Package: exposureEM 0.3.0

exposureEM: Combined-Exposure Models by EM and Marquardt Optimization

Fits general two-component combined-exposure models for binary event histories when the event setting is not observed. The observed binary event is represented as the union of two latent component-specific binary events. Known exposure proportions enter as offsets. Parameters can be estimated by expectation-maximization, direct Marquardt-damped Newton-Raphson maximization of the observed likelihood, or a hybrid that uses several expectation-maximization iterations before direct optimization. Uncertainty is estimated with Louis' formula for the expectation-maximization estimator and the inverse observed Hessian for direct and hybrid fits. Complementary log-log, logit, and log component links are available for all three estimation methods.

Authors:Wenjing Meng [aut, cre]