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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 ORCID iD [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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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.
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