data.table, yaml, R6, gtable, ggplot2 (>= 2.2.0), magrittr,
stringr, assertthat, GGally, zoo, knitr, rmarkdown, tidyr,
dplyr, purrr, readr, rlang, tibble, checkmate, scales

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
activities in order to reduce the overall effort required for generating such plots.
For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
<doi:10.1002/psp4.12161>.

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
activities in order to reduce the overall effort required for generating such plots.
For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
<doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' 'lixoftConnectors'
package in the 'Monolix' installation, as described at the following url
<https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>.
When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required
Chart Data instead of exporting it from the 'Monolix' gui.
testthat, xtable, vdiffr, rxode2, nlmixr, nlmixr2,
nlmixr2data, lixoftConnectors, xgxr, withr

Quality Control Charts for the Grammar of Graphics Plotting
System
Plot single and faceted type quality control charts
within the grammar of graphics plotting framework.

Plot single and faceted type quality control charts
for 'ggplot'.
testthat, gridExtra, knitr, rmarkdown, reshape2, plyr

Compare Big Datasets to the Uniform Distribution
A quantile-quantile plot can be used to compare a sample of p-values
to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
calculate the quantiles, but thins it out in a way that focuses on points near zero
before plotting to speed up plotting and decrease file size, when vector graphics are stored.