Thanks John! Another reason why I brought up the last question is the fear that following an R-style implementation will produce a Julia package that is riddled with sub-optimal legacy code. But I guess Julia is not mature enough that one could make such conclusions.
On Sunday, March 23, 2014 5:59:58 PM UTC-7, John Myles White wrote: > > Yes, including the same GPL-3 license is sufficient if you’ve derived your > work from a GPL-3 project. You may also need to include the original > headers of the files if they contain attribution information that you are > required to preserve. > > I don’t think there’s anything dishonest about creating a GPL-3 package. > If you would like to release something under a permissive license, you’ll > have to implement your code from scratch without ever reading any of the > code from a GPL or closed-source implementation. > > What’s most beneficial depends on context. Many businesses prohibit GPL > software, so many people in the Julia (and Python) communities > intentionally produce MIT or BSD software. But Julia benefits a lot from > having GPL packages when there’s no reasonable alternative. > > — John > > On Mar 23, 2014, at 1:17 PM, Ted Fujimoto <tftu...@gmail.com <javascript:>> > wrote: > > Hi all, > > I'm trying to familiarize myself with Julia by seeing how it compares to > other languages. I would also like to "open-source" my code if it seems > useful to others. Unfortunately, licenses have made this process > complicated. > > A tangible example: > > I am trying to implement a Julia version of the R package pcalg ( > http://cran.r-project.org/web/packages/pcalg/index.html). Like most R > packages, it is protected under the > GPL-3<http://cran.r-project.org/web/licenses/GPL-3> license. > Also, the license states that it would consider my implementation a > "modification" of the R package. Say I feel that my project is ready to be > open-sourced and put it in a github repository. Is it enough to follow the > RmathDist.jl <https://github.com/JuliaStats/RmathDist.jl> lead and do the > following?: > 1. Include the same license in the repository. > 2. Cite the R package I modified. > > A more long term question: I'm guessing a better (and more honest) > alternative to the above would be to implement the relevant algorithms by > looking at the pseudocode and applying it in a way that is friendlier to > future improvements using idiomatic Julia (if it exists yet). After that, > open-source it under the MIT license. Would this be a more beneficial > approach than the "Julia version of an R package" approach? > > >
