evabic: Evaluation of Binary Classifiers
Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.
| Version: | 0.1.1 |
| Suggests: | testthat (≥ 2.1.0) |
| Published: | 2022-08-17 |
| Author: | Antoine Bichat 
[aut, cre] |
| Maintainer: | Antoine Bichat <antoine.bichat at proton.me> |
| BugReports: | https://github.com/abichat/evabic/issues |
| License: | GPL-3 |
| URL: | https://abichat.github.io/evabic/, https://github.com/abichat/evabic |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README NEWS |
| CRAN checks: | evabic results |
Documentation:
| Reference manual: | evabic.pdf |
Downloads:
| Package source: | evabic_0.1.1.tar.gz |
| Windows binaries: | r-devel: evabic_0.1.1.zip, r-release: evabic_0.1.1.zip, r-oldrel: evabic_0.1.1.zip |
| macOS binaries: | r-release (arm64): evabic_0.1.1.tgz, r-oldrel (arm64): evabic_0.1.1.tgz, r-release (x86_64): evabic_0.1.1.tgz, r-oldrel (x86_64): evabic_0.1.1.tgz |
| Old sources: | evabic archive |
Linking:
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