# Cumulative History Analysis for Bistable Perception Time Series

## Documentation for package ‘bistablehistory’ version 1.1.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

bistablehistory-package | Cumulative History Analysis for Bistable Perception Time Series
bayes_R2 | Computes R-squared using Bayesian R-squared approach.
bayes_R2.cumhist | Computes R-squared using Bayesian R-squared approach.
bistablehistory | 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 'cumhist'.
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).