Given a matrices with observed and expected counts (baselines) for each timepoint (row) and location/data stream (column), sum counts and baselines cumulatively backwards in time, and compute the Poisson priority function value for each column and subset of time.
poisson_priority(counts, baselines, scalar_priority_fun = poisson_qmax)A matrix of observed counts. Rows represent timepoints, ordered from most recent to most distant. Columns represent e.g. locations or data streams.
A matrix of expected counts with the same dimensions as
counts.
A function taking two arguments c (count)
and b (baseline) and returning a scalar.
A matrix with the same dimensions as counts. The \(i\)th
element in each column contains the Poisson priority function value for
the window of time (duration) stretching from 1 to \(i\), for that
column.