Algebraic procedures for analyses of multiple social networks are delivered with this 
	    package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. 'multiplex' makes 
	    possible, among other things, to create and manipulate multiplex, multimode, and 
	    multilevel network data with different formats. Effective ways are available to treat 
	    multiple networks with routines that combine algebraic systems like the partially ordered 
	    semigroup with decomposition procedures or semiring structures with the relational 
	    bundles occurring in different types of multivariate networks. 'multiplex' provides also 
	    an algebraic approach for affiliation networks through Galois derivations between families 
	    of the pairs of subsets in the two domains of the network with visualization options.

Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.

Various utilities to manipulate multivariate polynomials.  The
    package is almost completely superceded by the 'spray' and 'mvp' packages,
    which are much more efficient.

Fits the Multiple Random Dot Product Graph Model and performs a test
	 for whether two networks come from the same distribution. Both methods are 
	 proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph
	 Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational
	 and Graphical Statistics).

Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap. See Keyel and Wiegand
    (2016) <doi:10.1111/2041-210X.12558> for more details.