-types-col_types" class="section level2">
 <h2>Excel types, R types, <code>col_types</code></h2>
@@ -455,7 +457,8 @@
 <span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2 Carrie Fisher    60</span></span>
 <span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3 Chuck Berry      90</span></span>
 <span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4 Bill Paxton      61</span></span>
-<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 6 more rows</span></span></code></pre></div>
+<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 6 more rows</span></span>
+<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # ℹ Use `print(n = ...)` to see more rows</span></span></code></pre></div>
 <p>More about the <code>&quot;list&quot;</code> column type in the last row:</p>
 <ul>
 <li>This will create a list-column in the output, each component of
@@ -533,11 +536,11 @@
 above:</p>
 <div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>deaths <span class="ot">&lt;-</span> <span class="fu">read_excel</span>(<span class="fu">readxl_example</span>(<span class="st">&quot;deaths.xlsx&quot;</span>))</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; New names:</span></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...2</span></span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...3</span></span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...4</span></span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...5</span></span>
-<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...6</span></span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...2`</span></span>
+<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...3`</span></span>
+<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...4`</span></span>
+<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...5`</span></span>
+<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...6`</span></span>
 <span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(deaths, <span class="at">n =</span> <span class="cn">Inf</span>)</span>
 <span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 18 × 6</span></span>
 <span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;    `Lots of people`             ...2                     ...3  ...4  ...5  ...6 </span></span>
@@ -590,7 +593,8 @@
 <span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2          4.9         3   1.4          0.2         setosa </span></span>
 <span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3          4.7         3.2 1.3          0.2         setosa </span></span>
 <span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4          4.6         3.1 1.5          0.2         setosa </span></span>
-<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 146 more rows</span></span></code></pre></div>
+<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 146 more rows</span></span>
+<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # ℹ Use `print(n = ...)` to see more rows</span></span></code></pre></div>
 </div>
 </div>
 <div id="square-pegs-in-round-holes" class="section level2">
diff -Nru r-cran-readxl-1.4.0/inst/doc/sheet-geometry.html r-cran-readxl-1.4.1/inst/doc/sheet-geometry.html
--- r-cran-readxl-1.4.0/inst/doc/sheet-geometry.html	2022-03-28 17:49:24.000000000 +0000
+++ r-cran-readxl-1.4.1/inst/doc/sheet-geometry.html	2022-08-17 14:30:13.000000000 +0000
@@ -38,6 +38,7 @@
     </style>
 
 
+
 <style type="text/css">
   code {
     white-space: pre;
@@ -506,13 +507,14 @@
 guesses for the column types.</p>
 <div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">read_excel</span>(<span class="fu">readxl_example</span>(<span class="st">&quot;deaths.xlsx&quot;</span>), <span class="at">range =</span> <span class="st">&quot;arts!A5:F15&quot;</span>)</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 10 × 6</span></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   Name       Profession   Age `Has kids` `Date of birth`     `Date of death`    </span></span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;      &lt;chr&gt;      &lt;dbl&gt; &lt;lgl&gt;      &lt;dttm&gt;              &lt;dttm&gt;             </span></span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 David Bow… musician      69 TRUE       1947-01-08 00:00:00 2016-01-10 00:00:00</span></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2 Carrie Fi… actor         60 TRUE       1956-10-21 00:00:00 2016-12-27 00:00:00</span></span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3 Chuck Ber… musician      90 TRUE       1926-10-18 00:00:00 2017-03-18 00:00:00</span></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4 Bill Paxt… actor         61 TRUE       1955-05-17 00:00:00 2017-02-25 00:00:00</span></span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 6 more rows</span></span></code></pre></div>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   Name          Profession   Age Has k…¹ `Date of birth`     `Date of death`    </span></span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;         &lt;chr&gt;      &lt;dbl&gt; &lt;lgl&gt;   &lt;dttm&gt;              &lt;dttm&gt;             </span></span>
+<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 David Bowie   musician      69 TRUE    1947-01-08 00:00:00 2016-01-10 00:00:00</span></span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2 Carrie Fisher actor         60 TRUE    1956-10-21 00:00:00 2016-12-27 00:00:00</span></span>
+<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3 Chuck Berry   musician      90 TRUE    1926-10-18 00:00:00 2017-03-18 00:00:00</span></span>
+<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4 Bill Paxton   actor         61 TRUE    1955-05-17 00:00:00 2017-02-25 00:00:00</span></span>
+<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 6 more rows, and abbreviated variable name ¹​`Has kids`</span></span>
+<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # ℹ Use `print(n = ...)` to see more rows</span></span></code></pre></div>
 <p>We repeat the screenshot of <code>geometry.xlsx</code> as a visual
 reference.</p>
 <p><img 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" width="70%" /></p>
@@ -522,9 +524,9 @@
 of <code>NA</code>s.</p>
 <div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">read_excel</span>(<span class="fu">readxl_example</span>(<span class="st">&quot;geometry.xlsx&quot;</span>), <span class="at">range =</span> <span class="st">&quot;A2:C4&quot;</span>)</span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; New names:</span></span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...1</span></span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...2</span></span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...3</span></span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...1`</span></span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...2`</span></span>
+<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...3`</span></span>
 <span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 2 × 3</span></span>
 <span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   ...1  ...2  ...3 </span></span>
 <span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;lgl&gt; &lt;chr&gt; &lt;chr&gt;</span></span>
@@ -580,8 +582,8 @@
 <span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">range =</span> <span class="fu">cell_limits</span>(<span class="fu">c</span>(<span class="dv">5</span>, <span class="dv">3</span>), <span class="fu">c</span>(<span class="cn">NA</span>, <span class="cn">NA</span>))</span>
 <span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; New names:</span></span>
-<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...1</span></span>
-<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; * `` -&gt; ...2</span></span>
+<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...1`</span></span>
+<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; • `` -&gt; `...2`</span></span>
 <span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 2 × 2</span></span>
 <span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   ...1  ...2 </span></span>
 <span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt; &lt;chr&gt;</span></span>
diff -Nru r-cran-readxl-1.4.0/inst/WORDLIST r-cran-readxl-1.4.1/inst/WORDLIST
--- r-cran-readxl-1.4.0/inst/WORDLIST	1970-01-01 00:00:00.000000000 +0000
+++ r-cran-readxl-1.4.1/inst/WORDLIST	2022-08-17 14:28:04.000000000 +0000
@@ -0,0 +1,75 @@
+BoolErr
+CMD
+CVE
+CVEs
+CentOS
+Cheatsheet
+Christophe
+Clippy
+Codecov
+Coercions
+Hoerl
+JMP
+Komarov
+Leitienne
+ORCID
+POSIXct
+RapidXML
+Rcpp
+Reproducibility
+SHA
+SpreadsheetML
+backtick
+behaviour
+cellranger
+chartsheets
+cheatsheet
+colours
+conformant
+cpp
+csv
+datetime
+datetimes
+dll
+dplyr
+epplussoftware
+foofy
+funder
+gcc
+gdata
+ggplot
+github
+googlesheets
+gridded
+https
+ish
+knitr
+libxls
+libxlsxwriter
+lifecycle
+metapackage
+minimalistic
+openxlsx
+pre
+programmatically
+purrr
+rapidxml's
+readr
+readxl's
+reproducibility
+rmarkdown
+targetting
+tibble
+tidyverse
+tidyxl
+timezones
+travelling
+unicode
+unskipped
+vulnerabilitities
+workspaces
+writexl
+www
+xls
+xlsReadWrite
+xlsx
diff -Nru r-cran-readxl-1.4.0/man/readxl-package.Rd r-cran-readxl-1.4.1/man/readxl-package.Rd
--- r-cran-readxl-1.4.0/man/readxl-package.Rd	2022-03-23 18:30:29.000000000 +0000
+++ r-cran-readxl-1.4.1/man/readxl-package.Rd	2022-08-17 05:44:10.000000000 +0000
@@ -6,9 +6,9 @@
 \alias{readxl-package}
 \title{readxl: Read Excel Files}
 \description{
-\if{html}{\figure{logo.png}{options: align='right' alt='logo' width='120'}}
+\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
 
-Import excel files into R. Supports '.xls' via the embedded 'libxls' C library <https://github.com/libxls/libxls> and '.xlsx' via the embedded 'RapidXML' C++ library <http://rapidxml.sourceforge.net>. Works on Windows, Mac and Linux without external dependencies.
+Import excel files into R. Supports '.xls' via the embedded 'libxls' C library \url{https://github.com/libxls/libxls} and '.xlsx' via the embedded 'RapidXML' C++ library \url{http://rapidxml.sourceforge.net}. Works on Windows, Mac and Linux without external dependencies.
 }
 \seealso{
 Useful links:
diff -Nru r-cran-readxl-1.4.0/MD5 r-cran-readxl-1.4.1/MD5
--- r-cran-readxl-1.4.0/MD5	2022-03-28 18:30:02.000000000 +0000
+++ r-cran-readxl-1.4.1/MD5	2022-08-17 15:50:02.000000000 +0000
@@ -1,8 +1,8 @@
-64a30e9360dcc943165e19860bb9ef97 *DESCRIPTION
+217006dcdc7568d5145e328fcf471fb5 *DESCRIPTION
 0019f8fa081b43e2587202d3146b66d0 *LICENSE
 649f255cd07b5c31b23b75c6e64e6e77 *LICENSE.note
 27f67e77c2508560a509e361be260fae *NAMESPACE
-19c9e2d695b9d4fe65e69feb881a7f25 *NEWS.md
+f8fc1f47af266430559df5a481b5c047 *NEWS.md
 81cf0bd7612517c2824b6950903f0f4c *R/cell-specification.R
 b6a023bd562d106f127a1a5a5e7823a0 *R/cpp11.R
 fa1193df7511b89f9a836be71861970a *R/example.R
@@ -13,14 +13,15 @@
 df6cc46bc7fae1a55b713f3d5065b35a *R/readxl-package.R
 13d9edcda97dcff96a707ad1a4458439 *R/utils.R
 1e192abda26e50fbc77cffd77a278b91 *R/xlsx-zip.R
-5b160d134a381c5fb62baee5311f864a *README.md
-0b767738657809c68af0bf9e160d858f *build/vignette.rds
+42a14383462f7e386935347fbefb0645 *README.md
+b3f0729776c2ea79f09e72ab9b1ffb5e *build/vignette.rds
+ba229a9ed846153ad70e321883c95f3b *inst/WORDLIST
 b02e5a51ee21260c491bebf6ce1582a8 *inst/doc/cell-and-column-types.R
 0f11d010e7d6b7096bbfc446cd876b8f *inst/doc/cell-and-column-types.Rmd
-955cb20fb0aa24e648130a1807e4c96c *inst/doc/cell-and-column-types.html
+159a4620702b28a4d5727a3a5a994c38 *inst/doc/cell-and-column-types.html
 db88d4da1c71354df8bb952fe76bcf14 *inst/doc/sheet-geometry.R
 4ddde17673c05de9ea835144ee7b9b42 *inst/doc/sheet-geometry.Rmd
-c4235e2cc2ece12aeaf7e15f9f8bec1a *inst/doc/sheet-geometry.html
+83b85dbf63a088163127ee89fb8b48d5 *inst/doc/sheet-geometry.html
 0926fd270eadb9855b99c4c8312e7580 *inst/extdata/clippy.xls
 23e4ef54a85bdd4c9e485c56d12f8080 *inst/extdata/clippy.xlsx
 5611040674e27c7429877740235d4381 *inst/extdata/datasets.xls
@@ -36,7 +37,7 @@
 cbf1ba9c1db5f6f412a97af2d97b44a1 *man/excel_sheets.Rd
 c044a37e3e9f3b4345bd43072c410573 *man/figures/logo.png
 c96daca6d65e891ec4cba4ce3d1eab02 *man/read_excel.Rd
-e5b3c1bb218d9e31894b9805a77b1f20 *man/readxl-package.Rd
+83efdac3ac6605e49ef5fcfc5af028a2 *man/readxl-package.Rd
 953d99864dfdea591fe4a60f0c05a25a *man/readxl_example.Rd
 8ab364897a1e08f1e74e95eb947c6d6f *man/readxl_progress.Rd
 a7ac412348449800a95aa21b6676f249 *src/CellLimits.h
diff -Nru r-cran-readxl-1.4.0/NEWS.md r-cran-readxl-1.4.1/NEWS.md
--- r-cran-readxl-1.4.0/NEWS.md	2022-03-28 17:47:50.000000000 +0000
+++ r-cran-readxl-1.4.1/NEWS.md	2022-08-17 14:25:21.000000000 +0000
@@ -1,3 +1,7 @@
+# readxl 1.4.1
+
+Help files below `man/` have been re-generated, so that they give rise to valid HTML5. (This is the impetus for this release, to keep the package safely on CRAN.)
+
 # readxl 1.4.0
 
 This release is mostly about substantial internal changes that should not be
diff -Nru r-cran-readxl-1.4.0/README.md r-cran-readxl-1.4.1/README.md
--- r-cran-readxl-1.4.0/README.md	2022-03-24 15:29:01.000000000 +0000
+++ r-cran-readxl-1.4.1/README.md	2022-08-17 05:50:16.000000000 +0000
@@ -75,7 +75,7 @@
 #>  [5] "deaths.xls"    "deaths.xlsx"   "geometry.xls"  "geometry.xlsx"
 #>  [9] "type-me.xls"   "type-me.xlsx"
 readxl_example("clippy.xls")
-#> [1] "/private/tmp/RtmpxLRgSx/temp_libpath73d16b200d00/readxl/extdata/clippy.xls"
+#> [1] "/private/tmp/RtmpG5ZAUG/temp_libpath1374b3dd1a473/readxl/extdata/clippy.xls"
 ```
 
 `read_excel()` reads both xls and xlsx files and detects the format from
@@ -91,6 +91,7 @@
 #> 2          4.9         3            1.4         0.2 setosa 
 #> 3          4.7         3.2          1.3         0.2 setosa 
 #> # … with 147 more rows
+#> # ℹ Use `print(n = ...)` to see more rows
 
 xls_example <- readxl_example("datasets.xls")
 read_excel(xls_example)
@@ -101,6 +102,7 @@
 #> 2          4.9         3            1.4         0.2 setosa 
 #> 3          4.7         3.2          1.3         0.2 setosa 
 #> # … with 147 more rows
+#> # ℹ Use `print(n = ...)` to see more rows
 ```
 
 List the sheet names with `excel_sheets()`.
@@ -121,6 +123,7 @@
 #> 2    160 horsebean
 #> 3    136 horsebean
 #> # … with 68 more rows
+#> # ℹ Use `print(n = ...)` to see more rows
 read_excel(xls_example, sheet = 4)
 #> # A tibble: 1,000 × 5
 #>     lat  long depth   mag stations
@@ -129,6 +132,7 @@
 #> 2 -20.6  181.   650   4.2       15
 #> 3 -26    184.    42   5.4       43
 #> # … with 997 more rows
+#> # ℹ Use `print(n = ...)` to see more rows
 ```
 
 There are various ways to control which cells are read. You can even
@@ -164,6 +168,7 @@
 #> 2         3            1.4         0.2
 #> 3         3.2          1.3         0.2
 #> # … with 147 more rows
+#> # ℹ Use `print(n = ...)` to see more rows
 read_excel(xlsx_example, range = "mtcars!B1:D5")
 #> # A tibble: 4 × 3
 #>     cyl  disp    hp
@@ -172,6 +177,7 @@
 #> 2     6   160   110
 #> 3     4   108    93
 #> # … with 1 more row
+#> # ℹ Use `print(n = ...)` to see more rows
 ```
 
 If `NA`s are represented by something other than blank cells, set the
@@ -186,6 +192,7 @@
 #> 2          4.9         3            1.4         0.2 <NA>   
 #> 3          4.7         3.2          1.3         0.2 <NA>   
 #> # … with 147 more rows
+#> # ℹ Use `print(n = ...)` to see more rows
 ```
 
 If you are new to the tidyverse conventions for data import, you may