Single-molecule techniques have emerged as incisive approaches for addressing a wide range of questions arising in contemporary biological research [Trends Biochem Sci 38:30-37, 2013, Nat Rev Genet 14:9-22, 2013, Curr Opin Struct Biol 2014, 28C:112-121, Annu Rev Biophys 43:19-39, 2014]. The analysis and interpretation of raw single-molecule data benefits greatly from the ongoing development of sophisticated statistical analysis tools that enable accurate inference at the low signal-to-noise ratios frequently associated with these measurements. While a number of groups have released analysis toolkits as open source software [J Phys Chem B 114:5386-5403, 2010, Biophys J 79:1915-1927, 2000, Biophys J 91:1941-1951, 2006, Biophys J 79:1928-1944, 2000, Biophys J 86:4015-4029, 2004, Biophys J 97:3196-3205, 2009, PLoS One 7:e30024, 2012, BMC Bioinformatics 288 11(8):S2, 2010, Biophys J 106:1327-1337, 2014, Proc Int Conf Mach Learn 28:361-369, 2013], it remains difficult to compare analysis for experiments performed in different labs due to a lack of standardization.