An Implementation of Ordinal Pattern Analysis

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Documentation for package ‘opa’ version 0.8.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

compare_conditions	Calculates PCCs and c-values based on pairwise comparison of conditions.
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
correct_pairs	Return the number of pairs of observations matched by the hypothesis
cval_plot	Plot individual chance values
group_cvals	Return the group chance values of the specified model
group_pccs	Return the group PCCs of the specified model
group_results	Group-level PCC and chance values.
hypothesis	create a hypothesis object
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
individual_pccs	Return the individual PCCs of the specified model
individual_results	Individual-level PCC and chance values.
opa	Fit an ordinal pattern analysis model
pcc_plot	Plot individual PCCs.
pituitary	Childhood growth data
plot.opafit	Plots individual-level PCCs and chance-values.
plot.opahypothesis	Plot a hypothesis.
print.opafit	Displays the call used to fit an ordinal pattern analysis model.
print.opaGroupComparison	Prints a summary of results from hypothesis comparison.
print.opahypothesis	Print details of a hypothesis
print.opaHypothesisComparison	Prints a summary of results from hypothesis comparison.
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
summary.opafit	Prints a summary of results from a fitted ordinal pattern analysis model.
summary.opaGroupComparison	Prints a summary of results from hypothesis comparison.
summary.opaHypothesisComparison	Prints a summary of results from hypothesis comparison.