Interactive data exploration with one line of code, automated
    reporting or use an easy to remember set of tidy functions for low
    code exploratory data analysis.

Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.

Computation of the matrix exponential, logarithm, sqrt,
  and related quantities, using traditional and modern methods.

Easily export 'R' graphs and statistical output to 'Microsoft
    Office' / 'LibreOffice', 'Latex' and 'HTML' Documents, using sensible defaults
    that result in publication-quality output with simple, straightforward commands.
    Output to 'Microsoft Office' is in editable 'DrawingML' vector format for
    graphs, and can use corporate template documents for styling. This enables
    the production of standardized reports and also allows for manual tidy-up
    of the layout of 'R' graphs in 'Powerpoint' before final publication. Export
    of graphs is flexible, and functions enable the currently showing R graph
    or the currently showing 'R' stats object to be exported, but also allow the
    graphical or tabular output to be passed as objects. The package relies on package
    'officer' for export to 'Office' documents,and output files are also fully compatible
    with 'LibreOffice'. Base 'R', 'ggplot2' and 'lattice' plots are supported, as
    well as a wide variety of 'R' stats objects, via wrappers to xtable(), broom::tidy() 
    and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as
    well as matrices and data frames and many more...

A variety of descriptive multivariate analyses with the singular value decomposition,
    such as principal components analysis, correspondence analysis, and multidimensional scaling.
    See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.