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title: "NEWS" output: html_document
pb()
and sdsm()
igraph 1.4.0
sparsify()
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 = TRUE
igraph
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 NAAny scripts or data that you put into this service are public.
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