print(), summary()
and plot() functions for backbone objects%*% with
(t)crossprod()igraph objectbackbone() wrapper function for all input network
types.fixedfill() null modelbackbone_only = FALSE returns a
backbone-class object that can be described using
print()igraph
objectsigraphigraph functionssdsm() via
the logit() functionsparsify()mlf()lans()missing.as.zero option to statistical modelspb() and sdsm()igraph 1.4.0sparsify() and all statistical backbone
functionshyperg() as alternate name for
fixedrow(), eliminated universal() as
alternate name for global()testthat with tinytest; expanded
unit testsPoissonBinomial;
sdsm() and fixedcol() now use an efficient
implementation of the Refined Normal Approximation in base RMASS; osdsm() now
uses glm() in base R to implement the conditional logistic
regression method described by Neal (2017)network and support for
network objects, which can easily be converted to matrix
objectsbipartite.from.probability(),
bipartite.from.sequence(),
bipartite.from.distribution(), and
bipartite.add.blocks(). These are now part of the
incidentally packagebicm()narrative = TRUEigraph object
with vertex attributes, the attributes are preserved in the
backbonefastball() so it will work with R <
4.1.0fastball() so it will work with R <
4.1.0fastball() algorithmalpha = 0.05 as default in all statistical
modelsfwer (familywise error rate) parameter as
mtc (multiple test correction)davis example data; add examples using synthetic
datasparsify()osdsm()disparity()fastball() algorithm for
marginal-preserving matrix randomizationtestthat testsbackbone.extract() functionp.adjust() method of correcting for
familywise error ratestestthat tests due to unknown MKL error; will
be restored in a future versionpositive and negative
backbone object matrices to NA
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