### Installing multilevelmod Development Version (R)

Source: https://github.com/tidymodels/multilevelmod/blob/main/README.md

This snippet shows how to install the development version of the `multilevelmod` package directly from its GitHub repository using the `pak::pak()` function. This requires the `pak` package to be installed first and is suitable for users who want the latest features or bug fixes.

```R
# install.packages("pak")
pak::pak("tidymodels/multilevelmod")
```

--------------------------------

### Installing multilevelmod from CRAN (R)

Source: https://github.com/tidymodels/multilevelmod/blob/main/README.md

This snippet demonstrates how to install the stable version of the `multilevelmod` package directly from CRAN using the `install.packages()` function in R. This is the recommended method for most users.

```R
install.packages("multilevelmod")
```

--------------------------------

### Fitting a Frequentist Multilevel Linear Regression (R)

Source: https://github.com/tidymodels/multilevelmod/blob/main/README.md

This R code demonstrates how to fit a frequentist multilevel linear regression model using `multilevelmod`'s `lmer` engine with `parsnip`. It loads the `sleepstudy` dataset, defines a `linear_reg()` model specification, sets the engine to `lmer`, and fits the model using the `Reaction ~ Days + (Days | Subject)` formula, which includes random intercepts and slopes for `Subject`.

```R
library(multilevelmod)
set.seed(1234)
data(sleepstudy, package = "lme4")

mixed_model_spec <- linear_reg() |> set_engine("lmer")

mixed_model_fit <-
  mixed_model_spec |>
  fit(Reaction ~ Days + (Days | Subject), data = sleepstudy)
```

--------------------------------

### Fitting a Bayesian Multilevel Linear Regression (R)

Source: https://github.com/tidymodels/multilevelmod/blob/main/README.md

This R code illustrates how to fit a Bayesian multilevel linear regression model using `multilevelmod`'s `stan_glmer` engine with `parsnip`. It defines a `linear_reg()` model specification, sets the engine to `stan_glmer`, and fits the model using the `Reaction ~ Days + (Days | Subject)` formula, which includes random intercepts and slopes for `Subject`, providing a Bayesian perspective on the `sleepstudy` data.

```R
hier_model_spec <- linear_reg() |> set_engine("stan_glmer")

hier_model_fit <-
  hier_model_spec |>
  fit(Reaction ~ Days + (Days | Subject), data = sleepstudy)
```

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