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Provides functions similar to the 'SAS' macros previously provided to accompany Collins, Dziak, and Li (2009) doi:10.1037/a0015826 and Dziak, Nahum-Shani, and Collins (2012) doi:10.1037/a0026972, 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) doi:10.1007/s10461-015-1145-4. 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.

Last 30 days

This package has been downloaded 207 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 11 times.

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Last 365 days

This package has been downloaded 2,849 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 34 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.