Using this package you can interact with the 'Aplos NCA API'<https://docs.aplosanalytics.com/> using standard R functions. This will allow you to authenticate with your 'Aplos NCA' account, upload input datasets, initiate analyses, and download results.

Supports data-driven composition of a main plot with associated subplots that need precise axis alignment. Unlike general layout-focused tools such as 'cowplot' and 'patchwork', it enables related subplots to be integrated on the top, bottom, left, or right sides of a main plot with matched axes, so that the combined panels can be interpreted as a coherent whole. This design was inspired by the 'Method 2' described in 'ggtree' (G Yu (2018) <doi:10.1093/molbev/msy194>).

Many complex plots are actually composite plots, such as 'oncoplot', 'funkyheatmap', 'upsetplot', etc. We can produce subplots using 'ggplot2' and combine them to create composite plots using 'aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.

Some functions for drawing some special plots:
   The function 'bagplot' plots a bagplot,
   'faces' plots chernoff faces,
   'iconplot' plots a representation of a frequency table or a data matrix,
   'plothulls' plots hulls of a bivariate data set,
   'plotsummary' plots a graphical summary of a data set,
   'puticon' adds icons to a plot,
   'skyline.hist' combines several histograms of a one dimensional data set in one plot,
   'slider' functions supports some interactive graphics,
   'spin3R' helps an inspection of a 3-dim point cloud,
   'stem.leaf' plots a stem and leaf plot,
   'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.

In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models' differential average prediction errors and the posterior distribution of which model is most robust.