c(person('Anthony','Hennessey',email='anthony.hennessey@nottingham.ac.uk',role=c('aut','cre')),
person('Viola','Wiegand',email='v.wiegand@bham.ac.uk',role='aut'),
person('Michaela','Mahlberg',email='m.a.mahlberg@bham.ac.uk',role='aut'),
person('Christopher R.','Tench',email='christopher.tench@nottingham.ac.uk',role='aut'),
person('Jamie','Lentin',email='jm@ravingmantis.com',role='aut'))
Anthony Hennessey [aut, cre],
Viola Wiegand [aut],
Michaela Mahlberg [aut],
Christopher R. Tench [aut],
Jamie Lentin [aut]

c(person('Anthony','Hennessey',email='anthony.hennessey@nottingham.ac.uk',role=c('aut','cre')),
person('Viola','Wiegand',email='v.wiegand@bham.ac.uk',role='aut'),
person('Michaela','Mahlberg',email='m.a.mahlberg@bham.ac.uk',role='aut'),
person('Christopher R.','Tench',email='christopher.tench@nottingham.ac.uk',role='aut'),
person('Jamie','Lentin',email='jamie.lentin@shuttlethread.com',role='aut'))

c(person('Anthony','Hennessey',role='aut'),
person('Viola','Wiegand',email='v.wiegand@bham.ac.uk',role='aut'),
person('Christopher R.','Tench',email='christopher.tench@nottingham.ac.uk',role='aut'),
person('Jamie','Lentin',email='jamie.lentin@shuttlethread.com',role='aut'),
person('Mike','Allaway',email='m.j.allaway@bham.ac.uk',role='ctb'),
person('Andrew','Edmondson',email='a.c.edmondson@bham.ac.uk',role='ctb'),
person('Matthew','Henderson',email='matthew.james.henderson@gmail.com',role='ctb'),
person('Michaela','Mahlberg',email='mahlberg.lab@gmail.com',role=c('aut','cre')))
Anthony Hennessey [aut],
Viola Wiegand [aut],
Christopher R. Tench [aut],
Jamie Lentin [aut],
Mike Allaway [ctb],
Andrew Edmondson [ctb],
Matthew Henderson [ctb],
Michaela Mahlberg [aut, cre]
Identifies significant difference in co-occurrence counts for a given node or set of nodes across two corpora, using a Fisher’s Exact test.
methods, stats, data.table (>= 1.11.2), stringi, RColorBrewer,
rlist
unittest, R.rsp, knitr, rmarkdown, lintr, testthat (>=
3.0.0), markdown

Nonparametrics with clustered binary data
This package implements non-parametric analyses for
clustered binary data. The elements of the cluster are assumed
exchangeable, and identical joint distribution (also known as
marginal compatibility, or reproducibility) is assumed for
clusters of different sizes. A trend test based on stochastic
ordering is implemented.