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
Author: Stefan Mayer ORCID iD [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:

Reference manual: imagefluency.pdf
Vignettes: getting-started

Downloads:

Package source: imagefluency_0.2.5.tar.gz
Windows binaries: r-devel: imagefluency_0.2.4.zip, r-release: imagefluency_0.2.5.zip, r-oldrel: imagefluency_0.2.5.zip
macOS binaries: r-release (arm64): imagefluency_0.2.5.tgz, r-oldrel (arm64): imagefluency_0.2.5.tgz, r-release (x86_64): imagefluency_0.2.5.tgz
Old sources: imagefluency archive

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