Provides functions similar to the 'SAS' macros previously provided
to accompany Collins, Dziak, and Li (2009)
and Dziak, Nahum-Shani, and Collins (2012) , papers
which outline practical benefits and challenges of factorial
and fractional factorial experiments for scientists interested
in developing biological and/or behavioral interventions, especially
in the context of the multiphase optimization strategy
(see Collins, Kugler & Gwadz 2016) . The package
currently contains three functions. First, RelativeCosts1() draws a graph
of the relative cost of complete and reduced factorial designs versus
other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
which contains a list of random numbers that can be used to conveniently
assign participants to conditions in an experiment with
many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
size, or required sample size of a factorial or fractional factorial
experiment, for main effects or interactions, given several possible choices
of effect size metric, and allowing pretests and clustering.