imagefluency: Image Statistics Based on Processing Fluency
Get image statistics based on processing fluency theory. The
    functions provide scores for several basic aesthetic principles that
    facilitate fluent cognitive processing of images: contrast,
    complexity / simplicity, self-similarity, symmetry, and typicality.
    See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr
    (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.
| Version: | 0.2.5 | 
| Depends: | R (≥ 4.1.0) | 
| Imports: | R.utils, readbitmap, pracma, magick, OpenImageR | 
| Suggests: | grid, ggplot2, scales, shiny, testthat, mockery, knitr, rmarkdown, furrr, future, pbmcapply, tictoc, dplyr | 
| Published: | 2024-02-22 | 
| DOI: | 10.32614/CRAN.package.imagefluency | 
| Author: | Stefan Mayer  [aut, cre] | 
| Maintainer: | Stefan Mayer  <stefan at mayer-de.com> | 
| BugReports: | https://github.com/stm/imagefluency/issues/ | 
| License: | GPL-3 | 
| URL: | https://imagefluency.com, https://github.com/stm/imagefluency/,
https://doi.org/10.5281/zenodo.5614665 | 
| NeedsCompilation: | no | 
| Materials: | README, NEWS | 
| CRAN checks: | imagefluency results | 
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