Package: precrec 0.14.4
precrec: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics) curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Authors:
precrec_0.14.4.tar.gz
precrec_0.14.4.zip(r-4.5)precrec_0.14.4.zip(r-4.4)precrec_0.14.4.zip(r-4.3)
precrec_0.14.4.tgz(r-4.4-x86_64)precrec_0.14.4.tgz(r-4.4-arm64)precrec_0.14.4.tgz(r-4.3-x86_64)precrec_0.14.4.tgz(r-4.3-arm64)
precrec_0.14.4.tar.gz(r-4.5-noble)precrec_0.14.4.tar.gz(r-4.4-noble)
precrec_0.14.4.tgz(r-4.4-emscripten)precrec_0.14.4.tgz(r-4.3-emscripten)
precrec.pdf |precrec.html✨
precrec/json (API)
NEWS
# Install 'precrec' in R: |
install.packages('precrec', repos = c('https://evalclass.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/evalclass/precrec/issues
- B1000 - Balanced data with 1000 positives and 1000 negatives.
- B500 - Balanced data with 500 positives and 500 negatives.
- IB1000 - Imbalanced data with 1000 positives and 10000 negatives.
- IB500 - Imbalanced data with 500 positives and 5000 negatives.
- M2N50F5 - 5-fold cross validation sample.
- P10N10 - A small example dataset with several tied scores.
Last updated 1 years agofrom:6ac1fdc8a9. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 13 2024 |
R-4.5-win-x86_64 | OK | Oct 13 2024 |
R-4.5-linux-x86_64 | OK | Oct 13 2024 |
R-4.4-win-x86_64 | OK | Oct 13 2024 |
R-4.4-mac-x86_64 | OK | Oct 13 2024 |
R-4.4-mac-aarch64 | OK | Oct 13 2024 |
R-4.3-win-x86_64 | OK | Oct 13 2024 |
R-4.3-mac-x86_64 | OK | Oct 13 2024 |
R-4.3-mac-aarch64 | OK | Oct 13 2024 |
Exports:aucauc_cicreate_sim_samplesevalmodformat_nfoldjoin_labelsjoin_scoresmmdatapartpauc
Dependencies:assertthatclicolorspacedata.tablefansifarverggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr