Phil suggested we discuss the relationship of the two least squares
packages in [math]
Current Status
Currently both fitting.leastsquares and stat.regression have least
squares implementations. The fitting.leastsquares package supports
nonlinear least squares with weights, pluggable optimization
algorithms, and (soon) data editing. The package is written from an
optimization/engineer's perspective.
As far as I can tell the stat.regression implementations are for linear
least squares. Some of the implementations contain neat optimizations,
for example requiring O(1) space for n data points. This package seems
to be written more from a statistician/economist's perspective.
Options
1. Keep separate packages.
2. delegate the implementation from one package to the other
3. merge into a single package. (could lead to some interesting
algorithms. e.g. nonlinear general least squares)
Phis, please add any important points I've missed.
Best Regards,
Evan
>
> Phil Steitz commented on MATH1105:
> 
>
> Might be better to take this discussion to the ML. We now have two
> least squares impls  one in fitting/leastsquares and another in
> stats.regression (actually this has been true for some time). The
> stats side of it (residual analysis, ANOVA, etc.) belongs more
> naturally in stats.regression. It might make more sense to add this
> functionality there. Or maybe we just refactor to have the
> stats.regression classes use the impl in leastsquares. In any case, we
> should discuss on the ML.
>
>>
>> Least squares statistical data editing
>> 
>>
>> Key: MATH1105
>> URL: https://issues.apache.org/jira/browse/MATH1105
>> Project: Commons Math
>> Issue Type: Improvement
>> Reporter: Evan Ward
>> Attachments: 0001Addstatisticaleditingcapability.patch,
>> 0002IntegratedataeditingwiththeLSframework.patch
>>
>

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