Package: irrCAC 1.4

Kilem L. Gwet

irrCAC: Computing the Extent of Agreement among Raters with Chance-Corrected Agreement Coefficient (CAC)

Contains a series of R functions for calculating various chance-corrected agreement coefficients (CAC) among 2 or more raters. Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. Also included in this package is Bangdiwala's B coefficient.

Authors:Kilem L. Gwet [aut, cre]

irrCAC_1.4.tar.gz
irrCAC_1.4.tar.gz(r-4.7-any)irrCAC_1.4.tar.gz(r-4.6-any)
irrCAC_1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
irrCAC/json (API)

# Install 'irrCAC' in R:
install.packages('irrCAC', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • agree.cac3rd - Dataset showing the distribution of 6 raters by psychiatric condition
  • agree.contingency - Dataset representing a 10x10 contingency table
  • agreeCAC - Ratings of 15 subjects from 4 raters
  • altman - Dataset describing the Altman's Benchmarking Scale
  • cac.ben.gerry - Ratings of 12 units from 2 raters named Ben and Gerry
  • cac.dist.g1g2 - Distribution of 4 raters by subject and by category, for 14 Subjects that belong to 2 groups "G1" and "G2"
  • cac.dist4cat - Distribution of 4 raters by Category and Subject - Subjects allocated in 2 groups A and B.
  • cac.raw.g1g2 - Dataset of raw ratings from 4 Raters on 14 Subjects that belong to 2 groups named "G1" and "G2"
  • cac.raw.gender - Rating Data from 4 Raters and 15 human Subjects, 9 of whom are female and 6 males.
  • cac.raw2raters - Dataset of raw ratings by 2 raters and 12 subjects.
  • cac.raw4raters - Rating Data from 4 Raters and 12 Subjects.
  • cac.raw5obser - Scores assigned by 5 observers to 20 experimental units.
  • cont3x3abstractors - Distribution of 100 pregnant women by pregnancy type and by abstractor.
  • cont4x4diagnosis - Distribution of 223 Psychiatric Patients by Type of of Psychiatric Disorder and Diagnosis Method.
  • distrib.6raters - Distribution of 6 psychiatrists by Subject/patient and diagnosis Category.
  • fleiss - Dataset describing Fleiss' Benchmarking Scale
  • freqs.data - Distribution of 10 subjects by rater (Ben and Gerry) and by category.
  • landis.koch - Dataset describing the Landis & Koch Benchmarking Scale
  • x.dist10x5 - Dataset of categorical ratings assigned to 10 subjects and presented in the form of a distribution of 4 raters by subject and category
  • x.dist6x5psy - Dataset showing how 6 psychiatrists classified 15 patients by their mental health condition.
  • x.raw10x4 - Raw categorical ratings assigned to 10 subjects by 4 raters
  • x.raw12x4 - This dataset contains raw categorical ratings that 4 raters assigned to 12 subjects.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.33 score 1 packages 106 scripts 1.1k downloads 4 mentions 32 exports 20 dependencies

Last updated from:720ab226e4. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK132
source / vignettesOK165
linux-release-x86_64OK176
wasm-releaseOK136

Exports:altman.bfbangdiwala.tablebangdiwala2RR.fnbipolar.weightsbp.coeff.distbp.coeff.rawbp2.tablecircular.weightsconger.kappa.rawfleiss.bffleiss.kappa.distfleiss.kappa.rawfreq.supp.fngwet.ac1.distgwet.ac1.rawgwet.ac1.tableidentity.weightskappa2.tablekrippen.alpha.distkrippen.alpha.rawkrippen2.tablelandis.koch.bflinear.weightslong2wide.fnordinal.weightspa.coeff.distpa.coeff.rawpa2.tablequadratic.weightsradical.weightsratio.weightsscott2.table

Dependencies:clicpp11dplyrgenericsgluelifecyclemagrittrpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselectutf8vctrswithr

irrCAC-benchmarking
Abstract | Interpreting the magnitude of agreement coeeficients | References:
Rendered frombenchmarking.Rmdusingknitr::rmarkdown

Last update: 2026-04-27
Started: 2019-09-23

Calculating Chance-corrected Agreement Coefficients (CAC)
Abstract | Computing Agreement Coefficients | Computing agreement Coefficients from Contingency tables | Computing agreement coefficients from the distribution of raters by subject & category | Computing agreement coefficients from raw ratings | References:
Rendered fromoverview.Rmdusingknitr::rmarkdown

Last update: 2026-04-27
Started: 2019-09-23

Weighted Chance-corrected Agreement Coefficients
Abstract | The different weights | Weighted Agreement Coefficients | Weighting ratings from a contingency table | Weighting for a dataset of raw ratings | Weighting when input data is the distribution of raters by subject and rating | References:
Rendered fromweighting.Rmdusingknitr::rmarkdown

Last update: 2026-04-27
Started: 2019-09-23

Readme and manuals

Help Manual

Help pageTopics
Dataset showing the distribution of 6 raters by psychiatric conditionagree.cac3rd
Dataset representing a 10x10 contingency tableagree.contingency
Ratings of 15 subjects from 4 ratersagreeCAC
Dataset describing the Altman's Benchmarking Scalealtman
Computing Altman's Benchmark Scale Membership Probabilitiesaltman.bf
Bangdiwala B coefficient for 2 ratersbangdiwala.table
Bangdiwala B coefficient for 2 raters when input dataset is made up of 2 columns of raw data.bangdiwala2RR.fn
Function for computing the Bipolar Weightsbipolar.weights
Brennan-Prediger's agreement coefficient among multiple raters (2, 3, +) when the input dataset is the distribution of raters by subject and category.bp.coeff.dist
Brennan & Prediger's (BP) agreement coefficient for an arbitrary number of raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.bp.coeff.raw
Brenann-Prediger coefficient for 2 ratersbp2.table
Ratings of 12 units from 2 raters named Ben and Gerrycac.ben.gerry
Distribution of 4 raters by subject and by category, for 14 Subjects that belong to 2 groups "G1" and "G2"cac.dist.g1g2
Distribution of 4 raters by Category and Subject - Subjects allocated in 2 groups A and B.cac.dist4cat
Dataset of raw ratings from 4 Raters on 14 Subjects that belong to 2 groups named "G1" and "G2"cac.raw.g1g2
Rating Data from 4 Raters and 15 human Subjects, 9 of whom are female and 6 males.cac.raw.gender
Dataset of raw ratings by 2 raters and 12 subjects.cac.raw2raters
Rating Data from 4 Raters and 12 Subjects.cac.raw4raters
Scores assigned by 5 observers to 20 experimental units.cac.raw5obser
Function for computing the Circular Weightscircular.weights
Conger's generalized kappa coefficient for an arbitrary number of raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.conger.kappa.raw
Distribution of 100 pregnant women by pregnancy type and by abstractor.cont3x3abstractors
Distribution of 223 Psychiatric Patients by Type of of Psychiatric Disorder and Diagnosis Method.cont4x4diagnosis
Distribution of 6 psychiatrists by Subject/patient and diagnosis Category.distrib.6raters
Dataset describing Fleiss' Benchmarking Scalefleiss
Computing Fleiss Benchmark Scale Membership Probabilitiesfleiss.bf
Fleiss' agreement coefficient among multiple raters (2, 3, +) when the input dataset is the distribution of raters by subject and category.fleiss.kappa.dist
Fleiss' generalized kappa among multiple raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.fleiss.kappa.raw
freq.supp.fn: This function reads a 3-variable input data file containing unique pairs of categories along with their frequency of occurrences, and outputs a similar file where all possible pairs of categories are represented, some with a frequency of occurrence of 0.freq.supp.fn
Distribution of 10 subjects by rater (Ben and Gerry) and by category.freqs.data
Gwet's AC1/AC2 agreement coefficient among multiple raters (2, 3, +) when the input dataset is the distribution of raters by subject and category.gwet.ac1.dist
Gwet's AC1/AC2 agreement coefficient among multiple raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.gwet.ac1.raw
Gwet's AC1/AC2 coefficient for 2 ratersgwet.ac1.table
Function for computing the Identity Weightsidentity.weights
Kappa coefficient for 2 raterskappa2.table
Krippendorff's agreement coefficient among multiple raters (2, 3, +) when the input dataset is the distribution of raters by subject and category.krippen.alpha.dist
Krippendorff's alpha coefficient for an arbitrary number of raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.krippen.alpha.raw
Krippendorff's Alpha coefficient for 2 raterskrippen2.table
Dataset describing the Landis & Koch Benchmarking Scalelandis.koch
Computing Landis-Koch Benchmark Scale Membership Probabilitieslandis.koch.bf
Function for computing the Linear Weightslinear.weights
long2wide.fn: This function transforms a 3-column dataset of frequencies to a square matrix or a contingency table. This function uses the freq.supp.fn() function.long2wide.fn
Function for computing the Ordinal Weightsordinal.weights
Percent agreement coefficient among multiple raters (2, 3, +) when the input dataset is the distribution of raters by subject and category.pa.coeff.dist
Percent agreement among multiple raters (2, 3, +) when the input data represent the raw ratings reported for each subject and each rater.pa.coeff.raw
Percent Agreement coefficient for 2 raterspa2.table
Function for computing the Quadratic Weightsquadratic.weights
Function for computing the Radical Weightsradical.weights
Function for computing the Ratio Weightsratio.weights
Scott's coefficient for 2 ratersscott2.table
An r function for trimming leading and trealing blankstrim
Dataset of categorical ratings assigned to 10 subjects and presented in the form of a distribution of 4 raters by subject and categoryx.dist10x5
Dataset showing how 6 psychiatrists classified 15 patients by their mental health condition.x.dist6x5psy
Raw categorical ratings assigned to 10 subjects by 4 ratersx.raw10x4
This dataset contains raw categorical ratings that 4 raters assigned to 12 subjects.x.raw12x4