Data-adaptive test statistics represent a general methodology for performing multiple hypothesis testing on effects sizes while maintaining honest statistical inference when operating in high-dimensional settings. The utilities provided here extend the use of this general methodology to many common data analytic challenges that arise in modern computational and genomic biology.