https://github.com/corymccartan/conformalbayes

Jackknife(+) Predictive Intervals for Bayesian Models
https://github.com/corymccartan/conformalbayes

bayesian conformal-prediction prediction r

Last synced: 2 months ago
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Jackknife(+) Predictive Intervals for Bayesian Models

• Host: GitHub
• URL: https://github.com/corymccartan/conformalbayes
• Owner: CoryMcCartan
• License: other
• Created: 2022-02-25T23:54:24.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-11-02T16:05:04.000Z (12 months ago)
• Last Synced: 2025-07-22T15:50:21.748Z (3 months ago)
• Topics: bayesian, conformal-prediction, prediction, r
• Language: R
• Homepage: https://corymccartan.github.io/conformalbayes/
• Size: 5.33 MB
• Stars: 8
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE

Awesome Lists containing this project

README

          
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

set.seed(5118)

```

# **conformalbayes** 

[![CRAN status](https://www.r-pkg.org/badges/version/conformalbayes)](https://CRAN.R-project.org/package=conformalbayes)

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![R-CMD-check](https://github.com/CoryMcCartan/conformalbayes/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/CoryMcCartan/conformalbayes/actions/workflows/R-CMD-check.yaml)

**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.