I believe that adding new methods in statistical packages, to the point that each package now offers hundreds of functions (dozens of regressions, dozens of classifiers, dozens of time series methods and so on), is a bad idea. Most of these functions are never used. It only confuses the high-level user, and makes these packages not suitable for automated or black-box data science by non-statisticians (engineers, economists). If you really need that level of sophistication and fine-tuning, you are better off writing your own code in Perl, Python, or R or some other programming language. Dr Granville is currently working on a new approach to statistical software development.

I believe that adding new methods in statistical packages, to the point that each package now offers hundreds of functions (dozens of regressions, dozens of classifiers, dozens of time series methods and so on), is a bad idea. Most of these functions are never used. It only confuses the high-level user, and makes these packages not suitable for automated or black-box data science by non-statisticians (engineers, economists). If you really need that level of sophistication and fine-tuning, you are better off writing your own code in Perl, Python, or R or some other programming language. Dr Granville is currently working on a new approach to statistical software development. It consists of producing very few, global methods with few parameters (one method per core problem, e.g. one generic clustering technique, one generic regression technique etc.) with focus on automation (algorithms run in batch mode and/or automatically scheduled), streaming data, black-box data processing by non-statisticians, and ability to process large data while avoiding the curse of big data at the same time.