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Combine efforts from marfx? #31

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jrnold opened this issue Aug 5, 2016 · 5 comments
Closed

Combine efforts from marfx? #31

jrnold opened this issue Aug 5, 2016 · 5 comments
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@jrnold jrnold commented Aug 5, 2016

I'm opening this up to follow-up on our twitter conversation. My package marfx replicates some of this package. I don't have time to go into detail now, but follow up later when I have more time to write my thoughts and how the two packages differ and how we can build on the strengths of each. But in the meantime, I wanted to open up this issue as a place for discussing this.

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@leeper leeper commented Aug 5, 2016

Thanks for starting this. I'll give your package a look once I have a moment and see how easily we can combine.

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@jrnold jrnold commented Aug 5, 2016

I need to look back over your package as well. I looked at it prior to writing marfx and it was the only package that was good, but I think I had a few different ideas about the API. I'll look again and write up my thoughts.

@leeper leeper added the question label Aug 6, 2016
@leeper leeper modified the milestone: CRAN Release Aug 23, 2016
@leeper leeper added enhancement and removed question labels Sep 6, 2016
@leeper leeper self-assigned this Sep 6, 2016
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@leeper leeper commented Sep 6, 2016

TODO:

  • Improve the value of eps
  • Switch to central difference approximation method (f(x+h) - f(x - h))/2h
  • Optionally use simulation approach from marfx
  • Expose internal calculators with better names a la marfx
  • identify "logical" class variables and handle as factor
  • handle "ordered" class variables as factor
  • Add ggplot2 examples; ensure all output is tidy (it should be, but need to check)
  • Add example to README of calling mfx() directly
  • Add example data beyond datasets (prob. examples from marfx vignette)
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@leeper leeper commented Oct 31, 2016

@jrnold I think I've effectively incorporated the core functionality of your package, including the simulation-based variance estimation and the addition of a low-level dydx() generic to calculate marginal effects or discrete differences for a single variable in a model. I'm finishing up the vignette explaining the computational approach and when that's done, I plan to push to CRAN. I've credited you as an author, given some of the code is from your package.

@leeper leeper closed this Oct 31, 2016
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@jrnold jrnold commented Oct 31, 2016

Awesome. Sorry I haven't had time to look at it. As I'm prepping/using it for my upcoming classes I'll see what I can add/contribute.

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