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

`install.packages("bistablehistory")`

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

```
library("devtools")
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 https://github.com/jeroen/v8.

You will also need the R package curl, which
depends on `libcurl-*`

in various operating systems. Please
see the documentation at https://cran.r-project.org/package=curl.

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

```
library(bistablehistory)
data(br_singleblock)
<- fit_cumhist(br_singleblock,
gamma_fit state = "State",
duration = "Duration")
```

or, equivalently

```
library(bistablehistory)
data(br_singleblock)
<- fit_cumhist(br_singleblock,
gamma_fit state = "State",
onset = "Time")
```

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

`history_tau(gamma_fit)`

and main effect of history for both parameters of gamma distribution

`coef(gamma_fit)`

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).