Simulating and Modeling Group (Pooled) Testing Data // Provides
an expectation-maximization (EM) algorithm using the approach
introduced in Xie (2001) doi:10.1002/sim.817. The EM algorithm
can be used to estimate the prevalence (overall proportion) of
a disease and to estimate a binary regression model from among
the class of generalized linear models based on group testing
data. The estimation framework we consider offers a flexible
and general approach; i.e., its application is not limited to
any specific group testing protocol. Consequently, the EM
algorithm can model data arising from simple pooling as well as
advanced pooling such as hierarchical testing, array testing,
and quality control pooling. Also, provided are functions that
can be used to conduct the Wald tests described in Buse (1982)
doi:10.1080/00031305.1982.10482817 and to simulate the group
testing data described in Kim et al. (2007)
doi:10.1111/j.1541-0420.2007.00817.x.