Algebraic Tools for the Analysis of Multiple Social Networks

Algebraic procedures for the analysis of multiple social networks are delivered with
this package. Among other things, it makes possible to create and manipulate multivariate
network data with different formats, and there are effective ways available to treat multiple
networks with routines that combine algebraic systems like the partially ordered semigroup or
the semiring structure together with the relational bundles occurring in different types of
multivariate network data sets. It also provides an algebraic approach for two-mode networks
through Galois derivations between families of the pairs of subsets in the two domains.

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

Data Manipulation with Parallelism and Shared Memory Matrices
Provides a new form of data frame backed by shared memory matrices and a way to manipulate them. Upon creation these data frames are shared across multiple local nodes to allow for simple parallel processing.
c(person(given="Jim", family="Blundell", role = c("aut", "cre", "cph"), email = "james.blundell@keble.ox.ac.uk"))
bigmemory (>= 4.5.19), bigmemory.sri (>= 0.1.3), lazyeval (>=
0.1.10), parallel, methods

Provides a new form of data frame backed by shared memory matrices
and a way to manipulate them. Upon creation these data frames are shared across
multiple local nodes to allow for simple parallel processing.
c(person(given="Jim", family="Blundell", role = c("aut", "cre", "cph"), email = "jeblundell@doctors.org.uk"))
bigmemory (>= 4.5.19), bigmemory.sri (>= 0.1.3), parallel,
methods