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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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=imagefluency
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.