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[PRE REVIEW]: arfpy: A Python package for adversarial random forests #5897
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Five most similar historical JOSS papers: forestatrisk: a Python package for modelling and forecasting deforestation in the tropics iRF: extracting interactions from random forests AgentPy: A package for agent-based modeling in Python ArviZ a unified library for exploratory analysis of Bayesian models in Python RandomForestsGLS: An R package for Random Forests for dependent data |
@editorialbot query scope @kristinblesch – thanks for your submission to JOSS. As this submission is rather small I'm going to ask the JOSS editorial team to check if this submission is in scope for us. This process might take a couple of weeks to complete. |
Submission flagged for editorial review. |
@kristinblesch - thanks for your submission to JOSS. Unfortunately, after review by the JOSS editorial team we've determined that this submission doesn't meet our substantial scholarly effort criterion. One possible alternative to JOSS is to follow GitHub's guide on how to create a permanent archive and DOI for your software. This DOI can then be used by others to cite your work. |
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Paper rejected. |
Submitting author: @kristinblesch (Kristin Blesch)
Repository: https://github.com/bips-hb/arfpy
Branch with paper.md (empty if default branch):
Version: v0.1.1
Editor: Pending
Reviewers: Pending
Managing EiC: Arfon Smith
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