Title:	Multiclass Performance Evaluation Toolkit
Version:	0.1.0
Description:	Implementation of custom 'tidymodels' metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problems” <doi:10.1016/j.knosys.2020.106490> and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) “Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificity” <doi:10.3760/cma.j.issn.0366-6999.20123102>.
License:	MIT + file LICENSE
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.3
URL:	https://github.com/astamm/mupet, https://astamm.github.io/mupet/
BugReports:	https://github.com/astamm/mupet/issues
Imports:	rlang, yardstick
NeedsCompilation:	no
Packaged:	2026-01-16 14:36:25 UTC; stamm-a
Author:	Lise Bellanger [aut], Manon Simonot [aut], Aymeric Stamm [aut, cre]
Maintainer:	Aymeric Stamm <aymeric.stamm@cnrs.fr>
Repository:	CRAN
Date/Publication:	2026-01-21 20:10:09 UTC

Macro Average Sensitivity

Description

This function computes the macro-average sensitivity for a multi-class prediction model. It assumes that the negative class is the first one.

Usage

macro_average_sensitivity_vec(
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

macro_average_sensitivity(data, ...)

## S3 method for class 'data.frame'
macro_average_sensitivity(
  data,
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

Arguments

Value

A scalar storing the value of the macro-average sensitivity score.

Examples

fold1 <- subset(yardstick::hpc_cv, Resample == "Fold01")
macro_average_sensitivity_vec(fold1$obs, fold1$pred)
macro_average_sensitivity(fold1, obs, pred)

Macro Average Specificity

Description

This function computes the macro-average specificity for a multi-class prediction model. It assumes that the negative class is the first one.

Usage

macro_average_specificity_vec(
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

macro_average_specificity(data, ...)

## S3 method for class 'data.frame'
macro_average_specificity(
  data,
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

Arguments

Value

A scalar storing the value of the macro-average specificity score.

Examples

fold1 <- subset(yardstick::hpc_cv, Resample == "Fold01")
macro_average_specificity_vec(fold1$obs, fold1$pred)
macro_average_specificity(fold1, obs, pred)

Weighted Youden Index

Description

This function computes the weighted Youden index for a multi-class prediction model. It assumes that the negative class is the first one.

Usage

weighted_youden_index_vec(
  truth,
  estimate,
  sensitivity_weight = 0.5,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

weighted_youden_index(data, ...)

## S3 method for class 'data.frame'
weighted_youden_index(
  data,
  truth,
  estimate,
  sensitivity_weight = 0.5,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

Arguments

Value

A scalar storing the value of the weighted Youden index.

Examples

fold1 <- subset(yardstick::hpc_cv, Resample == "Fold01")
weighted_youden_index_vec(fold1$obs, fold1$pred)
weighted_youden_index(fold1, obs, pred)