  Many scientific questions rely on determining whether two sequences of event
times are associated. This article introduces a likelihood ratio test which can
be parameterised in several ways to detect different forms of dependence. A
common finite-sample distribution is derived, and shown to be asymptotically
related to a weighted Kolmogorov-Smirnov test. Analysis leading to these
results also motivates a more general tool for diagnosing dependence. The
methodology is demonstrated on data generated on an email network, showing
evidence of information flow using only timing information. Implementation code
is available in the R package `mppa'.
