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<h1>This is a love letter</h1> <p>I love McElreath’s <a href="http://xcelab.net/rm/statistical-rethinking/" rel="nofollow"><em>Statistical Rethinking</em> text</a>. It's the entry-level textbook for applied researchers I spent years looking for. McElreath's <a href="https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA/playlists" rel="nofollow">freely-available lectures</a> on the book are really great, too.</p> <p>However, I prefer using Bürkner’s <a href="https://github.com/paul-buerkner/brms" rel="nofollow">brms package</a> when doing Bayeian regression in R. <a href="http://andrewgelman.com/2017/01/10/r-packages-interfacing-stan-brms/" rel="nofollow">It's just spectacular</a>. I also prefer plotting with Wickham's <a href="https://cran.r-project.org/web/packages/ggplot2/index.html" rel="nofollow">ggplot2</a>, and coding with functions and principles from the <a href="https://www.tidyverse.org" rel="nofollow">tidyverse</a>, which you might learn about <a href="http://style.tidyverse.org" rel="nofollow">here</a> or <a href="http://r4ds.had.co.nz/transform.html" rel="nofollow">here</a>.</p> <p>So, this project is an attempt to reexpress the code in McElreath’s textbook. His models are re-fit with brms, the figures are reproduced or reimagined with ggplot2, and the general data wrangling code now predominantly follows the tidyverse style. </p> <p>This project lives in two other places: <em> You can find the files on <a href="https://github.com/ASKurz/Statistical_Rethinking_with_brms_ggplot2_and_the_tidyverse" rel="nofollow">GitHub</a>. I tend to update those files more often than the ones here on the OSF, so feel free to head over there for the most current updates. </em> <a href="https://bookdown.org/connect/#/apps/1850/access" rel="nofollow">Go here</a> to find the online <a href="https://bookdown.org/connect/#/apps/1850/access" rel="nofollow">bookdown</a> version of the project. It'll probably be more useful for most purposes.</p> <p>Happy modeling!</p>
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