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opa

An R package for ordinal pattern analysis.

Installation

opa can be installed from CRAN with:

install.packages("opa")

You can install the development version of opa from GitHub with:

# install.packages("remotes")
remotes::install_github("timbeechey/opa")

Using opa

See the introductory guide for a brief demonstration of how to conduct an ordinal pattern analysis using opa.

Acknowledgements

Development of opa was supported by a Medical Research Foundation Fellowship (MRF-049-0004-F-BEEC-C0899).

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Version

Install

install.packages('opa')

Monthly Downloads

191

Version

0.8.3

License

GPL (>= 3)

Maintainer

Timothy Beechey

Last Published

March 11th, 2024

Functions in opa (0.8.3)

compare_groups

Calculate the c-value of the difference in PCCs produced by two groups
compare_hypotheses

Calculate the c-value of the difference in PCCs produced by two hypotheses
individual_pccs

Return the individual PCCs of the specified model
individual_results

Individual-level PCC and chance values.
plot.opahypothesis

Plot a hypothesis.
plot.oparandpccs

Plot PCC replicates.
summary.opafit

Prints a summary of results from a fitted ordinal pattern analysis model.
bees

Bee data
group_cvals

Return the group chance values of the specified model
compare_conditions

Calculates PCCs and c-values based on pairwise comparison of conditions.
group_pccs

Return the group PCCs of the specified model
incorrect_pairs

Return the number of pairs of observations not matched by the hypothesis
individual_cvals

Return the individual chance values of the specified model
pituitary

Childhood growth data
group_results

Group-level PCC and chance values.
correct_pairs

Return the number of pairs of observations matched by the hypothesis
plot.opaGroupComparison

Plot group comparison PCC replicates.
cval_plot

Plot individual chance values
print.opaGroupComparison

Prints a summary of results from hypothesis comparison.
hypothesis

Create a hypothesis object
summary.opaGroupComparison

Prints a summary of results from hypothesis comparison.
summary.opaHypothesisComparison

Prints a summary of results from hypothesis comparison.
print.opaHypothesisComparison

Prints a summary of results from hypothesis comparison.
opa

Fit an ordinal pattern analysis model
pcc_plot

Plot individual PCCs.
print.pairwiseopafit

Displays the results of a pairwise ordinal pattern analysis.
random_pccs

Return the random order generated PCCs used to calculate the group chance value
plot.opaHypothesisComparison

Plot hypothesis comparison PCC replicates.
plot.opafit

Plots individual-level PCCs and chance-values.
print.opafit

Displays the call used to fit an ordinal pattern analysis model.
print.opahypothesis

Print details of a hypothesis