Functions for producing enhanced image plots with a legend, scale,
projected coordinates, or multiple images with a common scale.

Display Multiple Images with Scaled Colors
Functions for displaying (multiple) images with scaled colors, possibly with projected coordinates and for an irregular grid.

Multiple Heat Maps for Projected Coordinates
Functions for displaying multiple images with a color
scale, i.e., heat maps, possibly with projected coordinates.  The
package relies on the base graphics system, so graphics are
rendered rapidly.
akima, abind, testthat, spatstat, gear, knitr, rmarkdown

fields, mapproj, viridisLite, ggplot2, maps
akima, abind, viridis, testthat, spatstat, gear, broom,
gridExtra, knitr, rmarkdown

fields, mapproj, viridisLite, ggplot2, maps, MBA
abind, viridis, testthat, spatstat, gear, broom, gridExtra,
knitr, rmarkdown

Functions for displaying multiple images  or scatterplots with a color
scale, i.e., heat maps, possibly with projected coordinates.  The
package relies on the base graphics system, so graphics are
rendered rapidly.

colorspace, fields, mapproj, ggplot2, maps, MBA
abind, testthat, spatstat, gear, broom, gridExtra, knitr,
rmarkdown

abind, testthat, spatstat.geom, gear, broom, gridExtra,
knitr, rmarkdown

c(
person(given = "Juan Cruz", family = "Rodriguez",
role = c("aut", "cre"), email = "jcrodriguez@unc.edu.ar"),
person(family = "Data Analytics at Texas A&M (DATA) Lab", role = "cph")
)
R Interface to 'AutoKeras' <https://autokeras.com/>.
'AutoKeras' is an open source software library for Automated Machine
Learning (AutoML). The ultimate goal of AutoML is to provide easily
accessible deep learning tools to domain experts with limited data science
or machine learning background. 'AutoKeras' provides functions to
automatically search for architecture and hyperparameters of deep
learning models.
Juan Cruz Rodriguez [aut, cre],
Data Analytics at Texas A&M (DATA) Lab [cph]

Automagically Document and Install Packages Necessary to Run R
Code
Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.
knitr, formatR, pacman, githubinstall, magrittr, dplyr, purrr,
devtools, remotes, yaml