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Merge pull request #112 from KrystynaGrzesiak/master
Webpage update
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.github/workflows/hugo.yml

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build:
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runs-on: ubuntu-latest
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env:
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HUGO_VERSION: 0.128.0
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HUGO_VERSION: 0.145.0
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steps:
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- name: Install Hugo CLI
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run: |

README.md

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3. and install hugo using blogdown
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```r
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install_hugo()
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install_hugo(version = "v0.124.0")
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options(blogdown.hugo.version = "0.124.0")
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```
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4. To use the website, open the R project that you have just cloned from github and start

config.toml

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# Hugo variables
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#
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baseurl = "https://rmisstastic.netlify.app/"
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baseurl = "https://rmisstastic-dev.netlify.app/"
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googleAnalytics = "UA-146921644-1"
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tags = ["missing values", "missing data", "imputation", "R", "incomplete data", "rmisstastic", "rmiss"]
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ignoreFiles = ["\\.Rmd$", "\\.Rmarkdown$", "\\.knit\\.md$", "\\.utf8\\.md$", "_cache$"]
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# number of posts to show on home/category/tag pages (Default: 10)
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paginate = 2
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[pagination]
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pagerSize = 2 # Change 10 to whatever your original paginate value was
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# optionally override the site's footer with custom copyright text
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# copyright = "Except where otherwise noted, content on this site is licensed under a [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by-sa/4.0/)."
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#[languages.bg]
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# vim: ts=2 sw=2 et
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[markup]
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[markup.goldmark]
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[markup.goldmark.renderer]
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unsafe = true

content/packages/Hmisc.md

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---
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author: Harrell Miscellaneous, Charles Dupont
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slug: Hmisc
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categories: []
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tags: []
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description: ''
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---
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##### Package:
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Hmisc
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##### Category:
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Single and Multiple Imputation
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##### Use-Cases:
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Data summarization and exploration, handling missing data and imputation, regression modeling and splines, correlation analysis and statistical tests, data visualization, variable labeling and metadata management, data manipulation and transformation.
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##### Description:
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Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.
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##### Algorithms:
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- impute(): Allows simple imputation (mean, median, or mode) of missing values.
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- transcan(): Performs multiple imputation and transformation of missing data.
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- aregImpute(): Performs multiple imputation using additive regression.
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- fit.mult.impute(): Fits regression models to multiply imputed datasets.
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##### Datasets:
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none
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##### Further Information:
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- Package repository: https://github.com/harrelfe/Hmisc/
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- Documentation: https://www.rdocumentation.org/packages/Hmisc/versions/5.2-3/
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- Web-page: https://hbiostat.org/r/hmisc/
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##### Input:
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data.frame, tibble
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##### Example:
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You can find a nice vignette with example usage [here](https://hbiostat.org/r/hmisc/examples)

content/posts/events.Rmd

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<b>Upcoming events:</b>
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<ul class="list-group" id="up-events-list">
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<li class="list-group-item"> <a href="http://www.iscb2021.info/en" target="_blank">43nd Conference of the International Society for Clinical Biostatistics</a> (August 21st - August 25th, 2022, Newcastle upon Tyne, UK)</li>
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<li class="list-group-item"> <a href="http://www.compstat2022.org/" target="_blank">International Conference on Computational Statistics (COMPSTAT)</a> (August 23rd - August 26th, 2022, Bologna, Italy)</li>
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<li class="list-group-item"> <a href="https://sites.google.com/view/icsds2022" target="_blank">2022 IMS International Conference on Statistics and Data Science (ICSDS)</a> (December 13th - December 16th, 2022, Florence, Italy)</li>
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</ul>
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</ul>
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<li class="list-group-item"> <a href="https://statml.io/index.php/statml-bocconi-spring-school-2025-uk-edition/" target="_blank">EPSRC StatML and Bocconi Spring School on Computational Optimal Transport and Statistical Learning with Missing Values</a> (7th April - 11th April, 2025, Windsor, UK)</li>
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</br>
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<b>Past events:</b>
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<ul class="list-group" id="past-events-list">
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<li class="list-group-item"> <a href="http://www.compstat2022.org/" target="_blank">International Conference on Computational Statistics (COMPSTAT)</a> (August 23rd - August 26th, 2022, Bologna, Italy)</li>
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<li class="list-group-item"> <a href="http://www.iscb2021.info/en" target="_blank">43nd Conference of the International Society for Clinical Biostatistics</a> (August 21st - August 25th, 2022, Newcastle upon Tyne, UK)</li>
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<li class="list-group-item"> <a href="https://sites.google.com/view/icsds2022" target="_blank">2022 IMS International Conference on Statistics and Data Science (ICSDS)</a> (December 13th - December 16th, 2022, Florence, Italy)</li>
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<li class="list-group-item"> <a href="https://ww2.amstat.org/meetings/jsm/2022/" target="_blank">Joint Statistical Meetings 2022</a> (August 6th - August 11th, 2022, Washington, DC, USA)</li>
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<li class="list-group-item"> <a href="https://www.ibc2022.org/home" target="_blank">31st International Biometric Conference</a> (July 10th - July 15th, 2022, Riga, Latvia)</li>
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<li class="list-group-item"> <a href="https://www.imsannualmeeting-london2022.com/" target="_blank">2022 IMS Annual Meeting</a> (June 27th - June 30th, 2022, London, UK) with an invited session on modern approaches to missing data</li>

content/posts/events.html

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<p><br></p>
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<p><b>Upcoming events:</b></p>
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<ul class="list-group" id="up-events-list">
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</ul>
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<li class="list-group-item">
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<a href="http://www.iscb2021.info/en" target="_blank">43nd Conference of the International Society for Clinical Biostatistics</a> (August 21st - August 25th, 2022, Newcastle upon Tyne, UK)
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<a href="https://statml.io/index.php/statml-bocconi-spring-school-2025-uk-edition/" target="_blank">EPSRC StatML and Bocconi Spring School on Computational Optimal Transport and Statistical Learning with Missing Values</a> (7th April - 11th April, 2025, Windsor, UK)
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</li>
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<p></br></p>
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<p><b>Past events:</b></p>
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<ul class="list-group" id="past-events-list">
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<li class="list-group-item">
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<a href="http://www.compstat2022.org/" target="_blank">International Conference on Computational Statistics (COMPSTAT)</a> (August 23rd - August 26th, 2022, Bologna, Italy)
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</li>
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<li class="list-group-item">
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<a href="http://www.iscb2021.info/en" target="_blank">43nd Conference of the International Society for Clinical Biostatistics</a> (August 21st - August 25th, 2022, Newcastle upon Tyne, UK)
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</li>
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<li class="list-group-item">
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<a href="https://sites.google.com/view/icsds2022" target="_blank">2022 IMS International Conference on Statistics and Data Science (ICSDS)</a> (December 13th - December 16th, 2022, Florence, Italy)
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</li>
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</ul>
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<p></br></p>
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<p><b>Past events:</b></p>
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<ul class="list-group" id="past-events-list">
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<li class="list-group-item">
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<a href="https://ww2.amstat.org/meetings/jsm/2022/" target="_blank">Joint Statistical Meetings 2022</a> (August 6th - August 11th, 2022, Washington, DC, USA)
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</li>

content/rpkg.Rmd

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```
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```{r PkGList, echo=FALSE, message=FALSE, warning=FALSE}
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pkg_list <- c("imputeTS", "mice", "missForest", "missMDA", "naniar","simputation", "VIM")
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pkg_list <- c("imputeTS", "mice", "missForest", "missMDA", "naniar","simputation", "VIM", "Hmisc")
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pkg_category <- c("Time-Series Imputation, Visualisations for Missing Data",
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"Multiple Imputation",
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"Single Imputation",
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"Single and Multiple Imputation, Multivariate Data Analysis",
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"Visualisations for Missing Data",
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"Single Imputation, Meta-Package",
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"Single Imputation, Visualisations for Missing Data")
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"Single Imputation, Visualisations for Missing Data",
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"Single and Multiple Imputation, Data Processing")
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```
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content/rpkg.html

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interface available in the separate package VIMGUI allows an easy handling of
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the implemented plot methods.</i><br><img src="https://cranlogs.r-pkg.org/badges/VIM" alt="CRAN Downloads" /><img src="https://www.r-pkg.org/badges/last-release/VIM" alt="CRAN Release" /> <br><a href="/packages/VIM"> more.. </a>
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</li>
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</br>
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<li style="margin: 0px;">
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<h3 class="name">
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Hmisc
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</h3>
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<h6 class="Category">
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Category: Single and Multiple Imputation, Data Processing
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</h6>
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<i class="Description">Contains many functions useful for data
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analysis, high-level graphics, utility operations, functions for
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computing sample size and power, simulation, importing and annotating datasets,
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imputing missing values, advanced table making, variable clustering,
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character string manipulation, conversion of R objects to LaTeX and html code,
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recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.</i><br><img src="https://cranlogs.r-pkg.org/badges/Hmisc" alt="CRAN Downloads" /><img src="https://www.r-pkg.org/badges/last-release/Hmisc" alt="CRAN Release" /> <br><a href="/packages/Hmisc"> more.. </a>
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</li>
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<p></br></p>
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<h3 class="name">

netlify.toml

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[build]
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publish = "public"
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command = "hugo --gc --minify"
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[build.environment]
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HUGO_VERSION = "0.145.0"
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HUGO_THEME = "hugo-theme-bootstrap4-blog"
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HUGO_ENABLEGITINFO = "true"

themes/hugo-theme-bootstrap4-blog/.build.yml

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image: archlinux
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packages:
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- nodejs-lts-erbium
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- nodejs-lts-fermium
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- npm
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sources:
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- https://git.sr.ht/~alanorth/hugo-theme-bootstrap4-blog

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