README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE, 
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# propensity <img src="man/figures/logo.png" align="right" height="138" />

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The goal of propensity is to calculate propensity scores and weights for a wide variety of research questions.

propensity is under very early development.

## Installation

You can install the development version of propensity from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("r-causal/propensity")
```

## Example

propensity is under very early development. Currently, it supports calculating propensity score weights for binary exposures:

```{r}
library(propensity)
propensity_scores <- c(.1, .3, .4, .3)
x <- c(0, 0, 1, 0)

# ATE weights
wt_ate(propensity_scores, .exposure = x)

# Stabilized ATE weights
wt_ate(propensity_scores, .exposure = x, stabilize = TRUE)

# ATO weights
wt_ato(propensity_scores, .exposure = x)
```


```{r example, include = FALSE, eval = FALSE}
library(propensity)

ps <- propensity(
  model, 
  exposure, 
  stabilizer = model2, 
  exposure_type = "categorical"
)

match(ps, data)

data |> 
  mutate(wts = wts(ps, estimand = "att"))
```