::: {.goals}

- ...

- ...

:::

Many simple stats are built into R (`glm()`, `chisq.test()` and many more). No additional

packages needed! For more advanced analyses there are often multiple packages. Best to ask

a domain expert what they use (or Google). Here are just a few examples:

- [GAM](https://noamross.github.io/gams-in-r-course/),

[HLM](https://github.com/lme4/lme4/), [Mixture

Models](https://mclust-org.github.io/mclust/)...

- [SEM with `{lavaan}`](https://lavaan.ugent.be/)

- Robust stats with {WRS2}

- Time series with [`{forecast}`](https://github.com/robjhyndman/forecast) `{zoo}`,

`{xts}`

- [Bayesian Statistics](https://github.com/rmcelreath/statrethinking_winter2019) with

[`{brms}`](https://paul-buerkner.github.io/brms/),

[`{rstanarm}`](https://mc-stan.org/rstanarm/),

[`{rstan}`](https://mc-stan.org/users/interfaces/rstan)

- [Text mining with `{tidytext}`](https://www.tidytextmining.com/)

- [Machine learning with `{tidymodels}`](https://www.tidymodels.org/)

- [Network analysis](https://kateto.net/network-visualization) with `{tidygraph}` or

`{igraph}`

- [Meta analysis](https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/) with

`{metafor}` and `{rmeta}`

- [Introduction to Statistical Learning](https://www.statlearning.com/) -- A classic

introduction to machine learning using R.

- [`{tidymodels}`](https://www.tidymodels.org/) -- package for a more modern machine

learning toolbelt in R

There are too many things to mention. The CRAN task views can be a good start.

Likewise, [Introductory Statistics in Psychology using R](https://rpsystats.com/)

``` {.r}

Let's try something more advanced...

``` {.r}