accum.interact(elist, old.acl=NULL)acl.adj(acl, iter, src, dest)acl.adjmat(acl, n, iter)acl.deg(acl, n, cmode=c("in","out","total"), old.deg=NULL)accum.ps(elist)acl.ps(elist, n, old.ps=NULL)acl.tri(acl, old.tri=NULL)accum.rrl(elist, old.rrl=NULL)covarPrep(covar, n, m, effects=NULL)
Details
Most of these are not to be called by the user; they can be employed by the cogniscenti, but they may change without notice (so use at own risk).
Arguments
elist: a three-column (time, source, destination) dyadic event list, sorted in ascending temporal order.
acl: a nested list structure, of the form iteration by ego by alter, containing accumulated dyadic event counts at each event onset.
iter: iteration (i.e., event) number.
src: integer denoting the sender of a dyadic event.
dest: integer denoting the receiver of a dyadic event.
n: the number of actors eligible to send/receive events.
cmode: the type of degree to be calculated.
old.acl: previously computed acl structure to which new events should be added.
old.deg: previously computed cumulative degree structure to which events should be added.
old.ps: previously computed P-shift structure to which events should be added.
old.tri: previously computed triad structure to which events should be added.
old.rrl: previously computed recency structure to which events should be added.
covar: a covariate list, of the form passed normally to rem.dyad.
m: the intended number of events for which data should be checked.
effects: a logical effect inclusion vector of the type used internally by rem.dyad.