[aut, cre]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: | 0.2.3 |
| Depends: | R (≥ 3.2.3) |
| Imports: | R.utils, readbitmap, pracma, magick, OpenImageR |
| Suggests: | grid, ggplot2, scales, shiny, testthat, mockery, knitr, rmarkdown |
| Published: | 2020-01-09 |
| 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://stm.github.io/imagefluency |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | imagefluency results |
| Reference manual: | imagefluency.pdf |
| Vignettes: |
introduction |
| Package source: | imagefluency_0.2.3.tar.gz |
| Windows binaries: | r-devel: imagefluency_0.2.3.zip, r-release: imagefluency_0.2.3.zip, r-oldrel: imagefluency_0.2.3.zip |
| macOS binaries: | r-release: imagefluency_0.2.3.tgz, r-oldrel: imagefluency_0.2.3.tgz |
| Old sources: | imagefluency archive |
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