As you probably know, I’m a big fan of R’s `brms`

package, available from CRAN. In case you haven’t heard of it, `brms`

is an R package by Paul-Christian Buerkner that implements Bayesian regression of all types using an extension of R’s formula specification that will be familiar to users of `lm`

, `glm`

, and `lmer`

. Under the hood, it translates the formula into Stan code, Stan translates this to C++, your system’s C++ compiler is used to compile the result and it’s run.

`brms`

is impressive in its own right. But also impressive is how it continues to add capabilities and the breadth of Buerkner’s vision for it. I last posted something way back on version 0.8, when `brms`

gained the ability to do non-linear regression, but now we’re up to version 1.1, with 1.2 around the corner. What’s been added since 0.8, you may ask? Here are a few highlights: