trinomial.audit converts the audited total counts for candidates to
overstatements and taints. trinomial.bound calculates the trinomial
bound given the size of an audit sample, the number of non-zero errors, and
the size of the small-error threshold. It can also plot a contour of the
distribution space, bounds, and alpha lines.
trinomial.audit(Z, audit)An elec.data object that is the race being audited.
A data.frame with a column for each candidate and a row for
each audited precinct, holding the audit totals for each candidate. An
additional column, count, holds the number of times that precinct was
sampled (since sampling was done by replacement).
Right now the p-value is computed in a clumsy, bad way. A grid of points over (0, xlim) X (0, ylim) is generated corresponding to values of p0 and pd, and for each point the mean of that distribution and the chance of generating an outcome as extreme as k is calculated. Then the set of points with an outcome close to alpha is extrated, and the corresponding bound is optimized over this subset. Not the best way to do things.