Package: opa 0.8.2.033

Timothy Beechey

opa: An Implementation of Ordinal Pattern Analysis

Quantifies hypothesis to data fit for repeated measures and longitudinal data, as described by Thorngate (1987) <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as pairwise relative orderings which are then compared to determine the percentage of orderings in the data that are matched by the hypothesis.

Authors:Timothy Beechey [aut, cre]

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manual.pdf |manual.html✨
card.svg |card.png
opa/json (API)

# Install 'opa' in R:
install.packages('opa', repos = c('https://timbeechey.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/timbeechey/opa/issues

Pkgdown/docs site:https://timbeechey.github.io

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

On CRAN:

Conda:

data-analysishypothesis-testinglongitudinalordinalrcpprepeated-measuresstatisticscpp

3.70 score 1 stars 2 scripts 191 downloads 16 exports 3 dependencies

Last updated from:1881bdd6bc. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK119
linux-devel-x86_64OK129
source / vignettesOK169
linux-release-arm64OK114
linux-release-x86_64OK123
macos-release-arm64OK147
macos-release-x86_64OK240
macos-oldrel-arm64OK207
macos-oldrel-x86_64OK376
windows-develOK105
windows-releaseOK115
windows-oldrelOK90
wasm-releaseOK108

Exports:compare_conditionscompare_groupscompare_hypothesescorrect_pairscval_plotgroup_cvalsgroup_pccsgroup_resultshypothesisincorrect_pairsindividual_cvalsindividual_pccsindividual_resultsopapcc_plotrandom_pccs

Dependencies:latticeRcppRcppArmadillo

opa

Rendered fromopa.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2024-03-09
Started: 2023-12-14

Readme and manuals

Help Manual

Help pageTopics
Bee databees
Calculates PCCs and c-values based on pairwise comparison of conditions.compare_conditions
Calculate the c-value of the difference in PCCs produced by two groupscompare_groups
Calculate the c-value of the difference in PCCs produced by two hypothesescompare_hypotheses
Return the number of pairs of observations matched by the hypothesiscorrect_pairs
Plot individual chance valuescval_plot
Return the group chance values of the specified modelgroup_cvals
Return the group PCCs of the specified modelgroup_pccs
Group-level PCC and chance values.group_results
Create a hypothesis objecthypothesis
Return the number of pairs of observations not matched by the hypothesisincorrect_pairs
Return the individual chance values of the specified modelindividual_cvals
Return the individual PCCs of the specified modelindividual_pccs
Individual-level PCC and chance values.individual_results
Fit an ordinal pattern analysis modelopa
Plot individual PCCs.pcc_plot
Childhood growth datapituitary
Plots individual-level PCCs and chance-values.plot.opafit
Plot group comparison PCC replicates.plot.opaGroupComparison
Plot a hypothesis.plot.opahypothesis
Plot hypothesis comparison PCC replicates.plot.opaHypothesisComparison
Plot PCC replicates.plot.oparandpccs
Displays the call used to fit an ordinal pattern analysis model.print.opafit
Prints a summary of results from hypothesis comparison.print.opaGroupComparison
Print details of a hypothesisprint.opahypothesis
Prints a summary of results from hypothesis comparison.print.opaHypothesisComparison
Displays the results of a pairwise ordinal pattern analysis.print.pairwiseopafit
Return the random order generated PCCs used to calculate the group chance valuerandom_pccs
Prints a summary of results from a fitted ordinal pattern analysis model.summary.opafit
Prints a summary of results from hypothesis comparison.summary.opaGroupComparison
Prints a summary of results from hypothesis comparison.summary.opaHypothesisComparison