diff --git a/README.Rmd b/README.Rmd
index 74380eb34..9531e3a2f 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -128,6 +128,12 @@ ggplot(iris, aes(x = Species, y = Sepal.Width)) +
theme_minimal()
```
+You can also get a "quick" plot using the `plot()` function:
+
+```{r}
+plot(means)
+```
+
### Contrast analysis
diff --git a/paper/.gitignore b/paper/.gitignore
new file mode 100644
index 000000000..075b2542a
--- /dev/null
+++ b/paper/.gitignore
@@ -0,0 +1 @@
+/.quarto/
diff --git a/paper/apa.csl b/paper/apa.csl
new file mode 100644
index 000000000..946c7fcd2
--- /dev/null
+++ b/paper/apa.csl
@@ -0,0 +1,1917 @@
+
+
\ No newline at end of file
diff --git a/paper/example.R b/paper/example.R
new file mode 100644
index 000000000..c777b9f76
--- /dev/null
+++ b/paper/example.R
@@ -0,0 +1,20 @@
+library(easystats)
+
+model <- lm(Petal.Width ~ Petal.Length * Species, data = iris)
+
+parameters::parameters(model)
+
+
+estimate_relation(model, by=c("Petal.Length", "Species"), length=100) |>
+ plot()
+
+
+estimate_means(model, by="Species")
+
+estimate_contrasts(model, contrast="Species")
+
+estimate_slopes(model, trend = "Petal.Length", by="Species")
+
+estimate_contrasts(model, contrast="Petal.Length", by="Species", backend="marginaleffectss")
+# estimate_contrasts(model, contrast="Petal.Length", by="Species")
+
diff --git a/paper/paper.Rmd b/paper/paper.Rmd
new file mode 100644
index 000000000..ff6800ca9
--- /dev/null
+++ b/paper/paper.Rmd
@@ -0,0 +1,302 @@
+---
+title: "modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions"
+tags:
+ - R
+ - easystats
+ - marginal effects
+ - marginal means
+ - model predictions
+authors:
+- name: Dominique Makowski
+ orcid: 0000-0001-5375-9967
+ affiliation: "1, 2"
+ corresponding: TRUE
+
+- name: Mattan S. Ben-Shachar
+ orcid: 0000-0002-4287-4801
+ affiliation: 3
+
+- name: Brenton M. Wiernik
+ orcid: 0000-0001-9560-6336
+ affiliation: 4
+
+- name: Indrajeet Patil
+ orcid: 0000-0003-1995-6531
+ affiliation: 5
+
+- name: Rémi Thériault
+ orcid: 0000-0003-4315-6788
+ affiliation: 6
+
+- name: Daniel Lüdecke
+ orcid: 0000-0002-8895-3206
+ affiliation: 7
+
+affiliations:
+- name: School of Psychology, University of Sussex, Brighton, UK
+ index: 1
+
+- name: Sussex Centre for Consciousness Science, University of Sussex, Brighton, UK
+ index: 2
+
+- name: Independent Researcher, Ramat Gan, Israel
+ index: 3
+
+- name: Independent Researcher, Tampa, FL, USA
+ index: 4
+
+- name: Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
+ index: 5
+
+- name: Department of Psychology, New York University, New York, NY, USA
+ index: 6
+
+- name: Institute of Medical Sociology, University Medical Center Hamburg-Eppendorf, Germany
+ index: 7
+correspondence: D.Makowski@sussex.ac.uk.
+type: article
+date: "`r Sys.Date()`"
+bibliography: paper.bib
+# abstract: |
+# The `modelbased` package provides a straightforward approach to computing and visualizing model-based estimates in R, including marginal means, contrasts, slopes, and predictions. Designed as part of the easystats ecosystem, it streamlines post-hoc analysis and model interpretation across a wide range of statistical models. While tools like `emmeans` and `marginaleffects` offer similar functionality, `modelbased` prioritizes ease of use, making it more accessible to researchers without extensive statistical programming expertise. By integrating modern workflows for effect estimation and visualization, modelbased enhances the interpretability and reproducibility of statistical analyses in psychological and social sciences.
+keywords: |
+ marginal effects; marginal means; model predictions; R; easystats
+acknowledgement: |
+ *{performance}* is part of the collaborative
+ [*easystats*](https://github.com/easystats/easystats) ecosystem
+ [@easystatspackage]. Thus, we thank all
+ [members of easystats](https://github.com/orgs/easystats/people),
+ contributors, and users alike.
+authorcontributions: |
+ D.M. drafted the paper; all authors contributed to both the writing of the
+ paper and the conception of the software.
+funding: |
+ This research received no external funding.
+conflictsofinterest: |
+ The authors declare no conflict of interest.
+output:
+ rticles::joss_article
+journal:
+ pissn: 2475-9066
+csl: apa.csl
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(
+ echo = TRUE,
+ comment = "#>",
+ out.width = "100%",
+ dpi = 300,
+ warning = FALSE
+)
+
+library(easystats)
+```
+
+# Statement of need
+
+Applied statistics have historically focused on statistical *tests* (e.g., *t* tests, correlation tests, and analyses of variances, ANOVAs), seen as most apt to provide researchers with interpretable answers to the questions they seek. However, these tests typically rely on statistical *models*—the true underlying cornerstone of modern data science. The replication crisis [@OSC2015estimating; @camerer2018evaluating] and methodological revolutions [@makowski2023we] have underlined some of the issues with the traditional focus on statistical tests (e.g., the effacement of model assumptions, a focus on null-hypothesis testing, non-compatibility with more complex variance structures) and called for shifting the focus to the models themselves.
+
+In line with these efforts, new tools have been created to facilitate the direct usage and reporting of statistical models. For instance, the `easystats` collection of R packages [@easystatspackage] has been developed to help researchers "tame, discipline, and harness" the power of statistical models. Existing packages are dedicated to model parameters [the `parameters` package, @ludecke2020extracting], predictive performance [the `performance` package, @ludecke2021performance] or effect importance [the `effectsize` package, @ben2020effectsize].
+
+**But the models themselves hide even more critical information!**
+
+The fundamental nature of these models—a statistical link between an outcome $y$ and predictor variables $X$—enables the generation of predictions for any combination of predictors, observed or unobserved alike. These predictions refer to expected values of the outcome for given levels of predictors of interest, as well as contrasting them, making it possible to visualize the model's behaviour in a more meaningful and comprehensive way, and answering a broad range of research questions.
+
+The two most popular R packages for extracting these quantities of interest from statistical models are `emmeans` [@russell2024emmeans] and `marginaleffects` [@arel2024interpret]. These packages pack an enormously rich set of features and cover (almost) all imaginable needs for post-hoc analysis of statistical models. However, their power and flexibility come at a cost: ease of use—especially for users not familiar with the underlying statistical concepts. The `modelbased` package, built on top of these two packages, aims to unleash this vast, untapped potential by providing a unified interface to extract marginal means, marginal effects, contrasts, comparisons, and model predictions from a wide range of statistical models. In line with the `easystats`' *raison d'être*, the `modelbased` package focuses on simplicity, flexibility, and user-friendliness to help researchers harness the full power of their models.
+
+
+# Key concepts
+
+Answering research questions based on statistical models means describing the relationship between predictors (also called *focal* predictors) of interest and the outcome, as well as differences between observed groups in the sample. There are four key concepts in `modelbased` to achieve this:
+
+- **Predictions:** Estimate the outcome for a specific combination of predictor values (which may or may not exist in the data).
+
+- **Marginal Means:** Estimate the average outcome across the observed distribution of other predictors, representing a "typical" observation.
+
+- **Marginal Effects:** Quantify the strength of a predictor's effect on the outcome (the average slope), which is especially useful when slopes vary (e.g., with interactions or in logistic regression).
+
+- **Contrasts:** Compare predicted outcomes between groups or levels of a predictor.
+
+## Predictions
+
+At a fundamental level, `modelbased` and similar packages leverage model *predictions*. These predictions can be of different types, depending on the model and the question at hand. For instance, predictions can be associated with **confidence intervals** (`predict = "expectation"`) or **prediction intervals** (`predict = "prediction"`). The former corresponds to the uncertainty around the "relationship" (i.e., the conditional estimate, typically of the expectation ($E[X]$) according to a model's parameters), while the latter is typically larger and provides information about the range which individual observations might take. Moreover, for generalized linear models (GLMs), predictions can be made on the **response scale** (`predict = "response"`) or the **link scale** (`predict = "link"`). This corresponds for instance to predictions in terms of probability (response scale) or log odds (link scale) for logistic regression models.
+
+These different types of estimates can be obtained for observations in the original dataset—which is useful to assess the model's goodness-of-fit—or for new data (typically a "data grid")—which is useful for visualization.
+
+For convenience, the `modelbased` package includes four related functions, which mostly differ in their default arguments for `data` and `predict`[^1]:
+
+- `estimate_prediction()`: original data, prediction intervals.
+- `estimate_expectation()`: original data, confidence intervals.
+- `estimate_relation()`: data grid, predictions on the response scale.
+- `estimate_link()`: data grid, predictions on the link scale.
+
+[^1]: These functions can become redundant if the defaults are changed. For instance, `estimate_relation(..., predict = "link")` matches `estimate_link(...)`.
+
+## Marginal means
+
+The concept of "marginal" in this context refers to how non-focal variables are treated. While predictions, as described above, fix non-focal variables at their reference level, marginal means compute the empirical or theoretical averages of them. These kind of predictions are a good representation of the sample, because they are not based on very specific characteristics. E.g., predictions are made for _female_ persons with _high_ income, while marginal means calculate the expected outcome for an _average_ observation.
+
+The `modelbased` package provides a simple and clear interface to extract marginal means via the `estimate_means()` function, which can be considered as "marginal" pendant to `estimate_relation()`. `estimate_means()` typically returns results in line with the _emmeans_ package. Focal predictors are specified in the `by` argument.
+
+## Marginal effects
+
+Predictions and marginal means can be used to better understand the _relationship_ of predictors with the outcome. They tell you that "the average health score for a person at the age of sixty is 80 points". Marginal _effects_, in turn, evaluate the (average) _strength_ of an effect (also called "slope"), telling you that "the average effect of age on the health score is an decrease 5 points".
+
+For the simple case of linear regression without interaction terms, the regression coefficient (slope) equals the marginal effect. However, in even slightly more complex situations (e.g., when interaction terms are involved, or logistic regression models), the slope is not constant across the predictor's values. Here we can estimate an _average_ slope, or marginal effect.
+
+Again, the `modelbased` package has a simple function to do so, `estimate_slopes()`. This function calculates the _trend_ or average effect, usually for numeric predictors. The `trend` argument specifies the focal predictors, while `by` allows for further grouping.
+
+## Contrasts
+
+Contrast analysis, or pairwise comparison, of estimated marginal means allows for direct statistical comparison of the predicted outcomes across different groups or levels of a focal predictor. Rather than simply observing differences in marginal means, contrast analysis quantifies these differences and assesses their statistical significance, which also involves determining the associated uncertainty (e.g., confidence intervals).
+
+In `modelbased`, this is achieved using the `estimate_contrasts()` function. Focal predictors are specified in the `contrast` argument. For further stratification or grouping, use the `by` argument.
+
+## Interpretation
+
+The interpretation of predicted outcome values requires careful consideration of whether they are derived from *predictions* or *marginal means*, despite both originating from the same underlying model and data. The `modelbased` package facilitates the estimation of several distinct prediction quantities, broadly categorized as *data grid-based* and *empirical*.
+
+### Data grid-based predictions
+
+The `estimate_relation()` and `estimate_means()` functions utilize a balanced data grid, representing all combinations of focal predictor levels. These functions emphasize the *predictors themselves*, enabling the analysis of associations and comparisons of specific characteristics. `estimate_relation()` provides predictions for a *specific* observation, while `estimate_means()` represents a *typical* observation from the sample.
+
+### Empirical predictions
+
+When the focus is on the *predicted outcome*, it is more appropriate to consider the empirical distribution of focal predictor characteristics rather than a balanced data grid. This can be achieved by changing the `estimate` argument within `estimate_means()`. Setting `estimate = "average"` calculates the average expected outcome from those observations _from the sample_ defined by the focal predictors.
+
+For analyses emphasizing outcome differences between groups (e.g., when computing contrasts) and particularly when causal effects are being considered, it may be beneficial to model a hypothetical population not directly represented in the sample. This approach, known as *G-computation* [@chatton_rohrer_2024], is implemented by setting `estimate = "population"` within `estimate_means()`. This `estimate` argument is also available for the `estimate_contrasts()` function.
+
+## Technical details
+
+The algorithmic heavy lifting is done by `modelbased`'s two back-end packages, `emmeans` and `marginaleffects` (the default), which can be set as a global option (e.g., with `options(modelbased_backend = "emmeans")`).
+
+Of the two, `emmeans` [@russell2024emmeans] is the more senior package and was originally known as `lsmeans` (for "Least-Squares Means"). This term has been historically used to describe what are now more commonly referred to as "Estimated Marginal Means" or EMMs: predictions made over a regular grid—a grid typically constructed from all possible combinations of the categorical predictors in the model and the mean of numerical predictors. The package was renamed in 2016 to `emmeans` to clarify its extension beyond least-squares estimation and its support of a wider range of models (e.g., Bayesian models).
+
+Within `emmeans`, estimates are generated as a linear function of the model's coefficients, with standard errors (SEs) produced in a similar manner by taking a linear combination of the coefficients' variance-covariance matrix.
+For example if $b$ is a vector of 4 coefficients, and $V$ is a 4-by-4 matrix of the coefficients' variance-covariance, we can get an estimate and SE for a linear combination (or set of linear combinations) $L$ like so:
+
+$$
+\hat{b} = L \cdot b
+$$
+
+$$
+SE_{\hat{b}} = \sqrt{\text{diag}(L \cdot V \cdot L^T)}
+$$
+
+```{r, echo=FALSE, eval=FALSE}
+# original formula, please check OK:
+L %*% b
+sqrt(diag(L %*% V %*% t(L)))
+```
+
+These grid predictions are sometimes averaged over (averaging being a linear operation itself) to produce "marginal" predictions (in the sense of marginalized-over): means. These predictions can then be contrasted using various built-in or custom contrasts weights to obtain meaningful estimates of various effects. Using linear combinations with regular grids often means that results from `emmeans` directly correspond to a models coefficients (which is a benefit for those who are used to understanding models by examining coefficient tables).
+
+`marginaleffects` [@arel2024interpret] was more recently introduced, and uses a different approach: various effects are estimated by generating two counter-factual predictions of unit-level observations, and then taking the difference between them [with SEs computed using the delta method, @arel2024interpret].
+By default, such effects are estimated for every observation in the original model frame. These unit-level effects are typically averaged to obtain average effects (e.g., an Average Treatment Effect, ATE).
+
+Using the delta method affords more flexibility in the specification of the marginal effects to be estimated. For example, while `emmeans` by default compares predictions from GLMs on the link scale and then transforms the comparison back to something closer to the response scales (e.g., the difference between two log-odds is taken, and then exponentiation to produce odds ratios), `marginaleffects` by default compares predictions on the response scale directly (e.g, taking the difference between two probabilities). The delta method implemented in `marginaleffects` uses iterative estimation, making it more computationally costly relative to the simple matrix multiplication used for estimating linear combinations (though `marginaleffects` is very efficient).
+
+This means that while `emmeans` typically produces _effects at the mean_, `marginaleffects` typically produces _mean effects_. Depending on the quantity of interest, model, use of a link function, design balance and weights, these can be nearly identical, or very different.
+
+Note that `emmeans` can also use the delta method and can use non-regular grids, and `marginaleffects` can also generate linear predictions at the mean. However, obtaining these requires some deeper knowledge of the relevant packages. This is easier to achieve and more accessible in `modelbased`, by simply modulating the `estimate` argument (see section _Interpretation_).
+
+Finally, `modelbased` leverages the `get_datagrid()` function from the `insight` package [@ludecke2019insight] 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.
+
+# Examples
+
+The `iris` dataset contains measures in centimeters of three different species of iris flowers [setosa, versicolor, and virginica, @becker1988new]. Imagine the following linear model in which we predict those flowers' petal width (`Petal.Width`) from the interaction between their petal length (`Petal.Length`) and their `Species`.
+
+```{r}
+library(easystats)
+
+model <- lm(Petal.Width ~ Petal.Length * Species, data = iris)
+
+parameters::parameters(model) |>
+ print(select = "minimal")
+```
+
+The model's **parameters** can be challenging to interpret and do not offer us all the insights that this model actually contains.
+
+## Visualize relationship
+
+We can start by easily visualizing the relationship between our response variable and our predictors (Figure 1).
+
+```{r fig1, fig.cap = "Scatter plot of petal length by pelal width, grouped by species"}
+estimate_relation(model, by = c("Petal.Length", "Species"), length = 100) |>
+ plot()
+```
+
+But what is the **average value** of `Petal.Width` for each species?
+
+## Marginal Means
+
+The marginal means can be computed, which are the mean predictions for each level of a categorical predictor, *averaged across* all levels of other predictors (`Petal.Length` in this case).
+
+```{r}
+estimate_means(model, by = "Species")
+```
+
+However, are these different species **significantly different** from each other?
+
+## Marginal Contrasts
+
+We can estimate all the pairwise contrasts between the levels of the `Species` factor.
+
+```{r}
+estimate_contrasts(model, contrast = "Species")
+```
+
+As we can see, the average difference between *versicolor* and *setosa* is not significant.
+
+## Marginal Slopes
+
+Similarly, we can compute the marginal effect of `Petal.Length` (i.e., the "slope") for each species.
+
+```{r}
+estimate_slopes(model, trend = "Petal.Length", by = "Species")
+```
+
+This shows that there is a significant positive relationship between `Petal.Length` and `Petal.Width` for all species but *setosa*.
+
+## Marginal Contrasts of Slopes
+
+Finally, we can even compute the contrasts between the slopes of `Petal.Length` for each species.
+
+```{r}
+estimate_contrasts(model, contrast = "Petal.Length", by = "Species")
+```
+
+The effect of `Petal.Length` on `Petal.Width` is significantly stronger in *virginica* compared to *versicolor*.
+
+# Conclusion
+
+The `modelbased` package provides a simple and intuitive interface to extract and visualize important information contained within statistical models.
+
+# Declarations
+
+## Funding information
+
+This research received no external funding.
+
+## Competing Interests
+
+The authors declare no conflict of interest
+
+## Availability of data and materials (data transparency)
+
+All data used in this paper uses data included with base R.
+
+## Code availability
+
+The `modelbased` package is available at the official website (https://easystats.github.io/modelbased), on CRAN (https://cran.r-project.org/package=modelbased), and on the R-Universe (https://easystats.r-universe.dev/modelbased). The source code is available on GitHub (https://github.com/easystats/modelbased), and the package can be installed from CRAN with `install.packages("modelbased")` or from R-Universe with `install.packages("modelbased", repos = "https://easystats.r-universe.dev")`.
+
+## Contributions
+
+DM: Writing- Original draft preparation, Writing- Reviewing and Editing, Software. MSB-S, BMW, IP, RT, and DL: Writing- Reviewing and Editing, Software.
+
+## Acknowledgements
+
+*{modelbased}* is part of the collaborative [*easystats*](https://github.com/easystats/easystats) ecosystem [@easystatspackage]. Thus, we thank all [members of easystats](https://github.com/orgs/easystats/people), contributors, and users alike.
+
+# References
diff --git a/paper/paper.bib b/paper/paper.bib
new file mode 100644
index 000000000..f564af41e
--- /dev/null
+++ b/paper/paper.bib
@@ -0,0 +1,132 @@
+@article{OSC2015estimating,
+ title ={Estimating the reproducibility of psychological science},
+ author = {{Open Science Collaboration}},
+ journal = {Science},
+ volume = {349},
+ issue = {6251},
+ pages = {aac4716},
+ year = {2015},
+ doi = {10.1126/science.aac4716},
+ URL = {https://doi.org/10.1126/science.aac4716}
+}
+
+@article{camerer2018evaluating,
+ title ={Evaluating the replicability of social science experiments in {Nature} and {Science} between 2010 and 2015},
+ author = {Camerer, Colin F. and Dreber, Anna and Holzmeister, Felix and Ho, Teck-Hua and Huber, Jürgen and Johannesson, Magnus and Kirchler, Michael and Nave, Gideon and Nosek, Brian A. and Pfeiffer, Thomas and Altmejd, Adam and Buttrick, Nick and Chan, Taizan and Chen, Yiling and Forsell, Eskil and Gampa, Anup and Heikensten, Emma and Hummer, Lily and Imai, Taisuke and Isaksson, Siri and Manfredi, Dylan and Rose, Julia and Wagenmakers, Eric-Jan and Wu, Hang},
+ journal = {Nature Human Behaviour},
+ volume = {2},
+ issue = {9},
+ pages = {637-644},
+ year = {2018},
+ doi = {10.1038/s41562-018-0399-z},
+ URL = {https://doi.org/10.1038/s41562-018-0399-z}
+}
+
+@misc{makowski2023we,
+ title={Where Are We Going with Statistical Computing? From Mathematical Statistics to Collaborative Data Science},
+ author={Makowski, Dominique and Waggoner, Philip D},
+ journal={Mathematics},
+ volume={11},
+ number={8},
+ pages={1821},
+ year={2023},
+ publisher={MDPI},
+ doi = {10.3390/math11081821},
+ url = {https://doi.org/10.3390/math11081821}
+}
+
+@software{easystatspackage,
+ title = {{easystats}: Streamline Model Interpretation, Visualization, and Reporting},
+ author = {Daniel Lüdecke and Dominique Makowski and Mattan S. Ben-Shachar and Indrajeet Patil and Brenton M. Wiernik and Etienne Bacher and Rémi Thériault},
+ year = {2023},
+ date = {2023-02-04},
+ url = {https://easystats.github.io/easystats/}
+}
+
+@article{ludecke2020extracting,
+ title={Extracting, computing and exploring the parameters of statistical models using {R}},
+ author={Lüdecke, Daniel and Ben-Shachar, Mattan S and Patil, Indrajeet and Makowski, Dominique},
+ journal={Journal of Open Source Software},
+ volume={5},
+ number={53},
+ pages={2445},
+ year={2020},
+ doi = {10.21105/joss.02445},
+ url = {https://doi.org/10.21105/joss.02445}
+}
+
+@Article{ludecke2021performance,
+ author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Philip Waggoner and Dominique Makowski},
+ title = {{performance}: An {R} package for assessment, comparison and testing of statistical models},
+ volume = {6},
+ number = {60},
+ journal = {Journal of Open Source Software},
+ year = {2021},
+ pages = {3139},
+ doi = {10.21105/joss.03139},
+ url = {https://doi.org/10.21105/joss.03139}
+}
+
+@article{ben2020effectsize,
+ title={{effectsize}: Estimation of effect size indices and standardized parameters},
+ author={Ben-Shachar, Mattan S and Lüdecke, Daniel and Makowski, Dominique},
+ journal={Journal of Open Source Software},
+ volume={5},
+ number={56},
+ pages={2815},
+ year={2020},
+ doi = {10.21105/joss.02815},
+ url = {https://doi.org/10.21105/joss.02815}
+}
+
+@Manual{russell2024emmeans,
+ title = {{emmeans}: Estimated Marginal Means, aka Least-Squares Means},
+ author = {Russell V. Lenth},
+ year = {2024},
+ note = {R package version 1.10.4},
+ doi = {10.32614/CRAN.package.emmeans},
+ url = {https://doi.org/10.32614/CRAN.package.emmeans}
+}
+
+@article{arel2024interpret,
+ title={How to interpret statistical models using marginaleffects for {R} and {Python}},
+ author={Arel-Bundock, Vincent and Greifer, Noah and Heiss, Andrew},
+ journal={Journal of Statistical Software},
+ volume={111},
+ pages={1-32},
+ year={2024},
+ doi = {10.18637/jss.v111.i09},
+ url = {https://doi.org/10.18637/jss.v111.i09}
+}
+
+@article{ludecke2019insight,
+ title={{insight}: A unified interface to access information from model objects in R},
+ author={L{\"u}decke, Daniel and Waggoner, Philip D and Makowski, Dominique},
+ journal={Journal of Open Source Software},
+ volume={4},
+ number={38},
+ pages={1412},
+ year={2019},
+ doi = {10.21105/joss.01412},
+ url = {https://doi.org/10.21105/joss.01412}
+}
+
+@article{becker1988new,
+ title={The New {S} Language},
+ author={Becker, Richard A and Chambers, John M and Wilks, Allan R},
+ journal={Cole.[Google Scholar]},
+ year={1988},
+ publisher={Wadsworth \& Brooks}
+}
+
+@article{chatton_rohrer_2024,
+ author = {Arthur Chatton and Julia M. Rohrer},
+ title ={The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization},
+ journal = {Advances in Methods and Practices in Psychological Science},
+ volume = {7},
+ number = {1},
+ pages = {25152459241236149},
+ year = {2024},
+ doi = {10.1177/25152459241236149},
+ URL = {https://doi.org/10.1177/25152459241236149}
+}
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+This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2023.12.17) 11 FEB 2025 11:07
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