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diff --git a/Gemfile b/Gemfile
deleted file mode 100644
index 6cdddfe4..00000000
--- a/Gemfile
+++ /dev/null
@@ -1,24 +0,0 @@
-source 'https://rubygems.org'
-group :jekyll_plugins do
- gem 'jekyll'
- gem 'jekyll-archives'
- gem 'jekyll-diagrams'
- gem 'jekyll-email-protect'
- gem 'jekyll-feed'
- gem 'jekyll-imagemagick'
- gem 'jekyll-minifier'
- gem 'jekyll-paginate-v2'
- gem 'jekyll-scholar'
- gem 'jekyll-sitemap'
- gem 'jekyll-link-attributes'
- gem 'jekyll-twitter-plugin'
- gem 'jemoji'
- gem 'mini_racer'
- gem 'unicode_utils'
- gem 'webrick'
- gem 'kramdown-parser-gfm'
-end
-group :other_plugins do
- gem 'httparty'
- gem 'feedjira'
-end
diff --git a/_bibliography/in_production.bib b/_bibliography/in_production.bib
deleted file mode 100644
index a16b67f3..00000000
--- a/_bibliography/in_production.bib
+++ /dev/null
@@ -1,104 +0,0 @@
-@article{susmann2024,
- bibtex_show = {true},
- author = {Susmann, Herbert and and Chambaz, Antoine and Josse, Julie},
- publisher = {French Statistical Society},
- title = {{AdaptiveConformal: An R Package for Adaptive Conformal Inference}},
- journal = {Computo},
- year = 2024,
- url = {https://computo.sfds.asso.fr/published-202407-susmann-adaptive-conformal},
- doi = {10.57750/edan-5f53},
- type = {{Research article}},
- domain = {Statistics},
- language = {R},
- repository = {published-202407-susmann-adaptive-conformal},
- langid = {en},
- abstract = {Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adaptive Conformal Inference (ACI) algorithms extend CI to the case of sequentially observed data, such as time series, and exhibit strong theoretical guarantees without having to assume exchangeability of the observed data. The common thread that unites algorithms in the ACI family is that they adaptively adjust the width of the generated prediction intervals in response to the observed data. We provide a detailed description of five ACI algorithms and their theoretical guarantees, and test their performance in simulation studies. We then present a case study of producing prediction intervals for influenza incidence in the United States based on black-box point forecasts. Implementations of all the algorithms are released as an open-source R package, AdaptiveConformal, which also includes tools for visualizing and summarizing conformal prediction intervals.}
-}
-
-
-@article{pishchagina2024,
- bibtex_show = {true},
- author = {Pishchagina, Liudmila and Rigaill, Guillem and Runge,
- Vincent},
- publisher = {French Statistical Society},
- title = {Geometric-Based {Pruning} {Rules} for {Change} {Point}
- {Detection} in {Multiple} {Independent} {Time} {Series}},
- journal = {Computo},
- year = 2024,
- url = {https://computo.sfds.asso.fr/published-202406-pishchagina-change-point/},
- doi = {10.57750/9vvx-eq57},
- issn = {2824-7795},
- type = {{Research article}},
- domain = {Statistics},
- language = {R},
- repository = {published-202406-pishchagina-change-point},
- langid = {en},
- abstract = {We address the challenge of identifying multiple change
- points in a group of independent time series, assuming these change
- points occur simultaneously in all series and their number is
- unknown. The search for the best segmentation can be expressed as a
- minimization problem over a given cost function. We focus on dynamic
- programming algorithms that solve this problem exactly. When the
- number of changes is proportional to data length, an
- inequality-based pruning rule encoded in the PELT algorithm leads to
- a linear time complexity. Another type of pruning, called functional
- pruning, gives a close-to-linear time complexity whatever the number
- of changes, but only for the analysis of univariate time series. We
- propose a few extensions of functional pruning for multiple
- independent time series based on the use of simple geometric shapes
- (balls and hyperrectangles). We focus on the Gaussian case, but some
- of our rules can be easily extended to the exponential family. In a
- simulation study we compare the computational efficiency of
- different geometric-based pruning rules. We show that for a small
- number of time series some of them ran significantly faster than
- inequality-based approaches in particular when the underlying number
- of changes is small compared to the data length.}
-}
-
-@article{legrand2024,
- bibtex_show = {true},
- author = {Legrand, Juliette and Pimont, François and Dupuy, Jean-Luc
- and Opitz, Thomas},
- publisher = {French Statistical Society},
- title = {Bayesian Spatiotemporal Modelling of Wildfire Occurrences and
- Sizes for Projections Under Climate Change},
- journal = {Computo},
- year = 2024,
- url = {https://computo.sfds.asso.fr/published-202407-legrand-wildfires/},
- doi = {10.57750/4y84-4t68},
- issn = {2824-7795},
- type = {{Research article}},
- domain = {Statistics},
- language = {R},
- repository = {published-202407-legrand-wildfires},
- langid = {en},
- abstract = {Appropriate spatiotemporal modelling of wildfire activity
- is crucial for its prediction and risk management. Here, we focus on
- wildfire risk in the Aquitaine region in the Southwest of France and
- its projection under climate change. We study whether wildfire risk
- could further increase under climate change in this specific region,
- which does not lie in the historical core area of wildfires in
- Southeastern France, corresponding to the Southwest. For this
- purpose, we consider a marked spatiotemporal point process, a
- flexible model for occurrences and magnitudes of such environmental
- risks, where the magnitudes are defined as the burnt areas. The
- model is first calibrated using 14 years of past observation data of
- wildfire occurrences and weather variables, and then applied for
- projection of climate-change impacts using simulations of numerical
- climate models until 2100 as new inputs. We work within the
- framework of a spatiotemporal Bayesian hierarchical model, and we
- present the workflow of its implementation for a large dataset at
- daily resolution for 8km-pixels using the INLA-SPDE approach. The
- assessment of the posterior distributions shows a satisfactory fit
- of the model for the observation period. We stochastically simulate
- projections of future wildfire activity by combining climate model
- output with posterior simulations of model parameters. Depending on
- climate models, spline-smoothed projections indicate low to moderate
- increase of wildfire activity under climate change. The increase is
- weaker than in the historical core area, which we attribute to
- different weather conditions (oceanic versus Mediterranean). Besides
- providing a relevant case study of environmental risk modelling,
- this paper is also intended to provide a full workflow for
- implementing the Bayesian estimation of marked log-Gaussian Cox
- processes using the R-INLA package of the R statistical software.}
-}
diff --git a/_bibliography/mock_papers.bib b/_bibliography/mock_papers.bib
deleted file mode 100644
index 63bfa931..00000000
--- a/_bibliography/mock_papers.bib
+++ /dev/null
@@ -1,39 +0,0 @@
-@Article {mock_tsne,
- bibtex_show = {true},
- author = {van der Maaten, Laurens and Hinton, Geoffrey},
- title = {{Visualizing Data using t-SNE: practical Computo example}},
- journal = {Computo},
- year = {2021},
- volume = {0},
- repository = {published-paper-tsne},
- type = {Template},
- language = {R, Python},
- domain = {Template},
- abstract = {We present a new technique called “t-SNE” that
- visualizes high-dimensional data by giving each
- datapoint a location in a two or three-dimensional
- map. The technique is a variation of Stochastic
- Neighbor Embedding hinton:stochastic that is much
- easier to optimize, and produces significantly
- better visualizations by reducing the tendency to
- crowd points together in the center of the
- map. t-SNE is better than existing techniques at
- creating a single map that reveals structure at many
- different scales. This is particularly important for
- high-dimensional data that lie on several different,
- but related, low-dimensional manifolds, such as
- images of objects from multiple classes seen from
- multiple viewpoints. For visualizing the structure
- of very large data sets, we show how t-SNE can use
- random walks on neighborhood graphs to allow the
- implicit structure of all the data to influence the
- way in which a subset of the data is displayed. We
- illustrate the performance of t-SNE on a wide
- variety of data sets and compare it with many other
- non-parametric visualization techniques, including
- Sammon mapping, Isomap, and Locally Linear
- Embedding. The visualization produced by t-SNE are
- significantly better than those produced by other
- techniques on almost all of the data sets.},
- keywords = {template, documentation, quarto, R, python}
-}
diff --git a/_bibliography/published.bib b/_bibliography/published.bib
deleted file mode 100644
index 2d0503de..00000000
--- a/_bibliography/published.bib
+++ /dev/null
@@ -1,237 +0,0 @@
-@article{lefort_peerannot,
- bibtex_show = {true},
- author = {Lefort, Tanguy and Charlier, Benjamin and Joly, Alexis and Salmon, Joseph},
- title = {{Peerannot: classification for crowdsourced image datasets with Python}},
- journal = {Computo},
- year = 2024,
- abstract = {Crowdsourcing is a quick and easy way to collect labels for large datasets, involving many workers. However, workers often disagree with each other. Sources of error can arise from the workers’ skills, but also from the intrinsic difficulty of the task. We present peerannot: a Python library for managing and learning from crowdsourced labels for classification. Our library allows users to aggregate labels from common noise models or train a deep learning-based classifier directly from crowdsourced labels. In addition, we provide an identification module to easily explore the task difficulty of datasets and worker capabilities.},
- doi = {10.57750/qmaz-gr91},
- repository = {published-202402-lefort-peerannot},
- type = {{Research article}},
- language = {Python},
- domain = {Machine Learning},
- keywords = {crowdsourcing, label noise, task difficulty, worker ability, classification},
- issn = {2824-7795}
-}
-
-@article{elmasri-optimal,
- bibtex_show = {true},
- author = {El Masri, Maxime and Morio, Jérôme and Simatos, Florian},
- title = {{Optimal projection for parametric importance sampling in high dimensions}},
- journal = {Computo},
- year = 2024,
- abstract = {In this paper we propose a dimension-reduction strategy in order to improve the performance of importance sampling in high dimension. The idea is to estimate variance terms in a small number of suitably chosen directions. We first prove that the optimal directions, i.e., the ones that minimize the Kullback--Leibler divergence with the optimal auxiliary density, are the eigenvectors associated to extreme (small or large) eigenvalues of the optimal covariance matrix. We then perform extensive numerical experiments that show that as dimension increases, these directions give estimations which are very close to optimal. Moreover, we show that the estimation remains accurate even when a simple empirical estimator of the covariance matrix is used to estimate these directions. These theoretical and numerical results open the way for different generalizations, in particular the incorporation of such ideas in adaptive importance sampling schemes},
- doi = {doi.org/10.57750/jjza-6j82},
- repository = {published-202402-elmasri-optimal},
- type = {{Research article}},
- language = {Python},
- domain = {Statistics},
- keywords = {Rare event simulation, Parameter estimation, Importance sampling, Dimension reduction, Kullback--Leibler divergence, Projection},
- issn = {2824-7795}
-}
-
-@article{adrat_repulsion,
- bibtex_show = {true},
- author = {Adrat, Hamza and Decreusefond, Laurent},
- title = {{Point Process Discrimination According to Repulsion}},
- journal = {Computo},
- year = 2024,
- abstract = {In numerous applications, cloud of points do seem to exhibit repulsion in the intuitive sense that there is no local cluster as in a Poisson process. Motivated by data coming from cellular networks, we devise a classification algorithm based on the form of the Voronoi cells. We show that, in the particular set of data we are given, we can retrieve some repulsiveness between antennas, which was expected for engineering reasons.},
- doi = {10.57750/3r07-aw28},
- repository = {published_202401_adrat_repulsion},
- type = {{Research article}},
- language = {Python},
- domain = {Statistics},
- keywords = {classification, point process, repulsion},
- issn = {2824-7795}
-}
-
-
-@article{favrot_hierarchical,
- bibtex_show = {true},
- author = {Favrot, Armand and Makoswki, David},
- title = {{A hierarchical model to evaluate pest treatments from prevalence and intensity data}},
- journal = {Computo},
- year = 2024,
- abstract = {In plant epidemiology, pest abundance is measured in field trials using metrics assessing either pest prevalence (fraction of the plant population infected) or pest intensity (average number of pest individuals present in infected plants). Some of these trials rely on prevalence, while others rely on intensity, depending on the protocols. In this paper, we present a hierarchical Bayesian model able to handle both types of data. In this model, the intensity and prevalence variables are derived from a latent variable representing the number of pest individuals on each host individual, assumed to follow a Poisson distribution. Effects of pest treaments, time trend, and between-trial variability are described using fixed and random effects. We apply the model to a real dataset in the context of aphid control in sugar beet fields. In this dataset, prevalence and intensity were derived from aphid counts observed on either factorial trials testing different types of pesticides treatments or field surveys monitoring aphid abundance. Next, we perform simulations to assess the impacts of using either prevalence or intensity data, or both types of data simultaneously, on the accuracy of the model parameter estimates and on the ranking of pesticide treatment efficacy. Our results show that, when pest prevalence and pest intensity data are collected separately in different trials, the model parameters are more accurately estimated using both types of trials than using one type of trials only. When prevalence data are collected in all trials and intensity data are collected in a subset of trials, estimations and pest treatment ranking are more accurate using both types of data than using prevalence data only. When only one type of observation can be collected in a pest survey or in an experimental trial, our analysis indicates that it is better to collect intensity data than prevalence data when all or most of the plants are expected to be infested, but that both types of data lead to similar results when the level of infestation is low to moderate. Finally, our simulations show that it is unlikely to obtain accurate results with fewer than 40 trials when assessing the efficacy of pest control treatments based on prevalence and intensity data. Because of its flexibility, our model can be used to evaluate and rank the efficacy of pest treatments using either prevalence or intensity data, or both types of data simultaneously. As it can be easily implemented using standard Bayesian packages, we hope that it will be useful to agronomists, plant pathologists, and applied statisticians to analyze pest surveys and field experiments conducted to assess the efficacy of pest treatments.},
- doi = {10.57750/6cgk-g727},
- repository = {published-202312-favrot-hierarchical},
- type = {{Research article}},
- language = {R},
- domain = {Statistics},
- keywords = {bayesian model, epidemiology, hierarchical model, pest control, trial, survey},
- issn = {2824-7795}
-}
-
-@article{cleynen_local,
- bibtex_show = {true},
- author = {Cleynen, Alice and Raynal, Louis and Marin, Jean-Michel},
- title = {{Local tree methods for classification: a review and some dead ends}},
- journal = {Computo},
- year = 2023,
- abstract = {Random Forests (RF) [@breiman:2001] are very popular machine learning methods. They perform well even with little or no tuning, and have some theoretical guarantees, especially for sparse problems [@biau:2012;@scornet:etal:2015]. These learning strategies have been used in several contexts, also outside the field of classification and regression. To perform Bayesian model selection in the case of intractable likelihoods, the ABC Random Forests (ABC-RF) strategy of @pudlo:etal:2016 consists in applying Random Forests on training sets composed of simulations coming from the Bayesian generative models. The ABC-RF technique is based on an underlying RF for which the training and prediction phases are separated. The training phase does not take into account the data to be predicted. This seems to be suboptimal as in the ABC framework only one observation is of interest for the prediction. In this paper, we study tree-based methods that are built to predict a specific instance in a classification setting. This type of methods falls within the scope of local (lazy/instance-based/case specific) classification learning. We review some existing strategies and propose two new ones. The first consists in modifying the tree splitting rule by using kernels, the second in using a first RF to compute some local variable importance that is used to train a second, more local, RF. Unfortunately, these approaches, although interesting, do not provide conclusive results.},
- doi = {10.57750/3j8m-8d57},
- repository = {published-202312-cleynen-local},
- type = {{Research article}},
- language = {R},
- domain = {Statistics},
- keywords = {classification, Random Forests, local methods},
- issn = {2824-7795}
-}
-
-@article{delattre_fim,
- bibtex_show = {true},
- author = {Delattre, Maud and Kuhn, Estelle},
- title = {{Computing an empirical Fisher information matrix estimate in latent variable models through stochastic approximation}},
- journal = {Computo},
- year = 2023,
- abstract = {The Fisher information matrix (FIM) is a key quantity in statistics. However its exact computation is often not trivial. In particular in many latent variable models, it is intricated due to the presence of unobserved variables. Several methods have been proposed to approximate the FIM when it can not be evaluated analytically. Different estimates have been considered, in particular moment estimates. However some of them require to compute second derivatives of the complete data log-likelihood which leads to some disadvantages. In this paper, we focus on the empirical Fisher information matrix defined as an empirical estimate of the covariance matrix of the score, which only requires to compute the first derivatives of the log-likelihood. Our contribution consists in presenting a new numerical method to evaluate this empirical Fisher information matrix in latent variable model when the proposed estimate can not be directly analytically evaluated. We propose a stochastic approximation estimation algorithm to compute this estimate as a by-product of the parameter estimate. We evaluate the finite sample size properties of the proposed estimate and the convergence properties of the estimation algorithm through simulation studies.},
- doi = {10.57750/r5gx-jk62},
- repository = {published-202311-delattre-fim},
- type = {{Research article}},
- language = {R},
- domain = {Statistics},
- keywords = {Model-based standard error, moment estimate, Fisher identity, stochastic approximation algorithm},
- issn = {2824-7795}
-}
-
-@article{sanou_multiscale,
- bibtex_show = {true},
- author = {Sanou, Edmond and Ambroise, Christophe and Robin, Geneviève},
- title = {{Inference of Multiscale Gaussian Graphical Model}},
- journal = {Computo},
- year = 2023,
- abstract = {Gaussian Graphical Models (GGMs) are widely used in high-dimensional data analysis to synthesize the interaction between variables. In many applications, such as genomics or image analysis, graphical models rely on sparsity and clustering to reduce dimensionality and improve performances. This paper explores a slightly different paradigm where clustering is not knowledge-driven but performed simultaneously with the graph inference task. We introduce a novel Multiscale Graphical Lasso (MGLasso) to improve networks interpretability by proposing graphs at different granularity levels. The method estimates clusters through a convex clustering approach — a relaxation of k-means, and hierarchical clustering. The conditional independence graph is simultaneously inferred through a neighborhood selection scheme for undirected graphical models. MGLasso extends and generalizes the sparse group fused lasso problem to undirected graphical models. We use continuation with Nesterov smoothing in a shrinkage-thresholding algorithm (CONESTA) to propose a regularization path of solutions along the group fused Lasso penalty, while the Lasso penalty is kept constant. Extensive experiments on synthetic data compare the performances of our model to state-of-the-art clustering methods and network inference models. Applications to gut microbiome data and poplar's methylation mixed with transcriptomic data are presented.},
- doi = {10.57750/1f4p-7955},
- repository = {published-202306-sanou-multiscale_glasso},
- type = {{Research article}},
- language = {R and Python},
- domain = {Statistics},
- keywords = {Neighborhood selection, Convex hierarchical clustering, Gaussian graphical models},
- issn = {2824-7795}
-}
-
-
-@article{chagneux_macrolitter,
- bibtex_show = {true},
- author = {Chagneux, Mathis and Le Corff, Sylvain and
- Gloaguen, Pierre and Ollion, Charles and Lepâtre, Océane and
- Bruge, Antoine},
- title = {Macrolitter Video Counting on Riverbanks Using State
- Space Models and Moving Cameras},
- journal = {Computo},
- year = {2023},
- repository = {published-202301-chagneux-macrolitter},
- doi = {10.57750/845m-f805},
- language = {Python},
- issn = {2824-7795},
- langid = {en},
- type = {{Research article}},
- abstract = {Litter is a known cause of degradation in marine
- environments and most of it travels in rivers before
- reaching the oceans. In this paper, we present a
- novel algorithm to assist waste monitoring along
- watercourses. While several attempts have been made
- to quantify litter using neural object detection in
- photographs of floating items, we tackle the more
- challenging task of counting directly in videos
- using boat-embedded cameras. We rely on multi-object
- tracking (MOT) but focus on the key pitfalls of
- false and redundant counts which arise in typical
- scenarios of poor detection performance. Our system
- only requires supervision at the image level and
- performs Bayesian filtering via a state space model
- based on optical flow. We present a new open image
- dataset gathered through a crowdsourced campaign and
- used to train a center-based anchor-free object
- detector. Realistic video footage assembled by water
- monitoring experts is annotated and provided for
- evaluation. Improvements in count quality are
- demonstrated against systems built from
- state-of-the-art multi-object trackers sharing the
- same detection capabilities. A precise error
- decomposition allows clear analysis and highlights
- the remaining challenges.}
-}
-
-@article{boulin_clayton,
- bibtex_show = {true},
- author = {Boulin, Alexis},
- title = {{A Python Package for Sampling from Copulae:
- clayton}},
- journal = {Computo},
- year = 2023,
- abstract = {The package clayton is designed to be intuitive,
- user-friendly, and efficient. It offers a wide range
- of copula models, including Archimedean, Elliptical,
- and Extreme. The package is implemented in pure
- Python, making it easy to install and use. In
- addition, we provide detailed documentation and
- examples to help users get started quickly. We also
- conduct a performance comparison with existing R
- packages, demonstrating the efficiency of our
- implementation. The clayton package is a valuable
- tool for researchers and practitioners working with
- copulae in Python},
- doi = {10.57750/4szh-t752},
- repository = {published-202301-boulin-clayton},
- type = {{Software paper}},
- language = {Python},
- domain = {Statistics},
- keywords = {Copulae, Random number generation},
- issn = {2824-7795}
-}
-
-@article{gimenez_lynx,
- bibtex_show = {true},
- author = {Gimenez, Olivier and Kervellec, Maelis and Fanjul,
- Jean-Baptiste and Chaine, Anna and Marescot, Lucile
- and Bollet, Yoann and Duchamp, Christophe},
- title = {{Trade-off between deep learning for species
- identification and inference about predator-prey
- co-occurrence: Reproducible R workflow integrating
- models in computer vision and ecological
- statistics}},
- journal = {Computo},
- year = 2022,
- abstract = {Deep learning is used in computer vision problems
- with important applications in several scientific
- fields. In ecology for example, there is a growing
- interest in deep learning for automatizing
- repetitive analyses on large amounts of images, such
- as animal species identification. However, there
- are challenging issues toward the wide adoption of
- deep learning by the community of ecologists. First,
- there is a programming barrier as most algorithms
- are written in Python while most ecologists are
- versed in R. Second, recent applications of deep
- learning in ecology have focused on computational
- aspects and simple tasks without addressing the
- underlying ecological questions or carrying out the
- statistical data analysis to answer these questions.
- Here, we showcase a reproducible R workflow
- integrating both deep learning and statistical
- models using predator-prey relationships as a case
- study. We illustrate deep learning for the
- identification of animal species on images collected
- with camera traps, and quantify spatial
- co-occurrence using multispecies occupancy models.
- Despite average model classification performances,
- ecological inference was similar whether we analysed
- the ground truth dataset or the classified
- dataset. This result calls for further work on the
- trade-offs between time and resources allocated to
- train models with deep learning and our ability to
- properly address key ecological questions with
- biodiversity monitoring. We hope that our
- reproducible workflow will be useful to ecologists
- and applied statisticians.},
- doi = {10.57750/yfm2-5f45},
- repository = {published-202204-deeplearning-occupancy-lynx},
- type = {{Research article}},
- language = {R},
- domain = {Statistical Ecology},
- keywords = {computer vision, deep-learning, species distribution
- modeling, ecological statistics},
- issn = {2824-7795}
-}
diff --git a/_bibliography/templates.bib b/_bibliography/templates.bib
deleted file mode 100644
index eda8eeba..00000000
--- a/_bibliography/templates.bib
+++ /dev/null
@@ -1,62 +0,0 @@
----
----
-
-@Article {template_r,
- author = {{Computo editorial board}},
- title = {{Computo Template for R users}},
- journal = {Computo},
- year = {2022},
- volume = {0},
- repository = {template-computo-R},
- type = {Template},
- language = {R},
- domain = {Template},
- abstract = {Documentation and sample of a simple R-based
- submission for the Computo journal, using our
- Quarto-based template and renv for handling
- dependencies. Shows how to automatically setup and
- build the HTML and PDF outputs, ready to submit to
- our peer-review platform.},
- keywords = {template, documentation, R, quarto, renv}
-}
-
-@Article {template_python,
- author = {{Computo editorial board}},
- title = {{Computo Template for Python users}},
- journal = {Computo},
- year = {2022},
- volume = {0},
- repository = {template-computo-python},
- type = {Template},
- language = {Python},
- domain = {Template},
- abstract = {Documentation and sample of a simple Python-based
- submission for the Computo journal, using our
- Quarto-based template and pip/venv for handling
- dependencies. Shows how to automatically setup and
- build the HTML and PDF outputs, ready to submit to
- our peer-review platform.},
- keywords = {template, documentation, Python, quarto, venv}
-}
-
-@Article {template_python,
- author = {{Computo editorial board}},
- title = {{Computo Template for Julia users}},
- journal = {Computo},
- year = {2022},
- volume = {0},
- repository = {template-computo-julia},
- type = {Template},
- language = {Julia},
- domain = {Template},
- abstract = {Documentation and sample of a simple Julia-based
- submission for the Computo journal, using our
- Quarto-based template and the built-in Julia Pkg manager.
- Shows how to automatically setup and
- build the HTML and PDF outputs, ready to submit to
- our peer-review platform.},
- keywords = {template, documentation, Julia, quarto, Pkg}
-}
-
-
-
diff --git a/_bibliography/templates_obsolete.bib b/_bibliography/templates_obsolete.bib
deleted file mode 100644
index dc57ecfd..00000000
--- a/_bibliography/templates_obsolete.bib
+++ /dev/null
@@ -1,22 +0,0 @@
----
----
-
-@Article {template_myst,
- author = {{Computo editorial board}},
- title = {{Template for writing a contribution for Computo based on Myst/Jupyter book}},
- journal = {Computo},
- year = {2021},
- volume = {0},
- repository = {template-computo-myst},
- type = {Template},
- language = {R, Python},
- domain = {Template},
- abstract = {This document provides a Myst/Jupyter book template
- for contributions to the Computo Journal. It also
- serves as a documentation for configuring the github
- repository which will host the notebook source of
- your manuscript and prove us the reproducibility of
- your work.},
- keywords = {template, documentation, Rmarkdown, binder}
-}
-
diff --git a/_config.yml b/_config.yml
deleted file mode 100644
index d27ac857..00000000
--- a/_config.yml
+++ /dev/null
@@ -1,372 +0,0 @@
-# -----------------------------------------------------------------------------
-# Site settings
-# -----------------------------------------------------------------------------
-
-title: COMPUTO
-first_name:
-middle_name:
-last_name:
-email: computo@sfds.asso.fr
-description: >
- A Journal of the French Statistical Society to promote reproducible Science
-footer_text: >
-# Powered by Jekyll with al-folio theme.
-# Hosted by GitHub Pages.
-
-icon: ⚙ # the emoji used as the favicon
-keywords: journal, statistics, machine-learning, reproducibility # add your own keywords or leave empty
-lang: en # the language of your site (for example: en, fr, cn, ru, etc.)
-url: https://computorg.github.io # the base hostname & protocol for your site
-baseurl: # the subpath of your site, e.g. /blog/
-last_updated: true # set to true if you want to display last updated in the footer
-impressum_path: # set to path to include impressum link in the footer, use the same path as permalink in a page, helps to conform with EU GDPR
-
-# -----------------------------------------------------------------------------
-# Theme
-# -----------------------------------------------------------------------------
-
-# code highlighter theme
-highlight_theme_light: github # https://github.com/jwarby/jekyll-pygments-themes
-highlight_theme_dark: native # https://github.com/jwarby/jekyll-pygments-themes
-
-# repo color theme
-repo_theme_light: default # https://github.com/anuraghazra/github-readme-stats/blob/master/themes/README.md
-repo_theme_dark: dark # https://github.com/anuraghazra/github-readme-stats/blob/master/themes/README.md
-
-# -----------------------------------------------------------------------------
-# RSS Feed
-# -----------------------------------------------------------------------------
-# will use title and url fields
-# Take a look to https://github.com/jekyll/jekyll-feed for more customization
-
-rss_icon: true
-
-# -----------------------------------------------------------------------------
-# Layout
-# -----------------------------------------------------------------------------
-
-navbar_fixed: true
-footer_fixed: true
-
-# Dimensions
-max_width: 800px
-
-# -----------------------------------------------------------------------------
-# Open Graph & Schema.org
-# -----------------------------------------------------------------------------
-# Display links to the page with a preview object on social media.
-serve_og_meta: false # Include Open Graph meta tags in the HTML head
-serve_schema_org: false # Include Schema.org in the HTML head
-og_image: # The site-wide (default for all links) Open Graph preview image
-
-# -----------------------------------------------------------------------------
-# Social integration
-# -----------------------------------------------------------------------------
-
-github_username: computorg
-twitter_username: # your Twitter handle
-mastodon_username: mathstodon.xyz/@computo # your mastodon instance+username in the format instance.tld/@username
-linkedin_username: # your LinkedIn user name
-telegram_username: # your Telegram user name
-scholar_userid: # your Google Scholar ID
-semanticscholar_id: # your Semantic Scholar ID
-whatsapp_number: # your WhatsApp number (full phone number in international format. Omit any zeroes, brackets, or dashes when adding the phone number in international format.)
-orcid_id: # your ORCID ID
-medium_username: # your Medium username
-quora_username: # your Quora username
-publons_id: # your ID on Publons
-research_gate_profile: # your profile on ResearchGate
-blogger_url: # your blogger URL
-work_url: # work page URL
-keybase_username: # your keybase user name
-wikidata_id: # your wikidata id
-dblp_url: # your DBLP profile url
-stackoverflow_id: # your stackoverflow id
-kaggle_id: # your kaggle id
-lastfm_id: # your lastfm id
-spotify_id: # your spotify id
-pinterest_id: # your pinterest id
-unsplash_id: # your unsplash id
-instagram_id: # your instagram id
-facebook_id: # your facebook id
-youtube_id: # your youtube channel id (youtube.com/@)
-discord_id: # your discord id (18-digit unique numerical identifier)
-
-#contact_note: >
-# You can even add a little note about which of these is the best way to reach you.
-
-# -----------------------------------------------------------------------------
-# Analytics and search engine verification
-# -----------------------------------------------------------------------------
-
-google_analytics: # your Goole Analytics measurement ID (format: G-XXXXXXXXXX)
-panelbear_analytics: # panelbear analytics site ID (format: XXXXXXXXX)
-
-google_site_verification: # your google-site-verification ID (Google Search Console)
-bing_site_verification: # out your bing-site-verification ID (Bing Webmaster)
-
-# -----------------------------------------------------------------------------
-# Blog
-# -----------------------------------------------------------------------------
-
-blog_name: Frequently Asked Questions # your blog must have a name for it to show up in the nav bar
-blog_description: Additional details and support for authors
-blog_name: F.A.Q. # blog_name will be displayed in your blog page
-blog_nav_title: FAQ # your blog must have a title for it to be displayed in the nav bar
-permalink: /blog/:year/:title/
-
-# Pagination
-pagination:
- enabled: true
-
-# Giscus comments (RECOMMENDED)
-# Follow instructions on https://giscus.app/ to setup for your repo to fill out the information below.
-giscus: #
-# repo: computorg/comutorg.github.io # /
-# repo_id: MDEwOlJlcG9zaXRvcnk2MDAyNDM2NQ==
-# category: Comments # name of the category under which discussions will be created
-# category_id: DIC_kwDOA5PmLc4CTBt6
-# mapping: title # identify discussions by post title
-# strict: 1 # use strict identification mode
-# reactions_enabled: 1 # enable (1) or disable (0) emoji reactions
-# input_position: bottom # whether to display input form below (bottom) or above (top) the comments
-# theme: preferred_color_scheme # name of the color scheme (preferred works well with al-folio light/dark mode)
-# emit_metadata: 0
-# lang: en
-
-# Disqus comments (DEPRECATED)
-disqus_shortname: #
-# https://help.disqus.com/en/articles/1717111-what-s-a-shortname
-
-# External sources.
-# If you have blog posts published on medium.com or other exteranl sources,
-# you can display them in your blog by adding a link to the RSS feed.
-external_sources:
-# - name: medium.com
-# rss_url: https://medium.com/@al-folio/feed
-
-# -----------------------------------------------------------------------------
-# Collections
-# -----------------------------------------------------------------------------
-
-collections:
- news:
- defaults:
- layout: post
- output: true
- permalink: /news/:path/
- projects:
- output: true
- permalink: /current/:path/
-
-news_scrollable: false # adds a vertical scroll bar if there are more than 3 news items
-news_limit: 5 # leave blank to include all the news in the `_news` folder
-
-# -----------------------------------------------------------------------------
-# Jekyll settings
-# -----------------------------------------------------------------------------
-
-# Markdown and syntax highlight
-markdown: kramdown
-highlighter: rouge
-kramdown:
- input: GFM
- syntax_highlighter_opts:
- css_class: 'highlight'
- span:
- line_numbers: false
- block:
- line_numbers: false
- start_line: 1
-
-# Includes & excludes
-include: ['_pages']
-exclude:
- - bin
- - Gemfile
- - Gemfile.lock
- - vendor
- - _pages_template
-keep_files:
- - CNAME
- - .nojekyll
- - .git
-
-# Plug-ins
-plugins:
- - jekyll-archives
- - jekyll-diagrams
- - jekyll-email-protect
- - jekyll-feed
- - jekyll-imagemagick
- - jekyll-minifier
- - jekyll-paginate-v2
- - jekyll/scholar
- - jekyll-sitemap
- - jekyll-link-attributes
- - jekyll-twitter-plugin
- - jemoji
-
-# Sitemap settings
-defaults:
- - scope:
- path: "assets/**/*.*"
- values:
- sitemap: false
-
-# -----------------------------------------------------------------------------
-# Jekyll Minifier
-# -----------------------------------------------------------------------------
-
-jekyll-minifier:
- exclude: ['robots.txt']
- uglifier_args:
- harmony: true
-
-# -----------------------------------------------------------------------------
-# Jekyll Archives
-# -----------------------------------------------------------------------------
-
-jekyll-archives:
- enabled: [year, tags, categories] # enables year, tag and category archives (remove if you need to disable one of them).
- layouts:
- year: archive-year
- tag: archive-tag
- category: archive-category
- permalinks:
- year: '/blog/:year/'
- tag: '/blog/tag/:name/'
- category: '/blog/category/:name/'
-
-display_tags: ['formatting', 'reproducibility', 'data', 'code'] # this tags will be displayed on the front page of your blog
-# -----------------------------------------------------------------------------
-# Jekyll Scholar
-# -----------------------------------------------------------------------------
-
-scholar:
-
- style: acm-siggraph
-# style: assets/bibliography/mystyle.csl
- locale: en
-
- sort_by: year, month
- order: descending, descending
-
- source: /_bibliography/
- bibliography: published.bib
- bibliography_template: bib
- # Note: if you have latex math in your bibtex, the latex filter
- # preprocessing may conflict with MathJAX if the latter is enabled.
- # See https://github.com/alshedivat/al-folio/issues/357.
- bibtex_filters: [latex, smallcaps, superscript]
-
- replace_strings: true
- join_strings: true
-
- use_raw_bibtex_entry: true
-
- details_dir: bibliography
- details_layout: bibtex.html
- details_link: Details
-
- query: "@*"
-
-# Filter out certain bibtex entry keywords used internally from the bib output
-filtered_bibtex_keywords: [abbr, abstract, arxiv, bibtex_show, html, pdf, selected, supp, blog, code, poster, slides, website, preview, altmetric]
-
-# Maximum number of authors to be shown for each publication (more authors are visible on click)
-max_author_limit: 3 # leave blank to always show all authors
-more_authors_animation_delay: 10 # more authors are revealed on click using animation; smaller delay means faster animation
-
-
-# -----------------------------------------------------------------------------
-# Jekyll Link Attributes
-# -----------------------------------------------------------------------------
-
-# These are the defaults
-external_links:
- enabled: true
- rel: external nofollow noopener
- target: _blank
- exclude:
-
-
-# -----------------------------------------------------------------------------
-# Responsive WebP Images
-# -----------------------------------------------------------------------------
-
-imagemagick:
- enabled: true # enables responsive images for your site (recomended, see https://github.com/alshedivat/al-folio/issues/537)
- widths:
- - 480
- - 800
- - 1400
- input_directories:
- - assets/img/
- input_formats:
- - ".jpg"
- - ".jpeg"
- - ".png"
- - ".tiff"
- output_formats:
- webp: "-resize 800x"
-
-# -----------------------------------------------------------------------------
-# Jekyll Diagrams
-# -----------------------------------------------------------------------------
-
-jekyll-diagrams:
- # configuration, see https://github.com/zhustec/jekyll-diagrams.
- # feel free to comment out this section if not using jekyll diagrams.
-
-
-# -----------------------------------------------------------------------------
-# Optional Features
-# -----------------------------------------------------------------------------
-
-enable_google_analytics: false # enables google analytics
-enable_panelbear_analytics: false # enables panelbear analytics
-enable_google_verification: false # enables google site verification
-enable_bing_verification: false # enables bing site verification
-enable_masonry: true # enables automatic project cards arangement
-enable_math: true # enables math typesetting (uses MathJax)
-enable_tooltips: false # enables automatic tooltip links generated
- # for each section titles on pages and posts
-enable_darkmode: false # enables switching between light/dark modes
-enable_navbar_social: false # enables displaying social links in the
- # navbar on the about page
-enable_project_categories: false # enables categorization of projects into
-enable_medium_zoom: true # enables image zoom feature (as on medium.com)
-enable_progressbar: true # enables a horizontal progress bar linked to the vertical scroll position
-
-# -----------------------------------------------------------------------------
-# Library versions
-# -----------------------------------------------------------------------------
-
-academicons:
- version: "1.9.1"
- integrity: "sha256-i1+4qU2G2860dGGIOJscdC30s9beBXjFfzjWLjBRsBg="
-bootstrap:
- version: "4.6.1"
- integrity:
- css: "sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY="
- js: "sha256-fgLAgv7fyCGopR/gBNq2iW3ZKIdqIcyshnUULC4vex8="
-fontawesome:
- version: "5.15.4"
- integrity: "sha256-mUZM63G8m73Mcidfrv5E+Y61y7a12O5mW4ezU3bxqW4="
-jquery:
- version: "3.6.0"
- integrity: "sha256-/xUj+3OJU5yExlq6GSYGSHk7tPXikynS7ogEvDej/m4="
-mathjax:
- version: "3.2.0"
-masonry:
- version: "4.2.2"
- integrity: "sha256-Nn1q/fx0H7SNLZMQ5Hw5JLaTRZp0yILA/FRexe19VdI="
-mdb:
- version: "4.20.0"
- integrity:
- css: "sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw="
- js: "sha256-NdbiivsvWt7VYCt6hYNT3h/th9vSTL4EDWeGs5SN3DA="
-medium_zoom:
- version: "1.0.6"
- integrity: "sha256-EdPgYcPk/IIrw7FYeuJQexva49pVRZNmt3LculEr7zM="
diff --git a/_data/coauthors.yml b/_data/coauthors.yml
deleted file mode 100644
index 8ed52124..00000000
--- a/_data/coauthors.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-"Adams":
- - firstname: ["Edwin", "E.", "E. P.", "Edwin Plimpton"]
- url: https://en.wikipedia.org/wiki/Edwin_Plimpton_Adams
-
-"Podolsky":
- - firstname: ["Boris", "B.", "B. Y.", "Boris Yakovlevich"]
- url: https://en.wikipedia.org/wiki/Boris_Podolsky
-
-"Rosen":
- - firstname: ["Nathan", "N."]
- url: https://en.wikipedia.org/wiki/Nathan_Rosen
-
-"Bach":
- - firstname: ["Johann Sebastian", "J. S."]
- url: https://en.wikipedia.org/wiki/Johann_Sebastian_Bach
-
- - firstname: ["Carl Philipp Emanuel", "C. P. E."]
- url: https://en.wikipedia.org/wiki/Carl_Philipp_Emanuel_Bach
-
-"Przibram":
- - firstname: ["Karl"]
- url: https://link.springer.com/article/10.1007/s00016-019-00242-z
-
-"Schrödinger":
- - firstname: ["Erwin"]
- url: https://en.wikipedia.org/wiki/Erwin_Schr%C3%B6dinger
-
-"Lorentz":
- - firstname: ["Hendrik Antoon"]
- url: https://en.wikipedia.org/wiki/Hendrik_Lorentz
-
-"Planck":
- - firstname: ["Max"]
- url: https://en.wikipedia.org/wiki/Max_Planck
diff --git a/_data/cv.yml b/_data/cv.yml
deleted file mode 100644
index 5b115724..00000000
--- a/_data/cv.yml
+++ /dev/null
@@ -1,97 +0,0 @@
-- title: General Information
- type: map
- contents:
- - name: Full Name
- value: Albert Einstein
- - name: Date of Birth
- value: 14th March 1879
- - name: Languages
- value: English, German
-
-- title: Education
- type: time_table
- contents:
- - title: PhD
- institution: University of Zurich, Zurich, Switzerland
- year: 1905
- description:
- - Description 1.
- - Description 2.
- - title: Description 3.
- contents:
- - Sub-description 1.
- - Sub-description 2.
- - title: Federal teaching diploma
- institution: Eidgenössische Technische Hochschule, Zurich, Switzerland
- year: 1900
- description:
- - Description 1.
- - Description 2.
-
-- title: Experience
- type: time_table
- contents:
- - title: Professor of Theoretical Physics
- institution: Institute for Advanced Study, Princeton University
- year: 1933 - 1955
- description:
- - Description 1.
- - Description 2.
- - title: Description 3.
- contents:
- - Sub-description 1.
- - Sub-description 2.
- - title: Visiting Professor
- institution: California Institute of Technology, Pasadena, California, US
- year: 1933
- description:
- - Description 1.
- - Description 2.
-
- - title: Director
- institution: Kaiser Wilhelm Institute for Physics, Berlin, Germany.
- year: 1917-1933
-
- - title: Professor of Theoretical Physics
- institution: Karl-Ferdinand University, Prague, Czechoslovakia
- year: 1911 - 1917
- description:
-
- - title: Associate Professor of Theoretical Physics
- institution: University of Zurich, Zurich, Switzerland
- year: 1909 - 1911
-
-- title: Open Source Projects
- type: time_table
- contents:
- - title: al-folio
- year: 2015-now
- description: A beautiful, simple, clean, and responsive Jekyll theme for academics.
-
-- title: Honors and Awards
- type: time_table
- contents:
- - year: 1921
- items:
- - Nobel Prize in Physics
- - Matteucci Medal
- - year: 2029
- items:
- - Max Planck Medal
-
-- title: Academic Interests
- type: nested_list
- contents:
- - title: Topic 1.
- items:
- - Description 1.
- - Description 2.
- - title: Topic 2.
- items:
- - Description 1.
- - Description 2.
-
-- title: Other Interests
- type: list
- contents:
- - Hobbies: Hobby 1, Hobby 2, etc.
diff --git a/_data/repositories.yml b/_data/repositories.yml
deleted file mode 100644
index 9535793a..00000000
--- a/_data/repositories.yml
+++ /dev/null
@@ -1,9 +0,0 @@
-github_users:
- - computorg
-
-github_repos:
- - computorg/template-computo-R
- - computorg/template-computo-python
- - computorg/template-computo-julia
- - computorg/computo-quarto-extension
- - computorg/published-paper-tsne
diff --git a/_data/venues.yml b/_data/venues.yml
deleted file mode 100644
index 6c16ad5d..00000000
--- a/_data/venues.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-"AJP":
- url: https://aapt.scitation.org/journal/ajp
- color: "#00369f"
-
-"PhysRev":
- url: https://journals.aps.org/
diff --git a/_includes/cv/list.html b/_includes/cv/list.html
deleted file mode 100644
index 75625859..00000000
--- a/_includes/cv/list.html
+++ /dev/null
@@ -1,5 +0,0 @@
-
- {% for content in entry.contents %}
-
{{ content }}
- {% endfor %}
-
\ No newline at end of file
diff --git a/_includes/cv/map.html b/_includes/cv/map.html
deleted file mode 100644
index e0d1983e..00000000
--- a/_includes/cv/map.html
+++ /dev/null
@@ -1,8 +0,0 @@
-
- {% for content in entry.contents %}
-
-
{{ content.name }}
-
{{ content.value }}
-
- {% endfor %}
-
\ No newline at end of file
diff --git a/_includes/cv/nested_list.html b/_includes/cv/nested_list.html
deleted file mode 100644
index 4778aca0..00000000
--- a/_includes/cv/nested_list.html
+++ /dev/null
@@ -1,14 +0,0 @@
-
- {% for content in entry.contents %}
-
-
{{ content.title }}
- {% if content.items %}
-
- {% for subitem in content.items %}
-
{{ subitem }}
- {% endfor %}
-
- {% endif %}
-
- {% endfor %}
-
\ No newline at end of file
diff --git a/_includes/cv/time_table.html b/_includes/cv/time_table.html
deleted file mode 100644
index 123b9d09..00000000
--- a/_includes/cv/time_table.html
+++ /dev/null
@@ -1,59 +0,0 @@
-
- {% for content in entry.contents %}
-
-
- {% if content.year %}
-
-
- {{ content.year }}
-
-
- {% endif %}
-
- {% if content.title %}
-
{{content.title}}
- {% endif %}
- {% if content.institution %}
-
{{content.institution}}
- {% endif %}
- {% if content.description %}
-
- {% for item in content.description %}
-
- {% if item.contents %}
- {{ item.title }}
-
- {% for subitem in item.contents %}
-
{{ subitem }}
- {% endfor %}
-
- {% else %}
- {{ item }}
- {% endif %}
-
- {% endfor %}
-
- {% endif %}
- {% if content.items %}
-
- {% for item in content.items %}
-
- {% if item.contents %}
- {{ item.title }}
-
- {% for subitem in item.contents %}
-
{{ subitem }}
- {% endfor %}
-
- {% else %}
- {{ item }}
- {% endif %}
-
- {% endfor %}
-
- {% endif %}
-
-
-
- {% endfor %}
-
\ No newline at end of file
diff --git a/_includes/disqus.html b/_includes/disqus.html
deleted file mode 100644
index 73fe9538..00000000
--- a/_includes/disqus.html
+++ /dev/null
@@ -1,13 +0,0 @@
-
- {{ year }}
- {%- if tags != "" %}
- ·
- {% for tag in page.tags -%}
-
- {{ tag }}
- {% endfor -%}
- {% endif %}
-
- {%- if categories != "" %}
- ·
- {% for category in page.categories -%}
-
- {{ category }}
- {% endfor -%}
- {% endif %}
-
-
-
-
-
- {{ content }}
-
-
- {%- if site.disqus_shortname and page.disqus_comments -%}
- {% include disqus.html %}
- {%- endif %}
- {%- if site.giscus.repo and page.giscus_comments -%}
- {% include giscus.html %}
- {%- endif -%}
-
-
diff --git a/_news/announcement_2.md b/_news/announcement_2.md
deleted file mode 100644
index dbd4b4d4..00000000
--- a/_news/announcement_2.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-layout: post
-title: A long announcement with details
-date: 2015-11-07 16:11:00-0400
-inline: false
----
-
-Announcements and news can be much longer than just quick inline posts. In fact, they can have all the features available for the standard blog posts. See below.
-
-***
-
-Jean shorts raw denim Vice normcore, art party High Life PBR skateboard stumptown vinyl kitsch. Four loko meh 8-bit, tousled banh mi tilde forage Schlitz dreamcatcher twee 3 wolf moon. Chambray asymmetrical paleo salvia, sartorial umami four loko master cleanse drinking vinegar brunch. Pinterest DIY authentic Schlitz, hoodie Intelligentsia butcher trust fund brunch shabby chic Kickstarter forage flexitarian. Direct trade cold-pressed meggings stumptown plaid, pop-up taxidermy. Hoodie XOXO fingerstache scenester Echo Park. Plaid ugh Wes Anderson, freegan pug selvage fanny pack leggings pickled food truck DIY irony Banksy.
-
-#### Hipster list
-
-
brunch
-
fixie
-
raybans
-
messenger bag
-
-
-Hoodie Thundercats retro, tote bag 8-bit Godard craft beer gastropub. Truffaut Tumblr taxidermy, raw denim Kickstarter sartorial dreamcatcher. Quinoa chambray slow-carb salvia readymade, bicycle rights 90's yr typewriter selfies letterpress cardigan vegan.
-
-***
-
-Pug heirloom High Life vinyl swag, single-origin coffee four dollar toast taxidermy reprehenderit fap distillery master cleanse locavore. Est anim sapiente leggings Brooklyn ea. Thundercats locavore excepteur veniam eiusmod. Raw denim Truffaut Schlitz, migas sapiente Portland VHS twee Bushwick Marfa typewriter retro id keytar.
-
-> We do not grow absolutely, chronologically. We grow sometimes in one dimension, and not in another, unevenly. We grow partially. We are relative. We are mature in one realm, childish in another.
-> —Anais Nin
-
-Fap aliqua qui, scenester pug Echo Park polaroid irony shabby chic ex cardigan church-key Odd Future accusamus. Blog stumptown sartorial squid, gastropub duis aesthetic Truffaut vero. Pinterest tilde twee, odio mumblecore jean shorts lumbersexual.
diff --git "a/_news/announcement_first_publ\303\256shed_paper.md" "b/_news/announcement_first_publ\303\256shed_paper.md"
deleted file mode 100644
index f5c97bfb..00000000
--- "a/_news/announcement_first_publ\303\256shed_paper.md"
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-date: 2022-04-03 07:59:00-0400
-inline: true
----
-
-Computo is proud to announce its [first published paper, by Olivier Gimenez et al.!](https://computo.sfds.asso.fr/publications/) :sparkles:
-
-Thanks to them and congratulations!
diff --git a/_news/announcement_open.md b/_news/announcement_open.md
deleted file mode 100644
index 0ba445f0..00000000
--- a/_news/announcement_open.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-layout: post
-date: 2021-10-28 07:59:00-0400
-inline: true
----
-
-[Paper submission](submit) is now officially opened! 📢
-
diff --git a/_news/announcement_publication_202301.md b/_news/announcement_publication_202301.md
deleted file mode 100644
index 092a5dd1..00000000
--- a/_news/announcement_publication_202301.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-date: 2023-01-11 07:59:00-0400
-inline: true
----
-
-Computo is happy to announce a [newly published paper, by Alexis Boulin!](https://computo.sfds.asso.fr/publications/) :sparkles:
-
-Congratulations!
diff --git a/_news/announcement_publication_202302.md b/_news/announcement_publication_202302.md
deleted file mode 100644
index 8ba932ee..00000000
--- a/_news/announcement_publication_202302.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-layout: post
-date: 2023-02-16 07:59:00-0400
-inline: true
----
-
-Another [published paper, by Mathis Chagneux and co-authors!](https://computo.sfds.asso.fr/publications/), a nice academic collaboration with Surfrider Europe for detecting macrolitter on riverbanks :sparkles:
diff --git a/_news/announcement_publication_202307.md b/_news/announcement_publication_202307.md
deleted file mode 100644
index 0ded1cb5..00000000
--- a/_news/announcement_publication_202307.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-layout: post
-date: 2023-07-11 07:59:00-0400
-inline: true
----
-
-[A new article was published, by Edmond Sanou, Geneviève Robin and Christophe Ambroise](https://computo.sfds.asso.fr/publications/), on a multiscale version of Graphical-Lasso.
diff --git a/_news/announcement_publication_202311.md b/_news/announcement_publication_202311.md
deleted file mode 100644
index af6e1403..00000000
--- a/_news/announcement_publication_202311.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-layout: post
-date: 2023-11-21 07:59:00-0400
-inline: true
----
-
-[Paper published by Maud Delattre and Estelle Kuhn](https://computo.sfds.asso.fr/published-202311-delattre-fim/), on computing an empirical Fisher information matrix estimate in latent variable models through stochastic approximation.
diff --git a/_news/announcement_publication_202312.md b/_news/announcement_publication_202312.md
deleted file mode 100644
index 3cfe55c7..00000000
--- a/_news/announcement_publication_202312.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-date: 2023-12-14 07:59:00-0400
-inline: true
----
-
-[Paper published by Alice Cleynen, Louis Raynal, and Jean-Michel
-Marin](https://computo.sfds.asso.fr/published-202312-cleynen-local/), on
-local tree methods for classification.
diff --git a/_news/announcement_publication_202401.md b/_news/announcement_publication_202401.md
deleted file mode 100644
index 175addb8..00000000
--- a/_news/announcement_publication_202401.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-layout: post
-date: 2024-01-09 07:59:00-0400
-inline: true
----
-
-[Paper published by Armand Favrot and David Makowski](https://computo.sfds.asso.fr/published-202312-favrot-hierarchical/), on MCMC estimation in a hierarchical model to evaluate pest treatments from prevalence and intensity data.
diff --git a/_news/announcement_publication_202402.md b/_news/announcement_publication_202402.md
deleted file mode 100644
index 3b8d9d0c..00000000
--- a/_news/announcement_publication_202402.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-layout: post
-date: 2024-02-26 07:59:00-0400
-inline: true
----
-
-A new article was published, by Hamza Adrat and Laurent Decreusefond: [Point Process Discrimination According to Repulsion](http://computo.sfds.asso.fr/published_202401_adrat_repulsion/).
diff --git a/_news/announcement_publication_202403.md b/_news/announcement_publication_202403.md
deleted file mode 100644
index 506a06ee..00000000
--- a/_news/announcement_publication_202403.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-date: 2024-04-04 07:59:00-0400
-inline: true
----
-
-A new article was published, by Maxime El Masri, Jérôme Morio, and Florian
-Simatos: [Optimal Projection for Parametric Importance Sampling in High
-Dimensions]( https://computo.sfds.asso.fr/published-202402-elmasri-optimal/)
diff --git a/_news/announcement_publication_202404.md b/_news/announcement_publication_202404.md
deleted file mode 100644
index 6a32bcbd..00000000
--- a/_news/announcement_publication_202404.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-layout: post
-date: 2024-05-15 07:59:00-0400
-inline: true
----
-
-A new article was published, by
- Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon: [Peerannot: classification for crowdsourced image datasets with Python]( https://computo.sfds.asso.fr/published-202402-lefort-peerannot/)
diff --git a/_news/announcement_start.md b/_news/announcement_start.md
deleted file mode 100644
index 86afda7d..00000000
--- a/_news/announcement_start.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-layout: post
-date: 2021-03-11 07:59:00-0400
-inline: true
----
-
-New version of the website relying on Jekyll, Jekyll-scholar, aI-folio emoji! :sparkles:
-
diff --git a/_news/extended_team.md b/_news/extended_team.md
deleted file mode 100644
index 602f7cc5..00000000
--- a/_news/extended_team.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-layout: post
-date: 2022-07-01 07:59:00-0400
-inline: true
----
-
-We are very pleased to welcome
-[Mathurin](https://mathurinm.github.io/),
-[François-David](https://fradav.github.io/) and
-[Ghislain](https://gdurif.perso.math.cnrs.fr/) [in the team](board) 👨💻 !
-Thanks for their support.
-
diff --git a/_news/extended_team_mp.md b/_news/extended_team_mp.md
deleted file mode 100644
index b02e2740..00000000
--- a/_news/extended_team_mp.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-date: 2023-06-21 07:59:00-0400
-inline: true
----
-
-Delighted to welcome
-[Marie-Pierre](httpshttps://marieetienne.github.io/) [in the team](board) 👨💻 !
-
diff --git a/_news/how_doesit_work.md b/_news/how_doesit_work.md
deleted file mode 100644
index 6283e65c..00000000
--- a/_news/how_doesit_work.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-date: 2021-05-25 07:59:00-0400
-inline: true
----
-
-[Check out these nice diagrams :sparkles: describing how Computo works
-on the author, editor and reviewer
-sides]({{ site.baseurl }}/blog/2021/submission-process/).
diff --git a/_news/moving_openreview.md b/_news/moving_openreview.md
deleted file mode 100644
index 41fde908..00000000
--- a/_news/moving_openreview.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-layout: post
-date: 2023-06-21 07:59:00-0400
-inline: true
----
-
-We're changing our peer-review platform 📰 to a fully free, open and communty-based solution with [OpenReview](https://openreview.net/group?id=Computo). Don't hesitate to send us feedback or contact us if you have any questions.
-
diff --git a/_news/new_templates.md b/_news/new_templates.md
deleted file mode 100644
index 00820015..00000000
--- a/_news/new_templates.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-layout: post
-date: 2023-02-08 07:59:00-0400
-inline: true
----
-
-We have [updated our templates for submitting to Computo](https://computo.sfds.asso.fr/submit/): all based on Quarto, but now with more specific and hoefully friendly set-ups for R, Python or Julia users.
diff --git a/_news/sfds_2022.md b/_news/sfds_2022.md
deleted file mode 100644
index 452b7273..00000000
--- a/_news/sfds_2022.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-layout: post
-date: 2022-06-14 07:59:00-0400
-inline: true
----
-
-The 53rd ["Journées de Statistique"](https://jds22.sciencesconf.org/)
-take place in Lyon, where Computo is presented. We will also be
-searching for volunteers for reviewing the submission. Please [fill
-this form](https://forms.gle/P9iYJANuNM4WTVHDA).
-
diff --git a/_news/toronto_workshop_reproducibility.md b/_news/toronto_workshop_reproducibility.md
deleted file mode 100644
index a5d7957c..00000000
--- a/_news/toronto_workshop_reproducibility.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-layout: post
-date: 2022-02-24 07:59:00-0400
-inline: true
----
-
-Computo has been presented at the [2nd Toronto Workshop on reproducibility](https://canssiontario.utoronto.ca/toronto_workshop_on_reproducibility_2022/) !
-🤩 [slides are online here](http://computo.sfds.asso.fr/comm/)
-
-
-
diff --git a/_news/workshop_reproducibility.md b/_news/workshop_reproducibility.md
deleted file mode 100644
index 5cd79643..00000000
--- a/_news/workshop_reproducibility.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-layout: post
-date: 2021-06-23 07:59:00-0400
-inline: true
----
-
-We are presenting Computo at a [mini-workshop on reproducible
-research](https://mtp-rr2021.sciencesconf.org/) in Montpellier 🗓. Very
-interesting talks to come besides ours
-
diff --git a/_pages/about.md b/_pages/about.md
deleted file mode 100644
index c604d56a..00000000
--- a/_pages/about.md
+++ /dev/null
@@ -1,79 +0,0 @@
----
-title: "About"
-layout: page
-permalink: /about
-order: 1
-nav: yes
----
-
----
-
-## Aims and scope
-
-Computo has been created in the context of a reproducibility crisis in
-science, which calls for higher standards in the publication of
-scientific results. Computo aims at promoting
-computational/algorithmic contributions in statistics and machine
-learning (ML) that provide insight into which models or methods are the
-most appropriate to address a specific scientific question.
-
-The journal welcomes the following types of contributions:
-
-- New **methods** with original stats/ML developments, or numerical
- studies that illustrate theoretical results in stats/ML;
-- **Case studies** or **surveys** on stats/ML methods to address a specific
- (type of) question in data analysis, neutral comparison studies that
- provide insight into when, how, and why the compared methods perform
- well or less well;
-- **Software/tutorial papers** to present implementations of stats/ML algorithms or
- to feature the use of a package/toolbox. For such papers we expect not only the description of an existing implementation but also the study of a concrete use case. If applicable, a comparison to related works and appropriate benchmarking are also expected.
-
-
-
Pre-submission enquiries
-
-
Prospective authors willing to know whether their contribution
- falls into the scope of Computo are encouraged to contact the
- editor at computo@sfds.asso.fr. Please make sure to include the title and abstract of your work in your pre-submission enquiry.
-
-
-
-## An open access journal with reproducible contributions
-
-Computo is free for readers and authors.
-It is an open access journal which means that all content is
-freely available without charge to the user or his/her institution.
-Users are allowed to read, download, copy, distribute, print,
-search, or link to the full texts of the articles, or use
-them for any other lawful purpose, without asking prior permission
-from the publisher or the author. This is in accordance with the
-Budapest Open Access Initiative (BOAI) definition of open access.
-
-The reproducibility of numerical results is a necessary condition for
-publication in Computo. In particular, submissions must include all
-necessary data (e.g. via Zenodo repositories) and code. For
-contributions featuring the implementation of methods/algorithms, the
-quality of the provided code is assessed during the review process.
-We accept contributions in the form of notebooks (e.g. Rmarkdown, or
-Jupyter).
-
-The reviews are open, i.e. visible to any reader after acceptance of
-the contribution. Reviewers may choose to remain anonymous or not.
-
-## Contact
-Enquiries can be sent to the Chief Editor, Julien Chiquet, through computo@sfds.asso.fr.
-
-## Thanks
-
-We rely on [Jekyll](https://jekyllrb.com/),
-[BibTeX](http://www.bibtex.org/), the
-[aI-folio](https://github.com/alshedivat/al-folio) Jekyll theme,
-[Rmarkdown](https://rmarkdown.rstudio.com/),
-[Jupyter-book](https://jupyterbook.org/) and we drew our inspiration
-from [Rescience-C](https://rescience.github.io/) and
-[distill.pub](https://distill.pub/) among others.
-
-## About the logo
-
-Computo's logo has been designed by [Loïc Schwaller](https://loack.me/).
-
-
diff --git a/_pages/board.md b/_pages/board.md
deleted file mode 100644
index 1a7a1089..00000000
--- a/_pages/board.md
+++ /dev/null
@@ -1,123 +0,0 @@
----
-layout: page
-permalink: /board/
-title: Staff
-nav_order: 6
-description: People making Computo
-nav: true
----
-
----
-
-
diff --git a/_pages/home.md b/_pages/home.md
deleted file mode 100644
index 08cedfcd..00000000
--- a/_pages/home.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-layout: about
-title: about
-permalink: /
-description:
COMPUTO
A journal of the French Statistical Society - ISSN 2824-7795
-nav_order: 1
-news: true # includes a list of news items
-selected_papers: false # includes a list of papers marked as "selected={true}"
-social: false # includes social icons at the bottom of the page
----
-
----
-
-This journal aims at promoting computational/algorithmic contributions
-in statistics and machine learning that provide insight into which
-models or methods are more appropriate to address a specific
-scientific question. In order to achieve this goal, Computo goes
-beyond classical static publications by leveraging technical advances
-in literate programming and scientific reporting.
-
-[More details about our philosophy](about).
-
diff --git a/_pages/publications.md b/_pages/publications.md
deleted file mode 100644
index 0b293cf4..00000000
--- a/_pages/publications.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-layout: page
-permalink: /publications/
-title: Articles
-description: Publications by years in reversed chronological order
-nav_order: 2
-years: [2024, 2023, 2022]
-nav: true
----
-
-## Published
-
-
-
-## In the pipeline
-
-Manuscript conditionally accepted, whose editorial and scientific reproducibility is being validated
-
-
-
-{% bibliography --file in_production %}
-
-
-
-## Example: a mock contribution
-
-This page is a reworking of the original t-SNE article using the
-Computo template. It aims to help authors submitting to the journal by
-using some advanced formatting features.
-
-
-
-{% bibliography --file mock_papers %}
-
-
-
diff --git a/_pages/repos.md b/_pages/repos.md
deleted file mode 100644
index 613da700..00000000
--- a/_pages/repos.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-layout: page
-permalink: /repos/
-title: Repositories
-nav: true
-nav_order: 5
----
-
-Check our pinned repositories for our quarto extension, some templates for authors and an advanced mock contribution.
-
-{% if site.data.repositories.github_repos %}
-
- {% for repo in site.data.repositories.github_repos %}
- {% include repository/repo.html repository=repo %}
- {% endfor %}
-
-Computo relies on Open Review
-for the review process. The review form is text-based, but Markdown and LaTeX formatting is supported so you can add hyperlinks and use LaTeX to add equations to your review. Reviewers are also required to answer a handful of rating scale questions about the submission.
-
-
Once a manuscript is accepted, reviews and discussion will be made available
-on the Computo website. Reviewers can choose to remain anonymous or
-not.
-
-
-
-## Guidelines for evaluation
-
-In order to help you in performing your review we provide a list of the main questions we are trying to answer when evaluating a submission:
-
-1. Is the paper within the scope of Computo?
-
- See [Aims and Scope]({{ site.baseurl }} /about) of Computo.
-
-2. Is the paper clearly written?
-
- Computo is intended for computational scientists in statistics/machine learning. The Abstract and Introduction should be as nontechnical as possible, and provide a clear description of the contributions of the paper. Strength and limitations of the work should be adequately discussed, in particular in relation to related work. Graphs and tables should be well thought out and uncluttered.
-
-3. Is the paper correct?
-
- Mathematical and algorithmic validity are the authors' professional responsibility. Referees can spot errors of reasoning, but are not expected to perform line-by-line checks of technical results.
-
-4. Is the paper adequately evaluated?
-
- Are all claims clearly articulated and supported either by empirical experiments or theoretical analyses? If appropriate, have the authors implemented their work and demonstrated its utility on a significant problem?
-
-5. Is the paper reproducible?
-
- The reproducibility of numerical results is a necessary condition for publication in Computo. The referees are expected to check whether they can run the code provided by the authors to reproduce their results. In case of major reproducibility issues, the referees should warn the Associate Editor as soon as possible.
-
-## Plagiarism policy
-
-Computo abides by the Committee on Publishing Ethics’s (COPE) guidelines listed below on plagiarism:
-- COPE Council. [Suspected plagiarism in a submitted manuscript](https://doi.org/10.24318/cope.2019.2.1), Version 2, November 2018.
-- COPE Council. [Suspected plagiarism in a published manuscript](https://doi.org/10.24318/cope.2019.2.2). Version 2. 2013.
\ No newline at end of file
diff --git a/_pages/submit.md b/_pages/submit.md
deleted file mode 100644
index 1b733d35..00000000
--- a/_pages/submit.md
+++ /dev/null
@@ -1,89 +0,0 @@
----
-layout: page
-permalink: /submit/
-title: Submit
-description: Overview and general guidelines to submit a contribution to Computo
-nav_order: 3
-nav: true
----
-
----
-
-## Overview
-
-Submissions to Computo require both scientific content (typically
-equations, codes and figures, data) and a proof that this content is
-reproducible. This is achieved by means of i) a notebook system, ii) a
-virtual environment fixing the dependencies and iii) continuous
-integration (plus, if needed, an external website to store data
-files such as [Zenodo](https://zenodo.org/) or [OSF](https://osf.io/) ).
-
-A Computo submission is thus a git repository (e.g. Github or Gitlab) typically containing
-
-- the source file of the notebook (a markdown file with yaml metadata)
-- auxiliary files: a $$\mathrm{bib}\TeX $$ file and some statics files, e.g. figures or small .csv data tables
-- configuration files to set up the dependencies in a virtual environment
-- configuration files to set up the continuous integration rendering the final documents
-
-The compiled notebook (both HTML and PDF) will be directly generated
-in the git(hub) repository via continuous integration (e.g., Github
-action or Gitlab CI) and published, if the action is successful,
-to a web page (e.g. gh-page).
-
-The PDF and the git repository address are then submitted via OpenReview.
-
-## Available templates for R, Python and Julia
-
-
-
Warning!
-
-
To start writing your own contribution, do not start from scratch!! Please use one of our quarto-based templates below to ensure reproducibility. quarto supports R, Python and Julia.
-
-
-
-
-To get started, click "[GIT REPO]" for the language of your choice and follow the corresponding "Step-by-step procedure". These templates serve both as material for starting to write your submission, and as a documentation for doing so.
-
-
-
-{% bibliography --file templates %}
-
-
-
-## Submit your work
-
-Once your are happy with your notebook AND the continuous integration (Github action or Gitlab CI) is successful, you must submit your PDF and the url of your git repository via [OpenReview, our platform for peer-reviewing](https://openreview.net/group?id=Computo).
-
-## Reviewing and publication
-
-Submitted papers are reviewed by external reviewers selected by the Associate Editor in charge of the paper.
-Computo strives for fast reviewing cycles, but cannot provide strict guarantees on the matter; the current time between submission and publication is under six months.
-
-In order to ensure an efficient reviewing process, authors are requested upon submission to suggest the names of four potential referees. To avoid conflicts of interests, recent co-authors or collaborators should not be suggested.
-
-Computo's accepted papers are published electronically immediately upon receipt under [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).
-Authors retain the copyright and full publishing rights without restrictions.
-
-More information about the reviewing process are available on the [Review page]({{ site.baseurl }} /review)
-
-## Computo's code of ethics for authors
-
-- **Originality**:
- Authors guarantee that their proposed article is original, and that it infringes no moral intellectual property right of any other person or entity.
- Authors guarantee that their proposed article has not been published previously, and that they have not submitted the proposed article simultaneously to any other journal.
-- **Conflicts of interest**:
- Authors shall disclose any potential conflict of interest, whether it is professional,
- financial or other, to the journal’s Editor, if this conflict could be interpreted as having
- influenced their work. Authors shall declare all sources of funding for the research presented in the article.
-- **Impartiality**:
- All articles are examined impartially, and their merits are assessed regardless of the
- sex, religion, sexual orientation, nationality, ethnic origin, length of service or institutional affiliation of the author(s).
-- **Funding**:
- All funding received by the author(s) shall be clearly stated in the article(s).
-- **Defamatory statements**:
- Authors guarantee that their proposed article contains no matter of a defamatory, hateful, fraudulent or knowingly inexact character.
-- **References**:
- Authors guarantee that all the publications used in their work have been cited appropriately.
-- **Copyright/author's right/license compliance**:
- Authors guarantee that they comply with the usage license of any third party contents/works (code, software, data, figures/images, documents, etc.) that were used to produce their work.
-
diff --git a/_pages_template/announcement_long.md b/_pages_template/announcement_long.md
deleted file mode 100644
index 3c57d275..00000000
--- a/_pages_template/announcement_long.md
+++ /dev/null
@@ -1,35 +0,0 @@
----
-layout: post
-title: A long announcement with details
-date: 2021-03-09 16:11:00-0400
-inline: false
----
-
-Announcements and news can be much longer than just quick inline posts.
-
-***
-
-Jean shorts raw denim Vice normcore, art party High Life PBR
-skateboard stumptown vinyl kitsch. Four loko meh 8-bit, tousled banh
-mi tilde forage Schlitz dreamcatcher twee 3 wolf moon. Chambray
-asymmetrical paleo salvia, sartorial umami four loko master cleanse
-drinking vinegar brunch. Pinterest DIY authentic Schlitz, hoodie
-Intelligentsia butcher trust fund brunch shabby chic Kickstarter
-forage flexitarian. Direct trade cold-pressed meggings stumptown plaid, pop-up
-taxidermy. Hoodie XOXO fingerstache scenester Echo Park. Plaid ugh Wes
-Anderson, freegan pug selvage fanny pack leggings pickled food truck
-DIY irony Banksy.
-
-#### Hipster list
-
-
brunch
-
fixie
-
raybans
-
messenger bag
-
-
-
-
diff --git a/_plugins/external-posts.rb b/_plugins/external-posts.rb
deleted file mode 100644
index e4fd5eb6..00000000
--- a/_plugins/external-posts.rb
+++ /dev/null
@@ -1,36 +0,0 @@
-require 'feedjira'
-require 'httparty'
-require 'jekyll'
-
-module ExternalPosts
- class ExternalPostsGenerator < Jekyll::Generator
- safe true
- priority :high
-
- def generate(site)
- if site.config['external_sources'] != nil
- site.config['external_sources'].each do |src|
- p "Fetching external posts from #{src['name']}:"
- xml = HTTParty.get(src['rss_url']).body
- feed = Feedjira.parse(xml)
- feed.entries.each do |e|
- p "...fetching #{e.url}"
- slug = e.title.downcase.strip.gsub(' ', '-').gsub(/[^\w-]/, '')
- path = site.in_source_dir("_posts/#{slug}.md")
- doc = Jekyll::Document.new(
- path, { :site => site, :collection => site.collections['posts'] }
- )
- doc.data['external_source'] = src['name'];
- doc.data['feed_content'] = e.content;
- doc.data['title'] = "#{e.title}";
- doc.data['description'] = e.summary;
- doc.data['date'] = e.published;
- doc.data['redirect'] = e.url;
- site.collections['posts'].docs << doc
- end
- end
- end
- end
- end
-
-end
diff --git a/_plugins/hideCustomBibtex.rb b/_plugins/hideCustomBibtex.rb
deleted file mode 100644
index 4a852fde..00000000
--- a/_plugins/hideCustomBibtex.rb
+++ /dev/null
@@ -1,15 +0,0 @@
- module Jekyll
- module HideCustomBibtex
- def hideCustomBibtex(input)
- keywords = @context.registers[:site].config['filtered_bibtex_keywords']
-
- keywords.each do |keyword|
- input = input.gsub(/^.*#{keyword}.*$\n/, '')
- end
-
- return input
- end
- end
-end
-
-Liquid::Template.register_filter(Jekyll::HideCustomBibtex)
diff --git a/_posts/2021-04-23-submission-process.md b/_posts/2021-04-23-submission-process.md
deleted file mode 100644
index ff166aa8..00000000
--- a/_posts/2021-04-23-submission-process.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-layout: post
-title: How does Computo work?
-date: 3021-04-23 00:00:00
-description: Diagrams that describe the submission process
----
-
-Check out the author's perspective or the full process to get a general
-overview.
-
-
-
diff --git a/_posts/2023-03-17-HTML-to-website.md b/_posts/2023-03-17-HTML-to-website.md
deleted file mode 100644
index 47dbbec9..00000000
--- a/_posts/2023-03-17-HTML-to-website.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-layout: post
-title: How to automatically publish the HTML of my contribution to a website?
-date: 2023-03-17 00:00:00
-tags: reproducibility
-description: Describe how to render your article, activate your gh-page and publish your contribution online
----
-
-
-Some authors reported that their contribution was not published automatically, even when using one of our [3 template repositories](/repos) and even when the build action was successful. This is basically due to the fact that the `gh-pages` is not preporly setup.
-
-We review here the full process for more clarity.
-
-## 1. Check that the build action is correctly configured
-
-If you used one of our template repository, the build action (in `.github/workflows/build.yml`) should look like this:
-
-{% highlight yaml linenos %}
-name: build
-
-on:
- workflow_dispatch:
- push:
- branches: main
-
-jobs:
- build-deploy:
- runs-on: ubuntu-latest
- permissions:
- contents: write
- steps:
- - name: Check out repository
- uses: actions/checkout@v2
-
- [...]
-
- - name: Render and Publish
- uses: quarto-dev/quarto-actions/publish@v2
- with:
- target: gh-pages
- env:
- GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-{% endhighlight %}
-
-The last step named `Render and Publish` first compiles your notebook and then pushes the HTML and PDF output to a special branch named `gh-pages` which is preferably used by Github to define a web page associated with the current repository, with the address https://user.github.io/repo_name : this is where your final rendered paper should go. If the build action is successful, you don't have to worry and you can move on to the next check.
-
-## 2. Check that gh-pages is activated on your repos
-
-By default, the mechanism that checks if a web page should be published in association with your repository is not activated. You need to go to Settings > page and apply the following configuration:
-
-
-
-Once this is done, you may need to trigger the build action for the first successful deployment of your web page.
-
-## 3. One last thing
-
-Don't forget to include the address of the page where your contribution is published to help the reviewer in the `About` section of your repository. For example, for the Computo `R` template, we get :
-
-
diff --git a/_posts/2023-03-24-gitlab-integration.md b/_posts/2023-03-24-gitlab-integration.md
deleted file mode 100644
index e6c27e62..00000000
--- a/_posts/2023-03-24-gitlab-integration.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-layout: post
-title: 'I use gitlab instead of github: what should I do?'
-date: 2030-03-24 00:00:00
-tags: reproducibility
-description: Discuss integration of Computo's contribution in Gitlab instances
----
-
-_Under Construction_
-
diff --git a/_posts/2023-03-24-others-languages.md b/_posts/2023-03-24-others-languages.md
deleted file mode 100644
index c3d35bb4..00000000
--- a/_posts/2023-03-24-others-languages.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-layout: post
-title: 'I use a different language than Python, R or Julia: would Computo accept my contributions?'
-date: 2023-03-24 00:00:00
-tags: [reproducibility, code]
-description: Describe how to handle other languages than R, Julia or Python
----
-
-In principle, we are open to any kind of language.
-
-In practice, we need to integrate reproducible and compilable code into our quarto template. Natively, we support, `R`, `Python` and `Julia` and provide dedicated templates. For others, if the language is supported by a Jupyter kernel ([there are kernels for many languages](https://gist.github.com/chronitis/682c4e0d9f663e85e3d87e97cd7d1624), [quarto allows code execution](https://quarto.org/docs/computations/execution-options.html#engine-binding).
-
-When writing your contribution though, keep in mind that some languages are not designed for interactivity and that there will be a formatting effort to support your point in your manuscript (which could be as expensive as interfacing this code with Python or R via `pybind11`, `Rcpp` or equivalent). It's your choice.
-
-From our side, we will do our best for the technical aspects to help with the integration of any language, but the editorial board and reviewers will also do the work to make sure the contribution is within the bounds scientifically and in the spirit of reproducibility.
diff --git a/_posts/2023-03-24-what-reproducibility.md b/_posts/2023-03-24-what-reproducibility.md
deleted file mode 100644
index 003ec43b..00000000
--- a/_posts/2023-03-24-what-reproducibility.md
+++ /dev/null
@@ -1,42 +0,0 @@
----
-layout: post
-title: What is expected exactly in terms of reproducibility?
-date: 2023-07-04 00:00:00
-tags: reproducibility
-description: Discuss the different kinds of reproducibility at play in Computo, and what is expected from the authors.
----
-
-Computo is not just about publishing a notebook and proving that it can be compiled with CI! This part of the process is what we call _"Editorial Reproducibility"_. _"Scientific"_ or _"numerical"_ reproducibility of the analyses is also mandatory, on top of classical peer-review evaluation.
-
-We don't ask people reproducing their data... yet! We also don't ask for "bit-wise computational" reproducibility (i.e. obtaining exactly the same results bit-by-bit) but rather a "statistical" reproducibility, i.e. obtaining results leading to the same conclusion, with potential non-significant statistical variability.
-
-
-
-Indeed, the global scientific workflow of a reproducible process for a Computo may be split in two types of steps:
-
-External
-: This part of the process may be conducted outside of the notebook environment, for a list of reasons (non-exclusive to each other):
-
-- the process is too long to be conducted in a notebook
-- the data to be processed is too big to be handled directly in the notebook
-- it needs a specific environment (e.g. a cluster, with gpus, etc.)
-- it needs to involve specific languages (e.g. C, C++, Fortran, etc.) or build tools (e.g. make, cmake, etc.)
-
-It is “Computational reproducibility”, where the reproducibility is achieved by providing the code and the environment to run it, but not the results themselves.
-
-Editorial
-: This is where the notebook presents the results of the external process, and where everything is put together to produce the final document, it is “Direct reproducibility” in the sense that the notebook is the only thing needed to reproduce the results.
-
-Ultimately, the workflow must end with a direct reproducibility step which concludes the whole process.
-
-When applicable, the switch from external to editorial reproducibility is done with a “data transfer” step, where the data produced by the external process is transferred to the notebook environment. It’s required that not only the intermediate results are provided, but also the code to transfer it in the notebook environment. They are a variety of software solutions to do so.
-
-## Examples of data transfer solutions
-
-### Intermediate results storage
-- in python environment: the [`joblib.Memory`](https://joblib.readthedocs.io/en/latest/memory.html) class which provides a caching mechanism for python functions, and can be used to save the results of a function call to disk, and load it back later.
-- in R environment: the `.RData` file format, which can be loaded back in R with the `load()` function.
-
-### Transfer of the results to the notebook environment
-- for both aforementioned solutions, the results (`.joblib` directory or `.Rdata` file) could be committed to the git repository, and directly loaded in the notebook environment.
-- Another solution is to centralize input data (when large enough) and intermediate results on a shared scientific provider (we recommend [Zenodo](https://zenodo.org/) for this purpose), and download them in the notebook environment.
diff --git a/_posts/2023-06-21-data.md b/_posts/2023-06-21-data.md
deleted file mode 100644
index 2582aef7..00000000
--- a/_posts/2023-06-21-data.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-layout: post
-title: I have large or sensible data. How should I proceed?
-date: 2023-06-21 00:00:00
-tags: reproducibility
-description: Describe how to handle large or sensible data files when submitting to Computo
----
-
-## Large data sets
-
-If your submission materials contain files larger than 50MB, **especially data files**, they won’t fit on a git repository as is. For this reason, we encourage you to put your data or any materials you deem necessary on an external “open data” centered repository hub such a [Zenodo](https://zenodo.org/) or [OSF](https://osf.io/).
-
-You could also use any long-term (emphasis on long-term) data repository that is standard in your scientific community (or for a specific type of data/scientific application), and for which it is straight-forward to retrieve the data using a script code/notebook code (we highly encourage to use open platforms, ideally institutionally hosted).
-
-## Sensible data sets
-
-Since the reproducibility of numerical results is a necessary condition for publication in *Computo*, your submissions must include all necessary data (e.g. via Zenodo repositories). However, if you have sensible data (for example, biomedical data that needs to be anonymized), you are invited to contact the editorial committee to explain and justify your position. In any case, we will ask you to make public at least a sample of the original data, and a demonstration of its use in your article for Computo. The results of the analyses carried out on the totality of the data should be made available in the form of a binary file, in order to produce the statistical summaries necessary to illustrate your assertions.
diff --git a/_posts/2023-06-21-long-running-code.md b/_posts/2023-06-21-long-running-code.md
deleted file mode 100644
index e73d2abd..00000000
--- a/_posts/2023-06-21-long-running-code.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-layout: post
-title: My data analysis takes several hours/days/weeks... How to address the issue of reproducibility?
-date: 2023-06-21 00:00:00
-tags: reproducibility
-description: Discuss the reproducibility for long-running code
----
-
-If your analyses, model tuning or training phase take a prohibitively long time to compile and integrate, you can include the results of the trained methods in the form of a binary file. However, you must provide the code enabling the user to fully reproduce the training phase, and illustrate your code in a small, toy-sized example.
diff --git a/_sass/_base.scss b/_sass/_base.scss
deleted file mode 100644
index 6806aebe..00000000
--- a/_sass/_base.scss
+++ /dev/null
@@ -1,783 +0,0 @@
-/*******************************************************************************
- * Styles for the base elements of the theme.
- ******************************************************************************/
-
-// Typography
-
-body {
- font-family: var(--ff-main) !important;
-}
-
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- font-family: $ff-title ;
- font-weight: 500;
- color: var(--global-title-color) !important;
-}
-
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- color: var(--global-text-color);
-}
-
-hr {
- border-top: 1px solid var(--global-divider-color);
-}
-
-table {
- td, th {
- color: var(--global-text-color);
- }
- td {
- font-size: 1rem;
- }
-}
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- &:hover {
- color: var(--global-theme-color);
- text-decoration: underline;
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- &:hover:after :not(.nav-item.dropdown) {
- width: 100%;
- }
-}
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-figure, img {
- max-width: 90vw;
-}
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-blockquote {
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- border-left: 2px solid var(--global-theme-color);
- margin: 1.5em 10px;
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- font-size: 1.2rem;
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-.equation {
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-.caption {
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-.card {
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- margin-bottom: 10px;
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- .row {
- display: flex;
- align-items: center;
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-// Citation
-
-.citation, .citation-number {
- color: var(--global-theme-color);
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-// Profile
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-.profile {
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- .address {
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-.profile.float-right{
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- transition-property: 0.15s ease;
- -moz-transition: 0.15s ease;
- -ms-transition: 0.15s ease;
- -o-transition: 0.15s ease;
- transition: all 0.15s ease;
- }
- div.abstract.hidden {
- border: dashed 1px var(--global-bg-color);
- }
- div.abstract.hidden.open {
- border-color: var(--global-text-color);
- }
- }
- }
-}
-
-// Rouge Color Customization
-figure.highlight {
- margin: 0 0 1rem;
-}
-
-pre {
- color: var(--global-theme-color);
- background-color: var(--global-code-bg-color);
- border-radius: 6px;
- padding: 6px 12px;
- pre, code {
- background-color: transparent;
- border-radius: 0;
- padding: 0;
- }
-}
-
-code {
- color: var(--global-theme-color);
- background-color: var(--global-code-bg-color);
- border-radius: 3px;
- padding: 3px 3px;
-}
-
-// Transitioning Themes
-html.transition,
-html.transition *,
-html.transition *:before,
-html.transition *:after {
- transition: all 750ms !important;
- transition-delay: 0 !important;
-}
-
-.info-block {
- border-left: 6px solid #{$computo-color};
- border-right: 1px solid #{$computo-color-light};
- border-top: 1px solid #{$computo-color-light};
- border-bottom: 1px solid #{$computo-color-light};
- margin-top: 1.25rem;
- margin-bottom: 1.25rem;
- border-radius: .25rem;
- padding: 0em ;
-}
-
-
-.info-block .info-block-header {
- background-color: #{$computo-color-light};
- color: #{$computo-color-dark};
- border-bottom: none;
- font-weight: 600;
- opacity: 85%;
- font-size: 1.1rem;
- padding-left: .5em;
- padding-right: .5em;
- padding-top: .2em;
- margin-bottom: -0.2em;
- display: flex;
-}
-
-.info-block .info-block-body {
- padding-left: .5em;
- padding-right: .5em;
- padding-top: .8em;
- padding-bottom: -.2em;
-}
-
-.about-title {
- float:left;
-}
-
-.about-title h1 {
- width:100%;
- font-family: $ff-title ;
- font-weight: 700;
- color: $computo-color !important;
- writing-mode: vertical-rl;
- text-orientation: upright;
- font-size:12px;
- margin:0px;
-}
-
-.container-about {
- width:100%;
- height:auto;
- padding:1%;
-}
-
-// Extra Markdown style (post Customization)
-.post{
- .post-meta{
- color: var(--global-text-color-light);
- font-size: 0.875rem;
- margin-bottom: 0;
- }
- .post-tags{
- color: var(--global-text-color-light);
- font-size: 0.875rem;
- padding-top: 0.25rem;
- padding-bottom: 1rem;
- a {
- color: var(--global-text-color-light);
- text-decoration: none;
- &:hover {
- color: var(--global-theme-color);
- }
- }
- }
- .post-content{
- blockquote {
- border-left: 5px solid var(--global-theme-color);
- padding: 8px;
- }
- }
-}
-
-progress {
- /* Positioning */
- position: fixed;
- left: 0;
- top: 56px;
- z-index: 10;
-
- /* Dimensions */
- width: 100%;
- height: 1px;
-
- /* Reset the appearance */
- -webkit-appearance: none;
- -moz-appearance: none;
- appearance: none;
-
- /* Get rid of the default border in Firefox/Opera. */
- border: none;
-
- /* Progress bar container for Firefox/IE10 */
- background-color: transparent;
-
- /* Progress bar value for IE10 */
- color: var(--global-theme-color);
- }
-
- progress::-webkit-progress-bar {
- background-color: transparent;
- }
-
- progress::-webkit-progress-value {
- background-color: var(--global-theme-color);
- }
-
- progress::-moz-progress-bar {
- background-color: var(--global-theme-color);
- }
-
- .progress-container {
- width: 100%;
- background-color: transparent;
- position: fixed;
- top: 56px;
- left: 0;
- height: 5px;
- display: block;
- }
-
- .progress-bar {
- background-color: var(--global-theme-color);
- width: 0%;
- display: block;
- height: inherit;
- }
diff --git a/_sass/_distill.scss b/_sass/_distill.scss
deleted file mode 100644
index d83fafd4..00000000
--- a/_sass/_distill.scss
+++ /dev/null
@@ -1,126 +0,0 @@
-/*******************************************************************************
- * Style overrides for distill blog posts.
- ******************************************************************************/
-
-d-byline {
- border-top-color: var(--global-divider-color) !important;
-}
-
-d-byline h3 {
- color: var(--global-text-color) !important;
-}
-
-d-byline a, d-article d-byline a {
- color: var(--global-text-color) !important;
- &:hover {
- color: var(--global-hover-color) !important;
- }
-}
-
-d-article {
- border-top-color: var(--global-divider-color) !important;
- a, p, h1, h2, h3, h4, h5, h6, li, table {
- color: var(--global-text-color) !important;
- }
- a, h1, h2, hr, table, table th, table td {
- border-bottom-color: var(--global-divider-color) !important;
- }
- a:hover {
- border-bottom-color: var(--global-hover-color) !important;
- }
- b i {
- display: inline;
- }
-
- d-contents {
- align-self: start;
- grid-column: 1 / 4;
- grid-row: auto / span 4;
- justify-self: end;
- margin-top: 0em;
- padding-left: 2em;
- padding-right: 3em;
- border-right: 1px solid var(--global-divider-color);
- width: calc(max(70%, 300px));
- margin-right: 0px;
- margin-top: 0em;
- display: grid;
- grid-template-columns:
- minmax(8px, 1fr) [toc] auto
- minmax(8px, 1fr) [toc-line] 1px
- minmax(32px, 2fr);
-
- nav {
- grid-column: toc;
- a {
- border-bottom: none !important;
- &:hover {
- border-bottom: 1px solid var(--global-text-color) !important;
- }
- }
- h3 {
- margin-top: 0;
- margin-bottom: 1em;
- }
- div {
- display: block;
- outline: none;
- margin-bottom: 0.8em;
- color: rgba(0, 0, 0, 0.8);
- font-weight: bold;
- }
- ul {
- padding-left: 1em;
- margin-top: 0;
- margin-bottom: 6px;
- list-style-type: none;
- li {
- margin-bottom: 0.25em;
- }
- }
- }
- .figcaption {
- line-height: 1.4em;
- }
- toc-line {
- border-right: 1px solid var(--global-divider-color);
- grid-column: toc-line;
- }
- }
-
- d-footnote {
- scroll-margin-top: 66px;
- }
-}
-
-d-appendix {
- border-top-color: var(--global-divider-color) !important;
- color: var(--global-distill-app-color) !important;
- h3, li, span {
- color: var(--global-distill-app-color) !important;
- }
- a, a.footnote-backlink {
- color: var(--global-distill-app-color) !important;
- &:hover {
- color: var(--global-hover-color) !important;
- }
- }
-}
-
-@media (max-width: 1024px) {
- d-article {
- d-contents {
- display: block;
- grid-column-start: 2;
- grid-column-end: -2;
- padding-bottom: 0.5em;
- margin-bottom: 1em;
- padding-top: 0.5em;
- width: 100%;
- border: 1px solid var(--global-divider-color);
- nav {
- grid-column: none;
- }
- }
- }
-}
diff --git a/_sass/_layout.scss b/_sass/_layout.scss
deleted file mode 100644
index 9c10cac7..00000000
--- a/_sass/_layout.scss
+++ /dev/null
@@ -1,50 +0,0 @@
-/******************************************************************************
- * Content
- ******************************************************************************/
-
-body {
- padding-bottom: 70px;
- color: var(--global-text-color);
- background-color: var(--global-bg-color);
-
- h1, h2, h3, h4, h5, h6 {
- scroll-margin-top: 66px;
- }
-}
-
-body.fixed-top-nav {
- // Add some padding for the nav-bar.
- padding-top: 56px;
-}
-
-body.sticky-bottom-footer {
- // Remove padding below footer.
- padding-bottom: 0;
-}
-
-.container {
- max-width: $max-content-width;
-}
-
-// Profile
-.profile {
- img {
- width: 100%;
- }
-}
-
-// TODO: redefine content layout.
-
-
-/******************************************************************************
- * Publications
- ******************************************************************************/
-
-// TODO: redefine publications layout.
-
-
-/*****************************************************************************
-* Projects
-*****************************************************************************/
-
-// TODO: redefine projects layout.
diff --git a/_sass/_themes.scss b/_sass/_themes.scss
deleted file mode 100644
index 9c873a26..00000000
--- a/_sass/_themes.scss
+++ /dev/null
@@ -1,72 +0,0 @@
-/*******************************************************************************
- * Themes
- ******************************************************************************/
-
-:root {
- --global-bg-color: #{$white-color};
- --global-code-bg-color: #{$code-bg-color-light};
- --global-text-color: #{$black-color};
- --global-title-color: #{$computo-color-dark};
- --global-distill-app-color: #{$grey-color};
- --global-theme-color: #{$computo-color};
- --global-hover-color: #{$computo-color-dark};
- --global-navbar-bg-color: #{$computo-color};
- --global-navbar-text-color: #{$white-color};
- --global-navbar-theme-color: #{$white-color};
- --global-navbar-hover-color: #{$computo-color-light};
- --global-footer-bg-color: #{$computo-color};
- --global-footer-text-color: #{$grey-color-light};
- --global-footer-link-color: #{$white-color};
- --global-icon-color: #{$grey-color-dark};
- --global-navbar-icon-color: navajowhite;
- --global-distill-app-color: #{$grey-color};
- --global-divider-color: rgba(0,0,0,.1);
- --global-card-bg-color: #{$white-color};
-
- .fa-sun {
- display : none;
- }
- .fa-moon {
- padding-left: 10px;
- padding-top: 12px;
- display : block;
- }
-
- .repo-img-light {
- display: block;
- }
- .repo-img-dark {
- display: none;
- }
-}
-
-html[data-theme='dark'] {
- --global-bg-color: #{$grey-color-dark};
- --global-code-bg-color: #{$code-bg-color-dark};
- --global-text-color: #{$grey-color-light};
- --global-text-color-light: #{$grey-color-light};
- --global-theme-color: #{$cyan-color};
- --global-hover-color: #{$cyan-color};
- --global-footer-bg-color: #{$grey-color-light};
- --global-footer-text-color: #{$grey-color-dark};
- --global-footer-link-color: #{$black-color};
- --global-distill-app-color: #{$grey-color-light};
- --global-divider-color: #424246;
- --global-card-bg-color: #{$grey-900};
-
- .fa-sun {
- padding-left: 10px;
- padding-top: 12px;
- display : block;
- }
- .fa-moon {
- display : none;
- }
-
- .repo-img-light {
- display: none;
- }
- .repo-img-dark {
- display: block;
- }
-}
diff --git a/_sass/_variables.scss b/_sass/_variables.scss
deleted file mode 100644
index 7c4d5ec2..00000000
--- a/_sass/_variables.scss
+++ /dev/null
@@ -1,45 +0,0 @@
-/*******************************************************************************
- * Variables used throughout the theme.
- * To adjust anything, simply edit the variables below and rebuild the theme.
- ******************************************************************************/
-
-
-// Colors
-$red-color: #FF3636 !default;
-$red-color-dark: #B71C1C !default;
-$orange-color: #F29105 !default;
-$blue-color: #0076df !default;
-$blue-color-dark: #00369f !default;
-$cyan-color: #2698BA !default;
-$light-cyan-color: lighten($cyan-color, 25%);
-$green-color: #00ab37 !default;
-$green-color-lime: #B7D12A !default;
-$green-color-dark: #009f06 !default;
-$green-color-light: #ddffdd !default;
-$green-color-bright: #11D68B !default;
-$purple-color: #B509AC !default;
-$light-purple-color: lighten($purple-color, 25%);
-$pink-color: #f92080 !default;
-$pink-color-light: #ffdddd !default;
-$yellow-color: #efcc00 !default;
-
-$grey-color: #828282 !default;
-$grey-color-light: lighten($grey-color, 40%);
-$grey-color-dark: #1C1C1D;
-$grey-900: #212529;
-
-$white-color: #ffffff !default;
-$black-color: #000000 !default;
-
-// Theme colors
-$dark-blue: #0F2E3D !default;
-$computo-color: #034E79 !default;
-$computo-color-dark: darken($computo-color, 10%);
-$computo-color-light: lighten($computo-color, 60%);
-
-$code-bg-color-light: rgba($computo-color, 0.05);
-$code-bg-color-dark: darken($computo-color, 25%);
-
-// Font
-$ff-title: 'Oswald', sans-serif;
-$ff-main: 'Open Sans', sans-serif;
diff --git a/about.html b/about.html
new file mode 100644
index 00000000..8bf877fe
--- /dev/null
+++ b/about.html
@@ -0,0 +1 @@
+ About | COMPUTO
About
Aims and scope
Computo has been created in the context of a reproducibility crisis in science, which calls for higher standards in the publication of scientific results. Computo aims at promoting computational/algorithmic contributions in statistics and machine learning (ML) that provide insight into which models or methods are the most appropriate to address a specific scientific question.
The journal welcomes the following types of contributions:
New methods with original stats/ML developments, or numerical studies that illustrate theoretical results in stats/ML;
Case studies or surveys on stats/ML methods to address a specific (type of) question in data analysis, neutral comparison studies that provide insight into when, how, and why the compared methods perform well or less well;
Software/tutorial papers to present implementations of stats/ML algorithms or to feature the use of a package/toolbox. For such papers we expect not only the description of an existing implementation but also the study of a concrete use case. If applicable, a comparison to related works and appropriate benchmarking are also expected.
Pre-submission enquiries
Prospective authors willing to know whether their contribution falls into the scope of Computo are encouraged to contact the editor at computo@sfds.asso.fr. Please make sure to include the title and abstract of your work in your pre-submission enquiry.
An open access journal with reproducible contributions
Computo is free for readers and authors. It is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the Budapest Open Access Initiative (BOAI) definition of open access.
The reproducibility of numerical results is a necessary condition for publication in Computo. In particular, submissions must include all necessary data (e.g. via Zenodo repositories) and code. For contributions featuring the implementation of methods/algorithms, the quality of the provided code is assessed during the review process. We accept contributions in the form of notebooks (e.g. Rmarkdown, or Jupyter).
The reviews are open, i.e. visible to any reader after acceptance of the contribution. Reviewers may choose to remain anonymous or not.
Contact
Enquiries can be sent to the Chief Editor, Julien Chiquet, through computo@sfds.asso.fr.
- 
- {% endif %}
- 
diff --git a/_pages/board.md b/_pages/board.md
deleted file mode 100644
index 1a7a1089..00000000
--- a/_pages/board.md
+++ /dev/null
@@ -1,123 +0,0 @@
----
-layout: page
-permalink: /board/
-title: Staff
-nav_order: 6
-description: People making Computo
-nav: true
----
-
----
-
-
- ISSN 2824-7795
-nav_order: 1
-news: true # includes a list of news items
-selected_papers: false # includes a list of papers marked as "selected={true}"
-social: false # includes social icons at the bottom of the page
----
-
----
-
-This journal aims at promoting computational/algorithmic contributions
-in statistics and machine learning that provide insight into which
-models or methods are more appropriate to address a specific
-scientific question. In order to achieve this goal, Computo goes
-beyond classical static publications by leveraging technical advances
-in literate programming and scientific reporting.
-
-[More details about our philosophy](about).
-
diff --git a/_pages/publications.md b/_pages/publications.md
deleted file mode 100644
index 0b293cf4..00000000
--- a/_pages/publications.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-layout: page
-permalink: /publications/
-title: Articles
-description: Publications by years in reversed chronological order
-nav_order: 2
-years: [2024, 2023, 2022]
-nav: true
----
-
-## Published
-
-
orcid.org/0000-0002-3629-3429