set up publication workflow for Jacques and Murphy

Author: jchiquet

.gitignore

@@ -1,18 +1,29 @@
+.Rproj.user
 .Rhistory
-.Rprofile
-.Rapp.history
 .RData
 .Ruserdata
+.Rprofile
+/renv/
+.Rapp.history
 /*.Rcheck/
 .Rproj.user/
-*_cache/
-/cache/
-*_files/
-template-computo-R.html
-logo_text_white.pdf
-template-computo-R.pdf
+
+DS_Store
+
+_extensions
 /.quarto/
-_extensions/
+/.luarc.json
+/*_files
+/*_cache
+/cache/
+/_freeze
+published-202507-jacques-count-data.html
+published-202507-jacques-count-data.pdf
+
 renv/
-*.pdf
-*.html
+_environment
+/.micromamba/
+
+_site/
+logo_notext_white.png
+logo_text_white.pdf

README.md

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+
+# Model-Based Clustering and Variable Selection for Multivariate Count Data
+
+[![build and publish](https://github.com/computorg/published-202507-jacques-count-data/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202507-jacques-count-data/actions/workflows/build.yml)
+[![DOI:10.57750/6v7b-8483](https://img.shields.io/badge/DOI-10.57750/6v7b--8483-034E79.svg)](https://doi.org/10.57750/6v7b-8483)
+[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202507-jacques-count-data/issues?q=is%3Aopen+is%3Aissue+label%3Areview)
+[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202507-jacques-count-data/)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202507-jacques-count-data)
+[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)
+
+Authors: 
+
+- [Julien Jacques](https://eric.univ-lyon2.fr/jjacques/)
+- [Thomas Brendan Murphy](https://mststats.github.io/author/thomas-brendan-murphy/)
+
+Model-based clustering provides a principled way of developing clustering methods. We develop a new model-based clustering methods for count data. The method combines clustering and variable selection for improved clustering. The method is based on conditionally independent Poisson mixture models and Poisson generalized linear models. The method is demonstrated on simulated data and data from an ultra running race, where the method yields excellent clustering and variable selection performance.

_quarto.yml

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 project:
   render:
-    - Jacques_Murphy.qmd
+    - publication-202507-jacques-count-data.qmd
 
 title: "Model-Based Clustering and Variable Selection for Multivariate Count Data"
 author:
@@ -14,22 +14,21 @@ author:
   affiliations: 
   - name: School of Mathematics \& Statistics, University College Dublin
   - name: Institut d'Études Avancées, Université de Lyon
-date: last-modified
+date: 07-01-2025
 date-modified: last-modified
 abstract: >+
   Model-based clustering provides a principled way of developing clustering methods. We develop a new model-based clustering methods for count data. The method combines clustering and variable selection for improved clustering. The method is based on conditionally independent Poisson mixture models and Poisson generalized linear models. The method is demonstrated on simulated data and data from an ultra running race, where the method yields excellent clustering and variable selection performance.
 keywords: [Count data, Model-based clustering, Variable selection]
 citation:
   type: article-journal
   container-title: "Computo"
-  doi: "xxxx"
-  url: https://computo.sfds.asso.fr/template-computo-quarto
-  publisher: "Société Française de Statistique"
+  doi: 10.57750/6v7b-8483
+  publisher: "French Statistical Society"
   issn: "2824-7795"
 bibliography: references.bib
 draft: true
-published: false
+published: true
 format:
   computo-html: default
   computo-pdf: default
-fontfamily: merriweather
+# fontfamily: merriweather