  Increased application of multivariate data in many scientific areas has
considerably raised the complexity of analysis and interpretation. Although
quite a few approaches have been put forward to address this issue, there is
still a gap between the most efficient proposed methods and available software.
muvis is an R package (core team (2017)) which is a toolkit for analyzing
multivariate datasets. Several tools are implemented for common analyses of
multivariate datasets, including preprocessing, dimensionality reduction,
statistical analysis, Probabilistic Graphical Modeling, hypothesis testing, and
visualization. Furthermore, we have implemented two novel
methods--Variable-wise Kullback-Leibler Divergence (VKL) and Violating
Variable-wise Kullback-Leibler Divergence (VVKL)--which are proposed to find
the features with most different probability distributions between two specific
groups of samples. The main aim of the package is to provide a wide range of
users with different levels of expertise in R with a set of tools for
comprehensive analysis of multivariate datasets. We exploited the NHANES
dataset to declare the functionality of muvis in practice.
