An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) 
    and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for 
    multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes 
    are able to be calculated for either null model, and subsequently classified into one of 
    four different interaction classifications (e.g., antagonistic or synergistic interactions). 
    Analyses can be conducted on data for single experiments through to large meta-analytical datasets. 
    Minimal input (or statistical knowledge) is required, with any output easily understood. 
    Summary figures are also able to be easily generated.

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    package is almost completely superceded by the 'spray' and 'mvp' packages,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.