---
title: "Supported Models and recipes steps"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Supported Models and recipes steps}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

The supported models currently all come from [tidypredict](https://tidypredict.tidymodels.org/) right now.

## Supported models

The following models are supported by `tidypredict`:

- Linear Regression - `lm()`
- Generalized Linear model - `glm()`
- Random Forest models - `randomForest::randomForest()`
- Random Forest models, via `ranger` - `ranger::ranger()`
- MARS models - `earth::earth()`
- XGBoost models - `xgboost::xgb.Booster.complete()` 
- Cubist models - `Cubist::cubist()` 
- Tree models, via `partykit` - `partykit::ctree()` 

### `parsnip` 

`tidypredict` supports models fitted via the `parsnip` interface. The ones confirmed currently work in `tidypredict` are:

- `lm()` - `parsnip`: `linear_reg()` with *"lm"* as the engine.
- `randomForest::randomForest()` - `parsnip`: `rand_forest()` with *"randomForest"* as the engine.
- `ranger::ranger()` - `parsnip`: `rand_forest()` with *"ranger"* as the engine.
- `earth::earth()`  - `parsnip`: `mars()` with *"earth"* as the engine.

## Recipes steps

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(orbital)
```

```{r}
#| echo: false
all_funs <- ls(getNamespace("orbital"))

steps <- grep("orbital.step_", all_funs, value = TRUE)
steps <- gsub("orbital.", "", steps)
```

The following `r length(steps)` recipes steps are supported

```{r, results='asis'}
#| echo: false
cat(paste0("- `", steps, "()`\n"))
```
