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zmjones / edarf

exploratory data analysis using random forests
MIT License
68 stars 11 forks source link

link update #11

Closed christophergandrud closed 9 years ago

christophergandrud commented 9 years ago

Noticed that the previous link was-404. Not sure, but I think the corrected one is the most relevant.

zmjones commented 9 years ago

ah yep, need to change the title too, though it is just a working title. thanks

christophergandrud commented 9 years ago

(almost didn't make the pull request because its really minor).

Looking forward to this work. It's really interesting.

zmjones commented 9 years ago

not too minor imo. the package is sort of a minor contribution imo but hopefully will be practically helpful. it will be rewritten pretty substantially when @flinder and I get the time.

christophergandrud commented 9 years ago

"the package is sort of a minor contribution"

Maybe, but methods papers that don't implement their methods can sometimes feel half-written.

Nagging thoughts I sometimes have: if the author didn't implement it in an R package, for example, does that mean that they don't plan to actually use the method in their research? And if so, why should anyone else invest time to learn how to use it?

zmjones commented 9 years ago

i certainly agree with that.

On Tue, Jan 20, 2015 at 12:03 PM, Christopher Gandrud < notifications@github.com> wrote:

"the package is sort of a minor contribution"

Maybe, but methods papers that don't implement their methods can sometimes feel half-written.

Nagging thoughts I sometimes have: if the author didn't implement it in an R package, for example, does that mean that they don't plan to actually use the method in their research? And if so, why should anyone else invest time to learn how to use it?

— Reply to this email directly or view it on GitHub https://github.com/zmjones/edarf/pull/11#issuecomment-70690815.

Zachary M. Jones zmjones.com