In CoryMcCartan/conformalbayes: Jackknife(+) Predictive Intervals for Bayesian Models

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conformalbayes

conformalbayes provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021). 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).

Installation

You can install the development version of conformalbayes with:

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

Example

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.

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.

CoryMcCartan/conformalbayes documentation built on Nov. 3, 2024, 12:46 a.m.