Cumulative History Analysis For Bistable Perception Time Series

A package to compute a cumulative history for time-series of perceptual dominance in bistable displays.

Estimates cumulative history, an estimate of accumulating adaptation/prediction error for the dominant percept, for time-series for continuously viewed bistable perceptual rivalry displays. Computes cumulative history via a homogeneous first order differential process. I.e., it assumes exponential growth/decay of the history as a function of time and perceptually dominant state. Supports Gamma, log normal, and normal distribution families.

For details on rationale please refer to (Pastukhov & Braun, 2011).


For current stable version use


The master branch is the development version. To install it please use

install_github("alexander-pastukhov/bistablehistory", dependencies = TRUE)


This package uses Stan, a “state-of-the-art platform for statistical modeling and high-performance statistical computation”. Therefore, it depends on the package rstantools, which in turn depends on the rstan package, which uses the V8 JavaScript library, through the V8 R package.

Therefore, you will need to install the V8 JavaScript library on your system, and it is recommended that you also install the V8 R package beforehand. For detailed instructions, please see

You will also need the R package curl, which depends on libcurl-* in various operating systems. Please see the documentation at


The main function is fit_cumhist that takes a data frame with time-series as the first argument. Minimally, you need to specify state — string with the column name that encodes perceptually dominant state — and either duration (column name with duration of individual dominance phases) or onset (column name with onset times of individual dominance phases). Thus, for a simplest case of a single subject and single run/block measurement with all defaults (gamma distribution, fitted cumulative history time constant but fixed mixed state value and history mixing proportion) the call would be

gamma_fit <- fit_cumhist(br_singleblock,
                         state = "State",
                         duration = "Duration")

or, equivalently

gamma_fit <- fit_cumhist(br_singleblock,
                         state = "State",
                         onset = "Time")

Now you can look at the fitted value for history time constant via


and main effect of history for both parameters of gamma distribution


For further details please see vignettes on package usage (Usage examples and Cumulative history) and on an example of writing Stan code directly (Writing Stan code).