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%"
)
set.seed(5118)
```

# **conformalbayes** <a href="https://corymccartan.com/conformalbayes/"><img src="man/figures/logo.png" align="right" height="138" /></a>

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**conformalbayes** provides functions to construct finite-sample calibrated
predictive intervals for Bayesian models, following the approach in 
[Barber et al. (2021)](https://doi.org/10.1214/20-AOS1965).
These intervals are calculated efficiently using importance sampling for the
leave-one-out residuals.
By default, the intervals will also reflect the relative uncertainty in the 
Bayesian model, using the locally-weighted conformal methods of
[Lei et al. (2018)](https://doi.org/10.1080/01621459.2017.1307116).

## Installation

You can install the development version of **conformalbayes** with:

``` r
# install.packages("devtools")
devtools::install_github("CoryMcCartan/conformalbayes")
```

## Example

```{r, message=F}
library(rstanarm)
library(conformalbayes)
data("Loblolly")

fit_idx = sample(nrow(Loblolly), 50)
d_fit = Loblolly[fit_idx, ]
d_test = Loblolly[-fit_idx, ]

# fit a simple linear regression
m = stan_glm(height ~ sqrt(age), data=d_fit,
    chains=1, control=list(adapt_delta=0.999), refresh=0)

# prepare conformal predictions
m = loo_conformal(m)

# make predictive intervals
pred_ci = predictive_interval(m, newdata=d_test, prob=0.9)
print(head(pred_ci))

# are we covering?
mean(pred_ci[, "5%"] <= d_test$height &
         d_test$height <= pred_ci[, "95%"])
```

Read more on the [Getting Started page](https://corymccartan.com/conformalbayes/articles/conformalbayes.html).

## Citations

Barber, R. F., Candes, E. J., Ramdas, A., & Tibshirani, R. J. (2021). Predictive inference with the jackknife+. *The Annals of Statistics, 49*(1), 486-507.

Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-free predictive inference for regression. *Journal of the American Statistical Association, 113*(523), 1094-1111.