added R package downloads count

konfound-project · Feb 13, 2024 · bd6e2e4 · bd6e2e4
1 parent 798b78f
commit bd6e2e4
Show file tree

Hide file tree

Showing 17 changed files with 148 additions and 57 deletions.
diff --git a/content/_index.md b/content/_index.md
@@ -1,5 +1,7 @@
 **Sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.**
 
+{{< awesome fas fa-paper-plane >}} Questions? Issues? Suggestions? Reach out through the [KounFound-It! Google Group](https://groups.google.com/g/konfound-it).
+
 ---
 
 {{< columns >}}

diff --git a/content/about.md b/content/about.md
@@ -7,7 +7,16 @@ comments: false
 
 ---
 
-Welcome to our space about **sensitivity analysis**. All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That's where sensitivity analysis comes in&#8212;so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as: 
+Welcome to our space about **[sensitivity analysis](https://en.wikipedia.org/wiki/Sensitivity_analysis)**. 
+
+{{< awesome fas fa-paper-plane >}} Questions? Issues? Suggestions? Reach out through the [KounFound-It! Google Group](https://groups.google.com/g/konfound-it).
+
+---
+
+
+## Purpose
+
+All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That's where sensitivity analysis comes in&#8212;so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as: 
 
 > XX% of the estimated effect would have to be due to bias to change your inference about the effect.
 
@@ -17,6 +26,7 @@ Our work in the KonFound-It project is to develop&#8212;and make easy to use&#82
 
 ---
 
+
 ## People
 
 We are a group of researchers spanning numerous institutions who would like to contribute to better communications of research inferences and findings.
@@ -46,37 +56,51 @@ We are a group of researchers spanning numerous institutions who would like to c
 
 ---
 
+
 ## Tools
 
-### KonFound-It! Shiny App
+### {{< awesome fas fa-calculator >}} KonFound-It! Shiny App
 
 Rosenberg, J. M., Narvaiz, S., Xu, R., Lin, Q., Maroulis, S., Frank, K. A., Saw, G. & Staudt Willet, K. B. (2023). *KonFound-it!: Quantify the robustness of causal inferences* (v. 2.0.0). https://konfound-project.shinyapps.io/konfound-it/
 
 
-### KonFound R Package
+### {{< awesome fas fa-code >}} KonFound R Package
 
 Rosenberg, J. M., Xu, R., Lin, Q., Maroulis, S., & Frank, K. A. (2023). *konfound: Quantify the robustness of causal inferences* (v. 0.4.0). https://CRAN.R-project.org/package=konfound 
 
-### KonFound Stata Package
+| Monthly Downloads | Total Downloads |
+| :---------------- | :-------------- |
+| ![](https://cranlogs.r-pkg.org/badges/konfound?color=9bbb59) | ![](https://cranlogs.r-pkg.org/badges/grand-total/konfound?color=9bbb59) |
+
+
+### {{< awesome fas fa-code >}} KonFound Stata Package
 
 Xu, R., Frank, K. A., Maroulis, S. J., & Rosenberg, J. M. (2019). konfound: Command to quantify robustness of causal inferences. *The Stata Journal, 19*(3), 523-550. https://doi.org/10.1177/1536867X19874223
 
+
+### Benchmarks: What Works Clearinghouse
+
+COMING SOON
+
 ---
 
+
 ## Internal Resources
 
 Be sure to look through the variety of supports for KonFound:
 
 - [FAQ](/page/faq)
 - [Resource Overview](/page/resources)
 - [Publications](/page/publications)
+- [Talks](/page/talks)
 - [Workshops](/page/workshop)
 - [User Guide]() - COMING SOON
 - [Blog]() - COMING SOON
-- [Forum]() - COMING SOON
+- [Forum (Google Group)](https://groups.google.com/g/konfound-it)
 
 ---
 
+
 ## External Resources
 
 We refer to a lot of open resources for building this site, including:
@@ -88,9 +112,9 @@ We refer to a lot of open resources for building this site, including:
 
 ## Connect
 
+- [{{< awesome fas fa-paper-plane >}} Join our Google Group mailing list](https://groups.google.com/forum/#!forum/konfound-it) for updates on KonFound workshops, publications, progress in related research!
 - [{{< awesome fas fa-globe >}} Visit Dr. Ken Frank's website](https://msu.edu/~kenfrank/research.htm#causal) for even more relevant and related resources.
 - [{{< awesome fas fa-envelope >}} Email Dr. Ken Frank](mailto:[email protected]) with any questions or suggestions.
-- [{{< awesome fas fa-paper-plane >}} Join our mailing list](https://groups.google.com/forum/#!forum/konfound-it) for updates on KonFound workshops, publications, progress in related research!
 - [{{< awesome fab fa-github >}} Open an issue in the KonFound GitHub.](https://github.com/konfound-project/konfound/issues)
 
 ### Thanks for visiting! Happy KonFounding!
diff --git a/content/post/2023-08-13-faq.md b/content/post/2023-08-13-faq.md
@@ -8,8 +8,13 @@ math: true
 
 ## Introduction
 
-This document addresses some frequently asked questions regarding KonFound. See the appendices for background readings and software. For quick reference, see:
+This document addresses some frequently asked questions regarding KonFound. 
 
+{{< awesome fas fa-paper-plane >}} Questions? Issues? Suggestions? Reach out through the [KounFound-It! Google Group](https://groups.google.com/g/konfound-it).
+
+See the appendices for background readings and software. For quick reference, visit:
+
+- [Development version of the FAQ](https://www.dropbox.com/s/9eymdekym5g50o7/frequently asked questions for application of konfound-it.docx)
 - [Overview of all KonFound commands](https://www.dropbox.com/s/33zkk861g04hocf/Overview%20of%20KonFound%20commands%20with%20inputs%20and%20outputs.docx?dl=0)
 - [Main introductory slides for combined frameworks](https://www.dropbox.com/scl/fi/jfhwfuim4d001usup81c8/quantifying-the-robustness-of-causal-inferences-combined-frameworks-for-stat-horizons-July-23.pptx?rlkey=32srefdir2q6r9pq5998i1q1b&dl=0)
 

diff --git a/docs/about/index.html b/docs/about/index.html
@@ -60,9 +60,9 @@
  "name" : ""
  },
  "headline": "About KonFound-It!",
- "description" : "Welcome to our space about sensitivity analysis. All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That\u0026rsquo;s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:\nXX% of the estimated effect would have to be due to bias to change your inference about the effect.",
+ "description" : "Welcome to our space about sensitivity analysis.\nQuestions? Issues? Suggestions? Reach out through the KounFound-It! Google Group.\nPurpose All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That\u0026rsquo;s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:",
  "inLanguage" : "en",
- "wordCount": 435 ,
+ "wordCount": 458 ,
  "datePublished" : "0001-01-01T00:00:00",
  "dateModified" : "0001-01-01T00:00:00",
  "image" : "https:\/\/konfound-project.github.io\/img\/konfound-logo.png",
@@ -291,7 +291,14 @@ <h1>About KonFound-It!</h1>
  <div class="col-lg-8 col-lg-offset-2 col-md-10 col-md-offset-1">
  <article role="main" class="blog-post">
  <hr>
-<p>Welcome to our space about <strong>sensitivity analysis</strong>. All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That&rsquo;s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:</p>
+<p>Welcome to our space about <strong><a href="https://en.wikipedia.org/wiki/Sensitivity_analysis">sensitivity analysis</a></strong>.</p>
+<p>
+ <i class="fas fa-paper-plane "></i>
+
+ Questions? Issues? Suggestions? Reach out through the <a href="https://groups.google.com/g/konfound-it">KounFound-It! Google Group</a>.</p>
+<hr>
+<h2 id="purpose">Purpose</h2>
+<p>All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That&rsquo;s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:</p>
 <blockquote>
 <p>XX% of the estimated effect would have to be due to bias to change your inference about the effect.</p>
 </blockquote>
@@ -325,23 +332,49 @@ <h2 id="people">People</h2>
 </ul>
 <hr>
 <h2 id="tools">Tools</h2>
-<h3 id="konfound-it-shiny-app">KonFound-It! Shiny App</h3>
+<h3 id="hahahugoshortcode4s1hbhb-konfound-it-shiny-app">
+ <i class="fas fa-calculator "></i>
+
+ KonFound-It! Shiny App</h3>
 <p>Rosenberg, J. M., Narvaiz, S., Xu, R., Lin, Q., Maroulis, S., Frank, K. A., Saw, G. &amp; Staudt Willet, K. B. (2023). <em>KonFound-it!: Quantify the robustness of causal inferences</em> (v. 2.0.0). <a href="https://konfound-project.shinyapps.io/konfound-it/">https://konfound-project.shinyapps.io/konfound-it/</a></p>
-<h3 id="konfound-r-package">KonFound R Package</h3>
+<h3 id="hahahugoshortcode4s2hbhb-konfound-r-package">
+ <i class="fas fa-code "></i>
+
+ KonFound R Package</h3>
 <p>Rosenberg, J. M., Xu, R., Lin, Q., Maroulis, S., &amp; Frank, K. A. (2023). <em>konfound: Quantify the robustness of causal inferences</em> (v. 0.4.0). <a href="https://CRAN.R-project.org/package=konfound">https://CRAN.R-project.org/package=konfound</a></p>
-<h3 id="konfound-stata-package">KonFound Stata Package</h3>
+<table>
+<thead>
+<tr>
+<th style="text-align:left">Monthly Downloads</th>
+<th style="text-align:left">Total Downloads</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left"><img src="https://cranlogs.r-pkg.org/badges/konfound?color=9bbb59" alt=""></td>
+<td style="text-align:left"><img src="https://cranlogs.r-pkg.org/badges/grand-total/konfound?color=9bbb59" alt=""></td>
+</tr>
+</tbody>
+</table>
+<h3 id="hahahugoshortcode4s3hbhb-konfound-stata-package">
+ <i class="fas fa-code "></i>
+
+ KonFound Stata Package</h3>
 <p>Xu, R., Frank, K. A., Maroulis, S. J., &amp; Rosenberg, J. M. (2019). konfound: Command to quantify robustness of causal inferences. <em>The Stata Journal, 19</em>(3), 523-550. <a href="https://doi.org/10.1177/1536867X19874223">https://doi.org/10.1177/1536867X19874223</a></p>
+<h3 id="benchmarks-what-works-clearinghouse">Benchmarks: What Works Clearinghouse</h3>
+<p>COMING SOON</p>
 <hr>
 <h2 id="internal-resources">Internal Resources</h2>
 <p>Be sure to look through the variety of supports for KonFound:</p>
 <ul>
 <li><a href="/page/faq">FAQ</a></li>
 <li><a href="/page/resources">Resource Overview</a></li>
 <li><a href="/page/publications">Publications</a></li>
+<li><a href="/page/talks">Talks</a></li>
 <li><a href="/page/workshop">Workshops</a></li>
 <li><a href="">User Guide</a> - COMING SOON</li>
 <li><a href="">Blog</a> - COMING SOON</li>
-<li><a href="">Forum</a> - COMING SOON</li>
+<li><a href="https://groups.google.com/g/konfound-it">Forum (Google Group)</a></li>
 </ul>
 <hr>
 <h2 id="external-resources">External Resources</h2>
@@ -353,6 +386,10 @@ <h2 id="external-resources">External Resources</h2>
 <hr>
 <h2 id="connect">Connect</h2>
 <ul>
+<li><a href="https://groups.google.com/forum/#!forum/konfound-it">
+ <i class="fas fa-paper-plane "></i>
+
+ Join our Google Group mailing list</a> for updates on KonFound workshops, publications, progress in related research!</li>
 <li><a href="https://msu.edu/~kenfrank/research.htm#causal">
  <i class="fas fa-globe "></i>
 
@@ -361,10 +398,6 @@ <h2 id="connect">Connect</h2>
  <i class="fas fa-envelope "></i>
 
  Email Dr. Ken Frank</a> with any questions or suggestions.</li>
-<li><a href="https://groups.google.com/forum/#!forum/konfound-it">
- <i class="fas fa-paper-plane "></i>
-
- Join our mailing list</a> for updates on KonFound workshops, publications, progress in related research!</li>
 <li><a href="https://github.com/konfound-project/konfound/issues">
  <i class="fab fa-github "></i>
 

diff --git a/docs/index.html b/docs/index.html
@@ -13,6 +13,7 @@
  <title>KonFound-It!</title>
 
  <meta name="description" content="Sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.
+Questions? Issues? Suggestions? Reach out through the KounFound-It! Google Group.
 Start KonFounding Try out KonFound-It! through an interactive web app.
 Benchmarks Sensitivity analyses calculated for the What Works Clearinghouse.
 Learn More Browse through a variety of resources and guides."><script type="application/ld+json">
@@ -38,6 +39,7 @@
 
 <meta property="og:title" content="KonFound-It!" />
 <meta property="og:description" content="Sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.
+Questions? Issues? Suggestions? Reach out through the KounFound-It! Google Group.
 Start KonFounding Try out KonFound-It! through an interactive web app.
 Benchmarks Sensitivity analyses calculated for the What Works Clearinghouse.
 Learn More Browse through a variety of resources and guides.">
@@ -48,7 +50,7 @@
 
  <meta name="twitter:title" content="KonFound-It!" />
  <meta name="twitter:description" content="Sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.
-Start KonFounding Try out KonFound-It! through an interactive web app. …">
+Questions? Issues? Suggestions? Reach out through the KounFound-It! …">
  <meta name="twitter:image" content="https://konfound-project.github.io/img/konfound-logo.png" />
  <meta name="twitter:card" content="summary_large_image" />
  <link href='https://konfound-project.github.io/img/konfound-logo.png' rel='icon' type='image/x-icon'/>
@@ -251,6 +253,10 @@ <h1>KonFound-It!</h1>
 
  <div class="well">
  <p><strong>Sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias.</strong></p>
+<p>
+ <i class="fas fa-paper-plane "></i>
+
+ Questions? Issues? Suggestions? Reach out through the <a href="https://groups.google.com/g/konfound-it">KounFound-It! Google Group</a>.</p>
 <hr>
 <div class="splitbox"><div class="left">
 

diff --git a/docs/index.xml b/docs/index.xml
@@ -13,7 +13,7 @@
  <link>https://konfound-project.github.io/post/2023-08-13-faq/</link>
  <pubDate>Sun, 13 Aug 2023 00:00:00 +0000</pubDate>
  <guid>https://konfound-project.github.io/post/2023-08-13-faq/</guid>
- <description>Introduction This document addresses some frequently asked questions regarding KonFound. See the appendices for background readings and software. For quick reference, see:&#xA;Overview of all KonFound commands Main introductory slides for combined frameworks Background There are two basic frameworks for sensitivity analysis employed within KonFound. First is the Impact Threshold for a Confounding Variable (ITCV)—Frank (2000). This generates statements such as &amp;ldquo;To invalidate an inference an omitted variable would have to be correlated at __ with the predictor of interest and with the outcome.</description>
+ <description>Introduction This document addresses some frequently asked questions regarding KonFound.&#xA;Questions? Issues? Suggestions? Reach out through the KounFound-It! Google Group.&#xA;See the appendices for background readings and software. For quick reference, visit:&#xA;[Development version of the FAQ](https://www.dropbox.com/s/9eymdekym5g50o7/frequently asked questions for application of konfound-it.docx) Overview of all KonFound commands Main introductory slides for combined frameworks Background There are two basic frameworks for sensitivity analysis employed within KonFound. First is the Impact Threshold for a Confounding Variable (ITCV)—Frank (2000).</description>
  </item>
  <item>
  <title>Welcome!</title>
@@ -41,7 +41,7 @@
  <link>https://konfound-project.github.io/about/</link>
  <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
  <guid>https://konfound-project.github.io/about/</guid>
- <description>Welcome to our space about sensitivity analysis. All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That&amp;rsquo;s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:&#xA;XX% of the estimated effect would have to be due to bias to change your inference about the effect.</description>
+ <description>Welcome to our space about sensitivity analysis.&#xA;Questions? Issues? Suggestions? Reach out through the KounFound-It! Google Group.&#xA;Purpose All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That&amp;rsquo;s where sensitivity analysis comes in—so we can understand how robust our inferences are to challenges to our assumptions. One example is a statement such as:</description>
  </item>
  <item>
  <title>FAQs</title>