Type: Package
Title: Krippendorff's Alpha for Multi-Valued Data
Version: 0.5.1
Description: Calculate Krippendorff's alpha for multi-valued data using the methods introduced by Krippendorff and Craggs (2016) <doi:10.1080/19312458.2016.1228863>. Nominal, ordinal, interval, and ratio data types are supported, with options to create bootstrapped estimates of alpha and/or parallelize calculations.
License: AGPL (≥ 3)
Encoding: UTF-8
URL: https://github.com/therealcfdrake/mvalpha
BugReports: https://github.com/therealcfdrake/mvalpha/issues
Depends: R (≥ 4.2.0)
RoxygenNote: 7.3.3
LazyData: true
Imports: stats, utils, rlang, Rdpack
Suggests: parallel
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2025-10-13 15:24:27 UTC; corie
Author: Corie Drake ORCID iD [aut, cre, cph]
Maintainer: Corie Drake <therealcfdrake@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-17 20:20:07 UTC

Published Examples

Description

These data represent examples found in the original paper describing the calculation of multi-valued Krippendorff's alpha by Krippendorff and Craggs (2016).

Usage

ex_table3

ex_table8a

ex_table8b

ex_table8c

ex_table9a

ex_table9b

ex_table9c

Format

Each is a multi-valued nominal set with observers as columns and units as rows.

Source

doi:10.1080/19312458.2016.1228863

References

Krippendorff K, Craggs R (2016). “The Reliability of Multi-Valued Coding of Data.” Communication Methods and Measures, 10(4), 181–198. doi:10.1080/19312458.2016.1228863.

Estimate Multi-Valued Krippendorff's Alpha

Description

mvalpha() calculates Krippendorff's alpha statistic when multi-valued observers are allowed to apply multiple values to an observation.

Usage

mvalpha(
  data,
  type = "nominal",
  verbose = TRUE,
  n_boot = NULL,
  parallelize = FALSE,
  cluster_size = NULL
)

Arguments

data

a data frame containing a list column for each observer. Each row represents an observation unit, and each cell contains a vector of 0 to w unique values, where w is the number of unique values found in the data set. NA values are used to represent missing observations and NULL values represent the empty set, {}, of responses.

type

a string describing the data type of the label set. This can be "nominal", "ordinal", "interval", or "ratio" and is used to select the appropriate distance metric.

verbose

a logical value which toggles whether status updates are printed to the console while alpha is being calculated.

n_boot

an integer representing the number of bootstrap estimates to calculate for mvDo. The default, NULL, will not generate additional estimates.

parallelize

a logical value indicating whether to implement parallelization using the parallel package.

cluster_size

an integer describing the number of cores to allocate to parallelization. If NULL and parallelize=TRUE, then the maximum number of available cores minus 1 will be used.

Value

An object of class mvalpha

References

Krippendorff K, Craggs R (2016). “The Reliability of Multi-Valued Coding of Data.” Communication Methods and Measures, 10(4), 181–198. doi:10.1080/19312458.2016.1228863.

Examples

library(mvalpha)

### replicate example from Table 3 in Krippendoff and Craggs (2016) with bootstrapped estimates

# View data
ex_table3

# Estimate alpha
x <- mvalpha(ex_table3, verbose = TRUE, n_boot = 500)

# View result
x

# View the unique values observed in the data
x$values

# View the unique labels used to code the data
x$labels

# Histogram of bootstrapped estimates
hist(x$bootstrap_mvalpha)

Create new mvalpha class object

Description

Wrapper for creating mvalpha class object.

Usage

new_mvalpha(
  mvalpha,
  type,
  mvDo,
  mvDe,
  bootstrap_mvalpha,
  unique_cardinalities,
  units,
  observers,
  labels,
  values,
  values_by_unit,
  dist_CK,
  p_CK,
  data
)

Arguments

mvalpha

Multi-valued alpha estimate

type

a string describing the data type of the label set. This can be "nominal", "ordinal", "interval", or "ratio" and is used to select the appropriate distance metric.

mvDo

Observed disagreement

mvDe

Expected disagreement

bootstrap_mvalpha

Bootstrap estimates of mvalpha

unique_cardinalities

Numeric vector of the unique cardinalities observed in the data

units

Names of units

observers

Names of observers

labels

Unique labels used in data

values

Unique values used in data

values_by_unit

Table of values by unit

dist_CK

Distance matrix for label sets C and K

p_CK

Probability matrix for label sets C and K

data

a data frame containing a list column for each observer. Each row represents an observation unit, and each cell contains a vector of 0 to w unique values, where w is the number of unique values found in the data set. NA values are used to represent missing observations and NULL values represent the empty set, {}, of responses.

Value

an mvalpha object

Print mvalpha class object

Description

Print generic

Usage

## S3 method for class 'mvalpha'
print(x, ...)

Arguments

x

mvalpha object

...

additional parameters

Value

invisibly returns the alpha estimate of an mvalpha object

Efficient Set Operations

Description

Find the intersection and set difference(s) of two sets all at once and more efficiently than calling base::intersect() and base::setdiff() separately. Based on this stackoverflow answer https://stackoverflow.com/a/72631719

Usage

set_ops(A, B, type)

Arguments

A, B

sets (vectors) of elements

type

a string describing the data type of the label set. This can be "nominal", "ordinal", "interval", or "ratio" and is used to select the appropriate distance metric.

mirror server hosted at Truenetwork, Russian Federation.