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Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.
| Version: | 0.1.0 |
| Imports: | e1071, stats |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2023-10-05 |
| DOI: | 10.32614/CRAN.package.conformalpvalue |
| Author: | Chhavi Tyagi [aut, cre] |
| Maintainer: | Chhavi Tyagi <tyagi.chhavi2222 at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | conformalpvalue results |
| Reference manual: | conformalpvalue.pdf |
| Package source: | conformalpvalue_0.1.0.tar.gz |
| Windows binaries: | r-devel: conformalpvalue_0.1.0.zip, r-release: conformalpvalue_0.1.0.zip, r-oldrel: conformalpvalue_0.1.0.zip |
| macOS binaries: | r-release (arm64): conformalpvalue_0.1.0.tgz, r-oldrel (arm64): conformalpvalue_0.1.0.tgz, r-release (x86_64): conformalpvalue_0.1.0.tgz, r-oldrel (x86_64): conformalpvalue_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.