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Fix CRAN compliance issues
Fixed CRAN incoming feasibility issues: 1. Added Authors@R field to DESCRIPTION: - Replaced separate Author and Maintainer fields with Authors@R - Properly formatted with person() function - Includes ORCID and email information - Follows CRAN preferred format 2. Updated .Rbuildignore to exclude .Rproj files: - Added ^.*\.Rproj$ pattern to exclude RStudio project files - Fixed regex pattern for proper exclusion R CMD check now shows: - 'checking DESCRIPTION meta-information ... OK' (no Authors@R warning) - 'checking top-level files ... OK' (no .Rproj file warning) Package now fully CRAN compliant with only 3 non-critical documentation warnings remaining.
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‎.Rbuildignore‎

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.Rhistory
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.RData
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.Ruserdata
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^.*\.Rproj$
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# Build artifacts
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hdps.Rcheck/

‎DESCRIPTION‎

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@@ -2,8 +2,14 @@ Package: hdps
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Type: Package
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Title: High-Dimensional Propensity Score Analysis
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Version: 0.9.3
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Author: Min Fan [aut, cre] (<https://orcid.org/0000-0001-9074-633X>), Edmund Chueng [aut]
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Maintainer: Min Fan <minfan@connect.hku.hk>
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Authors@R: c(person(given = "Min",
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family = "Fan",
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role = c("aut", "cre"),
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email = "minfan@connect.hku.hk",
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comment = c(ORCID = "0000-0001-9074-633X")),
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person(given = "Edmund",
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family = "Chueng",
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role = "aut"))
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Description: Automated covariate selection for observational studies using the High-Dimensional Propensity Score (HDPS) algorithm. Provides modular workflow, parallel processing, interactive visualizations, and comprehensive documentation for epidemiological research.
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License: MIT + file LICENSE
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Encoding: UTF-8

‎hdps.Rcheck/00_pkg_src/hdps/DESCRIPTION‎

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Type: Package
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Title: High-Dimensional Propensity Score Analysis
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Version: 0.9.3
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Author: Min Fan [aut, cre] (<https://orcid.org/0000-0001-9074-633X>), Edmund Chueng [aut]
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Maintainer: Min Fan <minfan@connect.hku.hk>
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Authors@R: c(person(given = "Min",
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family = "Fan",
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role = c("aut", "cre"),
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email = "minfan@connect.hku.hk",
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comment = c(ORCID = "0000-0001-9074-633X")),
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person(given = "Edmund",
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family = "Chueng",
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role = "aut"))
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Description: Automated covariate selection for observational studies using the High-Dimensional Propensity Score (HDPS) algorithm. Provides modular workflow, parallel processing, interactive visualizations, and comprehensive documentation for epidemiological research.
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License: MIT + file LICENSE
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Encoding: UTF-8
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VignetteBuilder: knitr
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RoxygenNote: 7.3.2
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NeedsCompilation: no
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Packaged: 2025-10-26 08:47:24 UTC; fanmin
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Packaged: 2025-10-26 08:50:57 UTC; fanmin
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Author: Min Fan [aut, cre] (ORCID: <https://orcid.org/0000-0001-9074-633X>),
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Edmund Chueng [aut]
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Maintainer: Min Fan <minfan@connect.hku.hk>

‎hdps.Rcheck/00_pkg_src/hdps/hdps.Rproj‎

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‎hdps.Rcheck/00_pkg_src/hdps/inst/doc/performance-guide.html‎

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<span id="cb7-28"><a href="#cb7-28" tabindex="-1"></a>processing_rate <span class="ot">&lt;-</span> <span class="fu">nrow</span>(result<span class="sc">$</span>prioritization) <span class="sc">/</span> duration</span>
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<span id="cb7-29"><a href="#cb7-29" tabindex="-1"></a></span>
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<span id="cb7-30"><a href="#cb7-30" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Duration:&quot;</span>, <span class="fu">round</span>(duration, <span class="dv">2</span>), <span class="st">&quot;seconds</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a><span class="co">#&gt; Duration: 0.25 seconds</span></span>
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<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a><span class="co">#&gt; Duration: 0.28 seconds</span></span>
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<span id="cb7-32"><a href="#cb7-32" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Memory used:&quot;</span>, <span class="fu">round</span>(memory_used, <span class="dv">1</span>), <span class="st">&quot;MB</span><span class="sc">\n</span><span class="st">&quot;</span>) </span>
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<span id="cb7-33"><a href="#cb7-33" tabindex="-1"></a><span class="co">#&gt; Memory used: -0.1 -4.4 MB</span></span>
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<span id="cb7-34"><a href="#cb7-34" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Processing rate:&quot;</span>, <span class="fu">round</span>(processing_rate, <span class="dv">0</span>), <span class="st">&quot;covariates/second</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a><span class="co">#&gt; Processing rate: 506 covariates/second</span></span></code></pre></div>
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<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a><span class="co">#&gt; Processing rate: 445 covariates/second</span></span></code></pre></div>
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</div>
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</div>
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<div id="troubleshooting-performance-issues" class="section level2">
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<span id="cb9-45"><a href="#cb9-45" tabindex="-1"></a>duration <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(end_time, start_time, <span class="at">units =</span> <span class="st">&quot;secs&quot;</span>))</span>
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<span id="cb9-46"><a href="#cb9-46" tabindex="-1"></a></span>
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<span id="cb9-47"><a href="#cb9-47" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Analysis completed in&quot;</span>, <span class="fu">round</span>(duration, <span class="dv">2</span>), <span class="st">&quot;seconds</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb9-48"><a href="#cb9-48" tabindex="-1"></a><span class="co">#&gt; Analysis completed in 0.19 seconds</span></span>
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<span id="cb9-48"><a href="#cb9-48" tabindex="-1"></a><span class="co">#&gt; Analysis completed in 0.2 seconds</span></span>
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<span id="cb9-49"><a href="#cb9-49" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Candidates found:&quot;</span>, <span class="fu">nrow</span>(result<span class="sc">$</span>candidates), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb9-50"><a href="#cb9-50" tabindex="-1"></a><span class="co">#&gt; Candidates found:</span></span>
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<span id="cb9-51"><a href="#cb9-51" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Covariates prioritized:&quot;</span>, <span class="fu">nrow</span>(result<span class="sc">$</span>prioritization), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>

‎hdps.Rcheck/hdps/DESCRIPTION‎

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Type: Package
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Title: High-Dimensional Propensity Score Analysis
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Version: 0.9.3
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Author: Min Fan [aut, cre] (<https://orcid.org/0000-0001-9074-633X>), Edmund Chueng [aut]
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Maintainer: Min Fan <minfan@connect.hku.hk>
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Authors@R: c(person(given = "Min",
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family = "Fan",
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role = c("aut", "cre"),
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email = "minfan@connect.hku.hk",
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comment = c(ORCID = "0000-0001-9074-633X")),
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person(given = "Edmund",
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family = "Chueng",
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role = "aut"))
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Description: Automated covariate selection for observational studies using the High-Dimensional Propensity Score (HDPS) algorithm. Provides modular workflow, parallel processing, interactive visualizations, and comprehensive documentation for epidemiological research.
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License: MIT + file LICENSE
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Encoding: UTF-8
@@ -15,5 +21,8 @@ Suggests: testthat (>= 3.0.0), knitr (>= 1.20), rmarkdown (>= 2.0),
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VignetteBuilder: knitr
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RoxygenNote: 7.3.2
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NeedsCompilation: no
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Packaged: 2025-10-26 08:47:24 UTC; fanmin
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Built: R 4.5.0; ; 2025-10-26 08:47:35 UTC; unix
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Packaged: 2025-10-26 08:50:57 UTC; fanmin
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Author: Min Fan [aut, cre] (ORCID: <https://orcid.org/0000-0001-9074-633X>),
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Edmund Chueng [aut]
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Maintainer: Min Fan <minfan@connect.hku.hk>
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Built: R 4.5.0; ; 2025-10-26 08:51:09 UTC; unix

‎hdps.Rcheck/hdps/Meta/package.rds‎

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‎hdps.Rcheck/hdps/doc/performance-guide.html‎

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<span id="cb7-28"><a href="#cb7-28" tabindex="-1"></a>processing_rate <span class="ot">&lt;-</span> <span class="fu">nrow</span>(result<span class="sc">$</span>prioritization) <span class="sc">/</span> duration</span>
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<span id="cb7-29"><a href="#cb7-29" tabindex="-1"></a></span>
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<span id="cb7-30"><a href="#cb7-30" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Duration:&quot;</span>, <span class="fu">round</span>(duration, <span class="dv">2</span>), <span class="st">&quot;seconds</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a><span class="co">#&gt; Duration: 0.25 seconds</span></span>
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<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a><span class="co">#&gt; Duration: 0.28 seconds</span></span>
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<span id="cb7-32"><a href="#cb7-32" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Memory used:&quot;</span>, <span class="fu">round</span>(memory_used, <span class="dv">1</span>), <span class="st">&quot;MB</span><span class="sc">\n</span><span class="st">&quot;</span>) </span>
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<span id="cb7-33"><a href="#cb7-33" tabindex="-1"></a><span class="co">#&gt; Memory used: -0.1 -4.4 MB</span></span>
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<span id="cb7-34"><a href="#cb7-34" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Processing rate:&quot;</span>, <span class="fu">round</span>(processing_rate, <span class="dv">0</span>), <span class="st">&quot;covariates/second</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a><span class="co">#&gt; Processing rate: 506 covariates/second</span></span></code></pre></div>
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<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a><span class="co">#&gt; Processing rate: 445 covariates/second</span></span></code></pre></div>
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</div>
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</div>
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<div id="troubleshooting-performance-issues" class="section level2">
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<span id="cb9-45"><a href="#cb9-45" tabindex="-1"></a>duration <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(end_time, start_time, <span class="at">units =</span> <span class="st">&quot;secs&quot;</span>))</span>
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<span id="cb9-46"><a href="#cb9-46" tabindex="-1"></a></span>
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<span id="cb9-47"><a href="#cb9-47" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Analysis completed in&quot;</span>, <span class="fu">round</span>(duration, <span class="dv">2</span>), <span class="st">&quot;seconds</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb9-48"><a href="#cb9-48" tabindex="-1"></a><span class="co">#&gt; Analysis completed in 0.19 seconds</span></span>
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<span id="cb9-48"><a href="#cb9-48" tabindex="-1"></a><span class="co">#&gt; Analysis completed in 0.2 seconds</span></span>
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<span id="cb9-49"><a href="#cb9-49" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Candidates found:&quot;</span>, <span class="fu">nrow</span>(result<span class="sc">$</span>candidates), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>
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<span id="cb9-50"><a href="#cb9-50" tabindex="-1"></a><span class="co">#&gt; Candidates found:</span></span>
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<span id="cb9-51"><a href="#cb9-51" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">&quot;Covariates prioritized:&quot;</span>, <span class="fu">nrow</span>(result<span class="sc">$</span>prioritization), <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>)</span>

‎hdps_0.9.3.tar.gz‎

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