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All functions

bees
Bee data
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(<opaGroupComparison>)
Plot group comparison PCC replicates.
plot(<opaHypothesisComparison>)
Plot hypothesis comparison PCC replicates.
plot(<opafit>)
Plots individual-level PCCs and chance-values.
plot(<opahypothesis>)
Plot a hypothesis.
plot(<oparandpccs>)
Plot PCC replicates.
print(<opaGroupComparison>)
Prints a summary of results from hypothesis comparison.
print(<opaHypothesisComparison>)
Prints a summary of results from hypothesis comparison.
print(<opafit>)
Displays the call used to fit an ordinal pattern analysis model.
print(<opahypothesis>)
Print details of a hypothesis
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(<opaGroupComparison>)
Prints a summary of results from hypothesis comparison.
summary(<opaHypothesisComparison>)
Prints a summary of results from hypothesis comparison.
summary(<opafit>)
Prints a summary of results from a fitted ordinal pattern analysis model.