Use paper for citation (#505)

* Use paper for citation

* update textversion

* add joss paper

* wordlist

Author: strengejacke

DESCRIPTION

@@ -42,7 +42,7 @@ Imports:
     bayestestR (>= 0.16.0),
     datawizard (>= 1.0.2),
     insight (>= 1.3.0),
-    parameters (>= 0.25.0),
+    parameters (>= 0.26.0),
     graphics,
     stats,
     tools,
@@ -106,4 +106,3 @@ Roxygen: list(markdown = TRUE)
 Config/Needs/check: stan-dev/cmdstanr
 Config/Needs/website: easystats/easystatstemplate
 LazyData: true
-Remotes: easystats/bayestestR, easystats/parameters

README.Rmd

@@ -30,7 +30,7 @@ options(marginaleffects_safe = FALSE)
 ```
 
 
-[![publication](https://img.shields.io/badge/Cite-Unpublished-yellow)](https://github.com/easystats/modelbased/blob/master/inst/CITATION) [![downloads](https://cranlogs.r-pkg.org/badges/modelbased)](https://cran.r-project.org/package=modelbased)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.07969/status.svg)](https://doi.org/10.21105/joss.07969) [![downloads](https://cranlogs.r-pkg.org/badges/modelbased)](https://cran.r-project.org/package=modelbased)
 [![total](https://cranlogs.r-pkg.org/badges/grand-total/modelbased)](https://cranlogs.r-pkg.org/)
 
 ***Make the most out of your models***
@@ -453,6 +453,11 @@ Note that `{emmeans}` can also perform numeric differentiation or use non-regula
 
 Finally, `{modelbased}` leverages the `get_datagrid()` function from the `{insight}` package [(Lüdecke et al., 2019)](https://doi.org/10.21105/joss.01412) to intuitively generate an appropriate grid of data points for which predictions or effects or slopes will be estimated. Since these packages support a wider range of models - including generalized linear models, mixed models, and Bayesian models - `{modelbased}` also inherits the support for such models.
 
+## Citation
+
+If this package helped you, please consider citing as follows:
+
+Makowski, D., Ben-Shachar, M. S., Wiernik, B. M., Patil, I., Thériault, R., & Lüdecke, D. (2025). modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions. Journal of Open Source Software, 10(109), 7969. doi: 10.21105/joss.07969
 
 ## Code of Conduct
 

README.md

@@ -1,7 +1,7 @@
 
 # modelbased <img src='man/figures/logo.png' align="right" height="139" />
 
-[![publication](https://img.shields.io/badge/Cite-Unpublished-yellow)](https://github.com/easystats/modelbased/blob/master/inst/CITATION)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.07969/status.svg)](https://doi.org/10.21105/joss.07969)
 [![downloads](https://cranlogs.r-pkg.org/badges/modelbased)](https://cran.r-project.org/package=modelbased)
 [![total](https://cranlogs.r-pkg.org/badges/grand-total/modelbased)](https://cranlogs.r-pkg.org/)
 
@@ -657,6 +657,16 @@ slopes will be estimated. Since these packages support a wider range of
 models - including generalized linear models, mixed models, and Bayesian
 models - `{modelbased}` also inherits the support for such models.
 
+## Citation
+
+If this package helped you, please consider citing as follows:
+
+Makowski, D., Ben-Shachar, M. S., Wiernik, B. M., Patil, I., Thériault,
+R., & Lüdecke, D. (2025). modelbased: An R package to make the most out
+of your statistical models through marginal means, marginal effects, and
+model predictions. Journal of Open Source Software, 10(109), 7969. doi:
+10.21105/joss.07969
+
 ## Code of Conduct
 
 Please note that the modelbased project is released with a [Contributor

inst/CITATION

@@ -1,11 +1,14 @@
 bibentry(
-    bibtype="Article",
-	title="Estimation of Model-Based Predictions, Contrasts and Means.",
-	author=c(person("Dominique", "Makowski"), person("Mattan S.", "Ben-Shachar"), person("Indrajeet", "Patil"), person("Daniel", "Lüdecke")),
-    journal="CRAN",
-    doi="",
-	year="2020",
-	url="https://github.com/easystats/modelbased",
-	textVersion = "Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Estimation of Model-Based Predictions, Contrasts and Means. CRAN.",
-	mheader = "To cite 'modelbased' in publications use:"
+  bibtype = "article",
+  key = "modelbasedPackage",
+  title = "{modelbased}: An {R} package to make the most out of your statistical models through marginal means, marginal effects, and model predictions.",
+	volume = "10",
+	doi = "10.21105/joss.07969",
+	number = "109",
+	journal = "Journal of Open Source Software",
+	author = c(person("Dominique", "Makowski"), person("Mattan S.", "Ben-Shachar"), person("Brenton M.", "Wiernik"), person("Indrajeet", "Patil"), person("Rémi", "Thériault"), person("Daniel", "Lüdecke")),
+	year = "2025",
+	pages = "7969",
+  url="https://joss.theoj.org/papers/10.21105/joss.02306",
+  textVersion = "Makowski, D., Ben-Shachar, M. S., Wiernik, B. M., Patil, I., Thériault, R., & Lüdecke, D. (2025). modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions. Journal of Open Source Software, 10(109), 7969. https://doi.org/10.21105/joss.07969"
 )

inst/WORDLIST

@@ -74,6 +74,7 @@ Visualisation
 Visualising
 Wagenmakers
 Wemrell
+Wiernik
 al
 analysing
 bc
@@ -126,6 +127,7 @@ intersectionality
 intra
 io
 jmr
+joss
 labelled
 lifecycle
 lme