acss: measures of algorithmic complexity in R.
Features
- Main functionality is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method (see
?acss
). The database containing the complexity is provided in the data only packageacss.data
, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity.
For a full overview see our introductory paper:
Gauvrit, N., Singmann, H., Soler-Toscano, F., & Zenil, H. (2016). Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method. Behavior Research Methods, 48(1), 314-329. http://doi.org/10.3758/s13428-015-0574-3
Installation
- From CRAN:
install.packages("acss")
- Development version from Github (which requires
devtools
):
devtools::install_github("singmann/acss")
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.