doi: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) <doi:10.1080/01621459.2017.1307116>.">

conformalbayes: Jackknife(+) Predictive Intervals for Bayesian Models (original) (raw)

Provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021) <doi: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) <doi:10.1080/01621459.2017.1307116>.

Version: 0.1.2
Imports: cli, rstantools, loo, matrixStats
Suggests: rstanarm, brms, testthat (≥ 3.0.0), ggplot2, knitr, rmarkdown
Published: 2022-08-12
DOI: 10.32614/CRAN.package.conformalbayes
Author: Cory McCartan ORCID iD [aut, cre]
Maintainer: Cory McCartan
BugReports: https://github.com/CoryMcCartan/conformalbayes/issues
License: MIT + file
URL: https://github.com/CoryMcCartan/conformalbayes,https://corymccartan.com/conformalbayes/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: conformalbayes results

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