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To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Hash IDentifiers (SWHIDs) must be used.
Select below a type of object currently browsed in order to display its associated SWHID and permalink.

  • content
content badge Iframe embedding
swh:1:cnt:83ead2cbf83f4c916642130aa168e6483066ae5f

This interface enables to generate software citations, provided that the root directory of browsed objects contains a citation.cff or codemeta.json file.
Select below a type of object currently browsed in order to generate citations for them.

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Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
---
title: "3. Become a Bayesian master"
output: 
  github_document:
    toc: true
    fig_width: 10.08
    fig_height: 6
  rmarkdown::html_vignette:
    toc: true
    fig_width: 10.08
    fig_height: 6
tags: [r, bayesian, posterior, test]
vignette: >
  \usepackage[utf8]{inputenc}
  %\VignetteIndexEntry{Example 3: Become a Bayesian master}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
bibliography: bibliography.bib
csl: apa.csl
---

This vignette can be referred to by citing the package:

- Makowski, D., Ben-Shachar M. S. \& Lüdecke, D. (2019). *Understand and Describe Bayesian Models and Posterior Distributions using bayestestR*. Available from https://github.com/easystats/bayestestR. DOI: [10.5281/zenodo.2556486](https://zenodo.org/record/2556486).

---

```{r message=FALSE, warning=FALSE, include=FALSE}
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
knitr::opts_chunk$set(dpi=300)
options(digits=2)

set.seed(333)
```

```{r echo=FALSE, fig.cap="Yoda Bayes (896 BBY - 4 ABY).", fig.align='center', out.width="80%"}
knitr::include_graphics("https://github.com/easystats/easystats/raw/master/man/figures/bayestestR/YodaBayes.jpg")
```




## Mixed Models

TO BE CONTINUED.

### Priors

TO BE CONTINUED.


## What's next?

The journey to become a true Bayesian master is not over. It is merely the beginning. It is now time to leave the `bayestestR` universe and apply the Bayesian framework in a variety of other statistical contexts: 

- [**Marginal means**](https://easystats.github.io/estimate/articles/marginal_means.html)
- [**Contrast analysis**](https://easystats.github.io/estimate/articles/contrast_analysis.html)
- [**Testing Contrasts from Bayesian Models with 'emmeans' and 'bayestestR'**](https://easystats.github.io/blog/posts/bayestestr_emmeans/)

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