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All functions

bayes_R2(<cumhist>)
Computes R-squared using Bayesian R-squared approach.
bistablehistory-package
Cumulative History Analysis for Bistable Perception Time Series
br
Binocular rivalry data
br_contrast
Binocular rivalry, variable contrast
br_singleblock
Single run for binocular rivalry stimulus
br_single_subject
Single experimental session for binocular rivalry stimulus
coef(<cumhist>)
Extract Model Coefficients
compute_history()
Computes cumulative history for the time-series
cumhist-class
Class cumhist.
extract_history()
Computes history for a fitted model
extract_history_parameter()
Extracts a history parameter as a matrix
extract_replicate_term_to_matrix()
Extract a term and replicates it randomN times for each linear model
extract_term_to_matrix()
Extracts a term with one column per fixed or random-level into a matrix
fast_history_compute()
Computes cumulative history
fit_cumhist()
Fits cumulative history for bistable perceptual rivalry displays.
fixef()
Extract the fixed-effects estimates
historyef()
Extract the history-effects estimates
history_mixed_state()
Extract values of used or fitted history parameter mixed_state
history_parameter()
Extract values of used or fitted history parameter
history_tau()
Extract values of used or fitted history parameter tau
kde
Kinetic-depth effect data
kde_two_observers
Multirun data for two participants, kinetic-depth effect display
loo(<cumhist>)
Computes an efficient approximate leave-one-out cross-validation via loo library. It can be used for a model comparison via loo::loo_compare() function.
nc
Necker cube data
predict(<cumhist>)
Computes predicted dominance phase durations using posterior predictive distribution.
predict_history()
Computes predicted cumulative history using posterior predictive distribution.
predict_samples()
Computes prediction for a each sample.
preprocess_data()
Preprocesses time-series data for fitting
print(<cumhist>)
Prints out cumhist object
summary(<cumhist>)
Summary for a cumhist object
waic(<cumhist>)
Computes widely applicable information criterion (WAIC).