  A regression method for proportional, or fractional, data with mixed effects
is outlined, designed for analysis of datasets in which the outcomes have
substantial weight at the bounds. In such cases a normal approximation is
particularly unsuitable as it can result in incorrect inference. To resolve
this problem, we employ a logistic regression model and then apply a bootstrap
method to correct conservative confidence intervals. This paper outlines the
theory of the method, and demonstrates its utility using simulated data.
Working code for the R platform is provided through the package glmmboot,
available on CRAN.
