FFD: Package to substantiate freedom from disease in R using two-stage sampling

In practice, when conducting surveys to substantiate freedom from disease in large populations, two-stage sampling strategies are often used in order to account for herdlevel clustering of diseases. Using a modified hypergeometric formula the optimal sample size can elegantly be computed, while incorporating imperfect diagnostic tests and finite populations; see Cameron and Baldock (1998a,b). In the package FFD, tools for calculating optimal sample sizes (on animal and herd level) using sampling strategies “individual sampling” or “limited sampling” (see Ziller, Selhorst, Teuffert, Kramer, and Schlüter (2002)), with the option of using risk-based targeted sampling are implemented. Further, cost optimal sampling strategies, while maintaining constant alpha (α)-levels can be computed using FFD. The package furthermore includes tools for evaluating the a-posteriori confidence (= 1−α) corresponding to a specific sample of herds. In order to provide both user friendliness and flexibility, a graphical user interface (GUI), S4-classes and conventional functions are made available.