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A diamond open-access journal in statistics and machine learning A journal of the French Statistical Society (SFdS) — ISSN 2824-7795
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Computo promotes computational and algorithmic contributions in statistics and machine learning that provide insight into which models or methods are most appropriate for a given scientific question. Rather than sticking to classical static publications, Computo leverages literate programming and modern scientific-reporting tools to make every published article fully reproducible: code, data, and narrative live together, and readers can re-run the analysis behind the results.
Computo is a diamond open-access journal: free to read, free to publish in, with peer review handled openly through OpenReview.
If you are an author, start from the template matching your language (R, Python, or Julia) and follow the author guidelines. Reviewers can find the process described in the reviewer guidelines.
source of the organisation website
Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series
Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change
Geometric-Based Pruning Rules for Change Point Detection in Multiple Independent Time Series
Repo for the Computo paper entitled Should we correct the bias in Confidence Bands for Repeated Functional Data?
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