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New package: CHMMera v0.1.0 #119495
New package: CHMMera v0.1.0 #119495
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JuliaRegistrator
commented
Nov 15, 2024
- Registering package: CHMMera
- Repository: https://github.com/MurrellGroup/CHMMera.jl
- Created by: @AntonOresten
- Version: v0.1.0
- Commit: c153f2edac461eb455f26e1a8a2e7450c45de9fb
- Reviewed by: @AntonOresten
- Reference: MurrellGroup/CHMMera.jl@c153f2e#commitcomment-149129672
UUID: 4c20a592-b4de-41d1-8706-b02f346981aa Repo: https://github.com/MurrellGroup/CHMMera.jl.git Tree: 34332e6a90098a777dda43d520b8fc9203ed9f5b Registrator tree SHA: 17aec322677d9b81cdd6b9b9236b09a3f1374c6a
[noblock] Hi @MurrellGroup and @AntonOresten, congrats on the new package! Just so you know, I have developed a generic library for HMMs in Julia called HiddenMarkovModels.jl. Not sure it is useful to you but I just thought I'd mention it. |
Hello, I am an automated registration bot. I help manage the registration process by checking your registration against a set of AutoMerge guidelines. If all these guidelines are met, this pull request will be merged automatically, completing your registration. It is strongly recommended to follow the guidelines, since otherwise the pull request needs to be manually reviewed and merged by a human. 1. New package registrationPlease make sure that you have read the package naming guidelines. 2. AutoMerge Guidelines are all met! ✅Your new package registration met all of the guidelines for auto-merging and is scheduled to be merged when the mandatory waiting period (3 days) has elapsed. 3. To pause or stop registrationIf you want to prevent this pull request from being auto-merged, simply leave a comment. If you want to post a comment without blocking auto-merging, you must include the text Tip: You can edit blocking comments to add |
[noblock] cc @mchernys |
[noblock] Thanks @gdalle ! We focused on creating a lightweight application-specific package. The structure of our transition matrix allows our forward/backward/viterbi run in O(N * M) instead of O(N * (M^2)) and we have built in support for handling sequence errors in different ways. It would be possible to implement using HiddenMarkovModels.jl, but I think the amount of overloading we would have to do led us to stay independent. |
[noblock] |