A funcionality added to the Impute values tool for multiple imputation and maximum likelihood imputation of fields with missing at random will be very useful.
Missing data form a problem and advanced techniques are complicated. One great idea in statistics is multiple imputation,
filling the gaps in the data not with average, median, mode or user defined static values but instead with plausible values considering other fields.
SAS has PROC MI tool, here is a page detailing the usage with examples: http://www.ats.ucla.edu/stat/sas/seminars/missing_data/mi_new_1.htm
Also there is PROC CALIS for maximum likelihood here...
Same useful tool exists in spss as well http://www.appliedmissingdata.com/spss-multiple-imputation.pdf
Agreed that the current imputation tool does not do justice to the other predictive tools available in Alteryx.
In R, multiple packages exist. Among them: Amelia, MI, MICE. Thanks!
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