Statistically consistent way to handle E(known outcomes) < deaths

The current implementation in CFR is based on calculating E(known outcomes) to compare to totals deaths. However, in extreme examples, such as small outbreaks with a very high CFR (like Ebola in Yambuku in 1976), there can be occasionally situations where E(known outcomes) < deaths and hence the binomial likelihood calculation is not valid. In this situation the code currently returns NA to make the problem clear to the user.

In the longer-term, a more statistically consistent approach would be to integrate over the possible known outcomes, rather than just using the expectation. This would allow calculation on the plausible known outcomes < deaths and automatic omission of known outcomes > deaths. Something like the following:
$E(CFR) = \sum_i P(\text{i known outcomes so far | cases, deaths}) E(\text{CFR | i known outcomes so far}) $

Start by reading the current CFR implementation and trace how E(known outcomes) is compared with deaths before the binomial likelihood is calculated. Work through the Ebola-in-Yambuku example and define how integrating over possible known outcomes should handle values below and above deaths. Done means the method remains statistically valid in these edge cases and no longer returns NA solely because the expected known outcomes are below deaths.

Written by the indexing model from the issue text.