make_dyads: Aggregate events to a regular time interval
In conjugateprior/events: Store and Manipulate Event Data

Description Usage Arguments Details Value Author(s)

Aggregates events to a regular time interval

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make_dyads(edo, scale = NULL, unit = "week", monday = TRUE,
  fun = function(x) {     mean(x, na.rm = TRUE) }, missing.data = NA,
  actors = NULL)

`edo`	Event data
`scale`	Name of an eventscale or `NULL` to create counts
`unit`	Temporal aggregation unit
`monday`	Whether weeks start on Monday. If `FALSE`, they start on Sunday
`fun`	Aggregation function. Should take a vector and return a scalar
`missing.data`	What weeks with no data are assigned
`actors`	An array of actor names from which to construct dyads.

In an event data set S, assume that A=length(actors(S)) actors K=length(codes(S)) event codes occur. This function creates (by default) A^2 data streams labelled by the combination of source and target actors. If scale is NULL these are K-dimensional time series of event counts. If scale names a scale that has been added to the event data fun is used to aggregate the events falling into each temporal interval. This creates a evenly spaced univariate interval-valued time series, possibly with missing data, for each directed dyad.