Package: precrec 0.14.5

Takaya Saito

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:Takaya Saito [aut, cre], Marc Rehmsmeier [aut]

precrec_0.14.5.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
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

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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.

On CRAN:

Conda:

cpp

9.94 score 45 stars 4 packages 594 scripts 5.4k downloads 14 mentions 10 exports 21 dependencies

Last updated from:e5e932db45. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK173
linux-devel-x86_64OK171
source / vignettesOK216
linux-release-arm64OK163
linux-release-x86_64OK162
macos-release-arm64OK162
macos-release-x86_64OK456
macos-oldrel-arm64OK184
macos-oldrel-x86_64OK281
windows-develOK182
windows-releaseOK158
windows-oldrelOK156
wasm-releaseOK122

Exports:aucauc_cicreate_sim_samplesevalmodformat_nfoldjoin_labelsjoin_scoresmmdatapartpauc

Dependencies:assertthatclicpp11data.tablefarverggplot2gluegridExtragtableisobandlabelinglifecycleR6RColorBrewerRcpprlangS7scalesvctrsviridisLitewithr

Introduction to precrec

Rendered fromintroduction.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2023-03-11
Started: 2015-11-29

Readme and manuals

Help Manual

Help pageTopics
Convert a curves and points object to a data frameas.data.frame as.data.frame.aucroc as.data.frame.mmcurves as.data.frame.mmpoints as.data.frame.mscurves as.data.frame.mspoints as.data.frame.smcurves as.data.frame.smpoints as.data.frame.sscurves as.data.frame.sspoints
Retrieve a data frame of AUC scoresauc auc.aucs
Calculate CIs of ROC and precision-recall AUCsauc_ci auc_ci.aucs
Plot performance evaluation measures with ggplot2autoplot autoplot.mmcurves autoplot.mmpoints autoplot.mscurves autoplot.mspoints autoplot.smcurves autoplot.smpoints autoplot.sscurves autoplot.sspoints
Balanced data with 1000 positives and 1000 negatives.B1000
Balanced data with 500 positives and 500 negatives.B500
Create random samples for simulationscreate_sim_samples
Evaluate models and calculate performance evaluation measuresevalmod
Create n-fold cross validation dataset from data frameformat_nfold
Convert a curves and points object to a data frame for ggplot2fortify fortify.mmcurves fortify.mmpoints fortify.mscurves fortify.mspoints fortify.smcurves fortify.smpoints fortify.sscurves fortify.sspoints
Imbalanced data with 1000 positives and 10000 negatives.IB1000
Imbalanced data with 500 positives and 5000 negatives.IB500
Join observed labels of multiple test datasets into a listjoin_labels
Join scores of multiple models into a listjoin_scores
5-fold cross validation sample.M2N50F5
Reformat input data for performance evaluation calculationmmdata
A small example dataset with several tied scores.P10N10
Calculate partial AUCspart part.mmcurves part.mscurves part.smcurves part.sscurves
Retrieve a data frame of pAUC scorespauc pauc.aucs
Plot performance evaluation measuresplot plot.mmcurves plot.mmpoints plot.mscurves plot.mspoints plot.smcurves plot.smpoints plot.sscurves plot.sspoints
precrec: A package for computing accurate ROC and Precision-Recall curvesprecrec-package precrec