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✨
DESCRIPTION
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:

bees - Bee data
pituitary - Childhood growth data

On CRAN:

data-analysis hypothesis-testing longitudinal ordinal rcpp repeated-measures statistics cpp

3.70 score 1 stars 3 scripts 247 downloads 16 exports 3 dependencies

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

Target	Result	Time
linux-devel-arm64	OK	117
linux-devel-x86_64	OK	122
source / vignettes	OK	186
linux-release-arm64	OK	119
linux-release-x86_64	OK	126
macos-release-arm64	OK	134
macos-release-x86_64	OK	208
macos-oldrel-arm64	OK	138
macos-oldrel-x86_64	OK	276
windows-devel	OK	148
windows-release	OK	109
windows-oldrel	OK	118
wasm-release	OK	103

Exports:compare_conditions compare_groups compare_hypotheses correct_pairs cval_plot group_cvals group_pccs group_results hypothesis incorrect_pairs individual_cvals individual_pccs individual_results opa pcc_plot random_pccs

Dependencies:lattice Rcpp RcppArmadillo

opa

Rendered fromopa.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bee data	bees
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 groups	compare_groups
Calculate the c-value of the difference in PCCs produced by two hypotheses	compare_hypotheses
Return the number of pairs of observations matched by the hypothesis	correct_pairs
Plot individual chance values	cval_plot
Return the group chance values of the specified model	group_cvals
Return the group PCCs of the specified model	group_pccs
Group-level PCC and chance values.	group_results
Create a hypothesis object	hypothesis
Return the number of pairs of observations not matched by the hypothesis	incorrect_pairs
Return the individual chance values of the specified model	individual_cvals
Return the individual PCCs of the specified model	individual_pccs
Individual-level PCC and chance values.	individual_results
Fit an ordinal pattern analysis model	opa
Plot individual PCCs.	pcc_plot
Childhood growth data	pituitary
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 hypothesis	print.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 value	random_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