Package {Romney}

Type: Package
Title: Classical Cultural Consensus Analysis
Version: 0.1.0
Description: Implements classical cultural consensus analysis with formal, informal, and covariance agreement models, 'UCINET'-aligned minimum-residual factor extraction, competence estimation, and answer-key estimation. Based on the classical framework of Romney, Weller, and Batchelder (1986) <doi:10.1525/aa.1986.88.2.02a00020>, Romney, Batchelder, and Weller (1987) <doi:10.1177/000276487031002003>, and Weller (2007) <doi:10.1177/1525822X07303502>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: psych, stats
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
Author: Werner Hertzog [aut, cre]
Maintainer: Werner Hertzog <werner.hertzog@isek.uzh.ch>
URL: https://github.com/wernerhertzog/Romney
BugReports: https://github.com/wernerhertzog/Romney/issues
NeedsCompilation: no
Packaged: 2026-05-15 11:40:23 UTC; wberga
Repository: CRAN
Date/Publication: 2026-05-20 08:50:15 UTC

Agreement Matrices for Consensus Analysis

Description

Compute respondent-by-respondent agreement matrices for the formal, informal, and covariance consensus models.

Usage

agreement_formal(data, n_answers = NULL)

agreement_informal(data)

agreement_covariance(data, prior = 0.5)

Arguments

data

A respondent-by-item matrix or data frame.

n_answers

Number of possible answers for the formal model.

prior

Prior proportion of true items for the covariance model.

Value

A square agreement matrix.

Estimate a Formal Consensus Answer Key

Description

Estimate the most likely answer key under the formal consensus model.

Usage

answerkey_formal(data, competence, prior = NULL, answer_levels = NULL)

Arguments

data

A respondent-by-item matrix or data frame.

competence

Numeric competence scores, one per respondent.

prior

Optional prior distribution over answer levels.

answer_levels

Optional ordered vector of allowable answer levels.

Value

A list with key, probabilities, and levels.

Run a Cultural Consensus Analysis

Description

Run a cultural consensus analysis using UCINET-aligned minimum-residual factor extraction for the consensus eigensystem.

Usage

consensus(
  data,
  cultures = 1,
  method = c("formal", "informal", "covariance"),
  prior = 0.5,
  return_answer_key = TRUE
)

Arguments

data

A respondent-by-item matrix or data frame.

cultures

Number of latent cultures to extract.

method

One of "formal", "informal", or "covariance".

prior

Prior proportion of true items for the covariance model.

return_answer_key

Whether to estimate the answer key for the formal model.

Value

An object of class romney_consensus.

Simulate Formal Consensus Data

Description

Simulate respondent-by-item response data for the formal consensus model.

Usage

simulate_consensus_data(
  n_respondents,
  n_questions,
  n_answers = 2,
  competence = 0.7,
  seed = NULL
)

Arguments

n_respondents

Number of respondents.

n_questions

Number of questions/items.

n_answers

Number of possible answers per item.

competence

Scalar or vector of respondent competences.

seed

Optional random seed.

Value

A list with responses, key, and competence.