Nothing
set.seed(); rpanet()
due to potential
cross-platform inconsistencies.directed
in rpanet()
into initial.network
;
rpanet(nstep = 1e4, initial.network = list(directed = TRUE))
.isolates
in clustcoef()
to accept binary input.distribution
, dparams
and shift
arguments from both
rpa_control_newedge()
and rpa_control_edgeweight()
; the new argument
sampler
accepts a function used for sampling the number of new edges and
edge weights.print.rpacontrol()
and summary.wdnet()
.binary
method due
to negligible performance gain.wdnet
objects.wdnet
and rpacontrol
objects.rpanet
.seednetwork
to initial.network
and changed seednetwork = NULL
to initial.network = list(edgelist = matrix(c(1, 2), nrow = 1))
;control = NULL
to control = list()
;naive
to linear
; nodelist
to bag
; edgesampler
to bagx
;Updated returns, put node strength and preference scores into a data frame.
Sort nodes from the seed network according to their preference scores before the sampling process.
rpanet
control functions: rpactl.foo()
to rpa_control_foo()
.cvxr.control()
to cvxr_control()
.dprewire
and dprewire.range
.eta
and corresponding assortativity levels.CVXR
.rpactl.preference
.rpanet
with binary
approach: renamed node structures (fix LTO issues).Any scripts or data that you put into this service are public.
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