The Tanimoto coefficient is the most widely used similarity metric in cheminformatics.

important to know how many subjects are at risk at each major time interval. I have to look this up in the table and add it manually for each group being analyzed.

Looking for a functions that performs Tukey's transformation (as it does in r but for larger than 5000 data sets) to identify the best transformation to normalize data for parametric analyses. This would eliminate the guesswork and save everyone t...

L'Abbé plot can be useful to assess heterogeneity in a meta-analysis Funnel plot can be useful to assess for bias in a meta-analysis

Logistic C(oncordance) statistics (ROC analysis) are classically used to evaluate diagnostic performance. Harrell's C Statistics and Somers'D are concordance statistics adapted to time-dependent models like survival Cox models. They are useful to ...

In order to report proper complete statistics on research papers, one needs the values of t and the number of degrees of freedom in AUC multiple comparison tests within the ROC curve analysis feature (the p-value is not enough).

It would be really useful to make the permuted clustering table that is used to construct heat maps available. Currently the heat map gives you the list of genes as labels of the heatmap graph but this doesn't allow the user to manipulate for furt...

XLSTAT has a few similarity coefficients, but data must be in binary format. XLSTAT will do the conversion, but this adds an extra step to the calculation. There are Python packages that will compute similarity coefficients on sets, strings, and s...

XLSTAT now has the Rogers & Tanimoto coefficient, but it lacks the Tanimoto coefficient, which is the most widely use similarity coefficient in cheminformatics and some other fields.