Description: The goal of 'image2data' is to extract images and return
especially for teaching data manipulation and data visualization.
Basically, the eponymous function takes an
image file ('png', 'tiff', 'jpeg', 'bmp') and turn it into a data set,
pixels being rows (subjects) and columns (variables) being their coordinate positions (x- and y-axis) and their respective color (in hex codes).
The function can return a complete image or a range of color (i.e., contour, silhouette).
The data can then be manipulated as would any data set by either creating other related variables (to hide the image) or as a genuine toy data set.

Title: Aids in Processing and Plotting Data from a Lemna-Tec
Description: Extracts traits from imaging data produced using a Lemna-Tec
Scananalyzer. Growth rates are between successive imagings are obtained and
those for a nominated set of intervals can also be calculated. Profile or
longitudinal plots of the traits and growth rates can be produced.

Description: Extracts traits from imaging data produced using a Lemna-Tec Scananalyzer
information). Growth rates between successive imagings are obtained and those for
a nominated set of intervals can also be calculated. Profile or longitudinal plots
of the traits and growth rates can be produced. These allow one to check for
anomalous data and to explore growth patterns in the data.

Description: Note that 'imageData' has been superseded by 'growthPheno'.
The package 'growthPheno' incorporates all the functionality of
'imageData' and has functionality not available in 'imageData',
but some 'imageData' functions have been renamed.
The 'imageData' package is no longer maintained, but is retained

Title: Image Statistics Based on Processing Fluency
Authors@R: person(given = "Stefan", family = "Mayer", email = "stefan@mayer-de.com",
Description: 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.
Imports: R.utils, readbitmap, pracma, quadprog, magick, OpenImageR
Suggests: grid, ggplot2, scales, shiny, testthat, knitr, rmarkdown
Collate: 'utils.R' 'complexity.R' 'contrast.R' 'imagefluency-package.R'
Author: Stefan Mayer [aut, cre] (<https://orcid.org/0000-0003-0034-7090>)