This machine learning technique is a new form of PCA that allows for determination of the set of variables that best explain two groups.https://arxiv.org/abs/1709.06716

I was hoping to use your program to reproduce figures like these that were original produced by http://www.biosoft.hacettepe.edu.tr/easyROC/ using data like the .txt files provided. Also need to provide parametric (curved) option. If one already e...

Number at risk is calculated in the table generated in the Kaplan-Meier analysis. Put the number at risk below the chart at the selected time intervals

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.

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

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...

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).