Open AlxndrMlk opened 1 year ago
Hi @AlxndrMlk ,
Thank you for the suggestion! New algorithms to benchmark against are always welcome.
We are making an initial investigation into the efforts required to add an implementation of the algorithm. Due to various work arrangements, I can't really give an ETA on when/if the algorithm will be added but we are looking into it.
Hi @shaido987
Thank you for the update! If you're open for collaborations, I am happy to help with the implementation H1 next year (~Feb/Mar/Apr).
Great! You are more than welcome to help add it if you have time. You can give me a ping here later if you decide to work on it and I can make sure no one started implementing it already.
Great! Let's do a check in around Feb 20, 2023.
Hi @shaido987
I needed to update my plans as I am finishing writing my book. As a consequence, I won't be able to start working on this earlier than in May.
I'll keep you posted and in case someone else wants to take it earlier - can you let me know here so we can make sure not to double the work?
Hi @AlxndrMlk
Sure, no problems at all. I will let you know if there are any updates. All the best with finishing the book!!
Thank you @shaido987, I appreciate it!
Hi!
I'd like to propose to add NPVAR algorithm (Gao et al., 2020) to the package. I believe that this would facilitate benchmarking and further research in the field.
There is an existing R implementation provided by the authors.
What are your thoughts on this idea?
References
Gao, M., Ding, Y., Aragam, B. (2020). A polynomial-time algorithm for learning nonparametric causal graphs.