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‎site/mock-papers.yml‎

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}
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pdf: ''
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url: http://computo-journal.org/published-paper-tsne/
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url: https://computo-journal.org/published-paper-tsne/
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status: published
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- title: Visualizing Data using t-SNE (mock contribution)
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name: published-paper-tsne-R
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title = {Visualizing {Data} Using {t-SNE} (Mock Contribution)},
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journal = {Computo},
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date = {2008-08-11},
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url = {https://computo-journal.org/published-paper-tsne/},
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url = {https://computo-journal.org/published-paper-tsne-R/},
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doi = {10.57750/xxx-xxx},
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issn = {2824-7795},
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langid = {en},
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}
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pdf: ''
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url: http://computo-journal.org/published-paper-tsne-R/
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url: https://computo-journal.org/published-paper-tsne-R/
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status: published

‎site/published.xml‎

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</item>
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<item>
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<title>Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series - Thomas Ferté, Kalidou Ba, Dan Dutartre, Pierrick Legrand, Vianney Jouhet, Rodolphe Thiébaut, Xavier Hinaut and Boris P Hejblum</title>
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<link>http://computo-journal.org/published-202505-ferte-reservoirnet/</link>
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<guid>http://computo-journal.org/published-202505-ferte-reservoirnet/</guid>
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<link>https://computo-journal.org/published-202505-ferte-reservoirnet/</link>
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<guid>https://computo-journal.org/published-202505-ferte-reservoirnet/</guid>
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<pubDate>Fri, 27 Jun 2025 00:00:00 GMT</pubDate>
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<description>Reservoir Computing (RC) is a machine learning method
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based on neural networks that efficiently process information
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</item>
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<item>
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<title>Efficient simulation of individual-based population models - Daphné Giorgi, Sarah Kaakai and Vincent Lemaire</title>
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<link>http://computo-journal.org/published-202412-giorgi-efficient/</link>
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<guid>http://computo-journal.org/published-202412-giorgi-efficient/</guid>
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<link>https://computo-journal.org/published-202412-giorgi-efficient/</link>
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<guid>https://computo-journal.org/published-202412-giorgi-efficient/</guid>
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<pubDate>Mon, 27 Jan 2025 00:00:00 GMT</pubDate>
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<description>The `R` Package `IBMPopSim` facilitates the simulation of
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the random evolution of heterogeneous populations using stochastic

‎site/published.yml‎

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}
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pdf: ''
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url: http://computo-journal.org/published-202505-ferte-reservoirnet/
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url: https://computo-journal.org/published-202505-ferte-reservoirnet/
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status: published
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- title: Efficient simulation of individual-based population models
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name: published-202412-giorgi-efficient
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doi: 10.57750/sfxn-1t05
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year: 2025
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date: 2025-01-27
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description: ''
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description: >
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This document provides a full description of the Stochastic Individual-Based Models (IBMs) that can be implemented in the IBMPopSim package. A unified mathematical and simulation framework is given, with a detailed description of the simulation algorithm. Examples of applications for the package are also provided, showing the performance and flexibility of IBMPopSim.
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abstract: >-
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The `R` Package `IBMPopSim` facilitates the simulation of
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the random evolution of heterogeneous populations using stochastic
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}
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pdf: ''
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url: http://computo-journal.org/published-202412-giorgi-efficient/
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url: https://computo-journal.org/published-202412-giorgi-efficient/
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status: published
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- title: Spectral Bridges
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name: published-202412-ambroise-spectral
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}
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pdf: ''
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url: http://computo-journal.org/published-202412-ambroise-spectral/
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url: https://computo-journal.org/published-202412-ambroise-spectral/
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status: published
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- title: 'AdaptiveConformal: An `R` Package for Adaptive Conformal Inference'
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name: published-202407-susmann-adaptive-conformal
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}
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pdf: ''
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url: http://computo-journal.org/published-202407-susmann-adaptive-conformal/
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url: https://computo-journal.org/published-202407-susmann-adaptive-conformal/
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status: published
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- title: Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change
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name: published-202407-legrand-wildfires
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bibtex: >+
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@article{legrand2024,
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author = {Legrand, Juliette and Pimont, François and Dupuy, Jean-Luc
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and Opitz, Thomas and Pimont, François and Dupuy, Jean-Luc and
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Opitz, Thomas},
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and Opitz, Thomas},
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publisher = {French Statistical Society},
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title = {Bayesian Spatiotemporal Modelling of Wildfire Occurrences and
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Sizes for Projections Under Climate Change},
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}
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pdf: ''
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url: http://computo-journal.org/published-202407-legrand-wildfires/
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url: https://computo-journal.org/published-202407-legrand-wildfires/
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status: published
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- title: Geometric-Based Pruning Rules for Change Point Detection in Multiple Independent Time Series
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name: published-202406-pishchagina-change-point
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}
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pdf: ''
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url: http://computo-journal.org/published-202406-pishchagina-change-point/
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url: https://computo-journal.org/published-202406-pishchagina-change-point/
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status: published
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- title: 'Peerannot: classification for crowdsourced image datasets with Python'
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name: published-202402-lefort-peerannot
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doi: 10.57750/qmaz-gr91
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year: 2024
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date: 2024-05-07
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description: ''
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description: >
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Crowdsourcing is a quick and easy way to collect labels for large datasets, involving many workers.
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However, it is common for workers to disagree with each other.
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Sources of error can arise from the workers' skills, but also from the intrinsic difficulty of the task.
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We introduce `peerannot`, a Python library for managing and learning from crowdsourced labels of image classification tasks.
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abstract: >-
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Crowdsourcing is a quick and easy way to collect labels
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for large datasets, involving many workers. However, workers often
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}
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pdf: ''
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url: http://computo-journal.org/published-202402-lefort-peerannot/
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url: https://computo-journal.org/published-202402-lefort-peerannot/
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status: published
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- title: Optimal projection for parametric importance sampling in high dimensions
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name: published-202402-elmasri-optimal
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}
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pdf: ''
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url: http://computo-journal.org/published-202402-elmasri-optimal/
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url: https://computo-journal.org/published-202402-elmasri-optimal/
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status: published
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- title: Point Process Discrimination According to Repulsion
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name: published-202401-adrat-repulsion
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repo: published-202312-favrot-hierarchical
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bibtex: >+
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@article{favrot2024,
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author = {Favrot, Armand and Makowski, David and Makowski, David},
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author = {Favrot, Armand and Makowski, David},
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publisher = {French Statistical Society},
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title = {A Hierarchical Model to Evaluate Pest Treatments from
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Prevalence and Intensity Data},
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}
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pdf: ''
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url: http://computo-journal.org/published-202312-cleynen-local/
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url: https://computo-journal.org/published-202312-cleynen-local/
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status: published
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- title: Computing an empirical Fisher information matrix estimate in latent variable models through stochastic approximation
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name: published-202311-delattre-fim
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}
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pdf: ''
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url: http://computo-journal.org/published-202311-delattre-fim/
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url: https://computo-journal.org/published-202311-delattre-fim/
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status: published
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- title: Inference of Multiscale Gaussian Graphical Models
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name: published-202306-sanou-multiscale_glasso
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name: published-202306-sanou-multiscale-glasso
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authors: Edmond Sanou, Christophe Ambroise and Geneviève Robin
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journal: Computo
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doi: 10.57750/1f4p-7955
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performances of our model to state-of-the-art clustering methods and
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network inference models. Applications to gut microbiome data and
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poplar’s methylation mixed with transcriptomic data are presented.
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repo: published-202306-sanou-multiscale_glasso
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repo: published-202306-sanou-multiscale-glasso
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bibtex: >+
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@article{sanou2023,
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author = {Sanou, Edmond and Ambroise, Christophe and Robin, Geneviève},
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publisher = {French Statistical Society},
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title = {Inference of {Multiscale} {Gaussian} {Graphical} {Models}},
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journal = {Computo},
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date = {2023-06-28},
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url = {https://computo-journal.org/published-202306-sanou-multiscale_glasso/},
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url = {https://computo-journal.org/published-202306-sanou-multiscale-glasso/},
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doi = {10.57750/1f4p-7955},
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issn = {2824-7795},
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langid = {en},
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}
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pdf: ''
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url: http://computo-journal.org/published-202306-sanou-multiscale_glasso/
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url: https://computo-journal.org/published-202306-sanou-multiscale-glasso/
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status: published
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- title: 'Macrolitter video counting on riverbanks using state space models and moving cameras '
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name: published-202301-chagneux-macrolitter
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}
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pdf: ''
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url: http://computo-journal.org/published-202301-chagneux-macrolitter/
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url: https://computo-journal.org/published-202301-chagneux-macrolitter/
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status: published
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- title: 'A Python Package for Sampling from Copulae: clayton'
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name: published-202301-boulin-clayton
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}
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pdf: ''
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url: http://computo-journal.org/published-202301-boulin-clayton/
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url: https://computo-journal.org/published-202301-boulin-clayton/
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status: published
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- title: Trade-off between deep learning for species identification and inference about predator-prey co-occurrence
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name: published-202204-deeplearning-occupancy-lynx
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@article{gimenez2022,
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author = {Gimenez, Olivier and Kervellec, Maëlis and Fanjul,
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Jean-Baptiste and Chaine, Anna and Marescot, Lucile and Bollet,
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Yoann and Duchamp, Christophe and Gimenez, Olivier and Kervellec,
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Maëlis and Marescot, Lucile},
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Yoann and Duchamp, Christophe},
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publisher = {French Statistical Society},
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title = {Trade-Off Between Deep Learning for Species Identification
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and Inference about Predator-Prey Co-Occurrence},

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