@article{bunchIndirectReferenceIntervals2022,
  title = {Indirect Reference Intervals Using an {{R}} Pipeline.},
  author = {Bunch, Dustin R.},
  date = {2022-04},
  journaltitle = {J Mass Spectrom Adv Clin Lab},
  volume = {24},
  eprint = {35252947},
  eprinttype = {pmid},
  pages = {22--30},
  location = {{Netherlands}},
  issn = {2667-145X 2667-1468},
  doi = {10.1016/j.jmsacl.2022.02.004},
  abstract = {BACKGROUND: Indirect reference intervals require robust statistical approaches to separate the pathological and healthy values. This can be achieved with a data  pipeline created in R, a freely available statistical programming language.  METHODS: A data pipeline was created to ingest, partition, normalize, remove  outliers, and identify reference intervals for testosterone (Testo; n ~=~7,207)  and aspartate aminotransferase (AST; n ~=~5,882) using data sets from NHANES.  RESULTS: The estimates for AST and Testo determined by this pipeline approximated  current RIs. Care should be taken when using this pipeline as there are  limitations that depend on the pathology of the analyte and the data set being  used for RI estimation. CONCLUSIONS: R can be used to create a robust statistical  reference interval pipeline.},
  langid = {english},
  pmcid = {PMC8889237},
  keywords = {ANOVA; Analysis of variance,AST; aspartate aminotransferase,CLSI; Clinical Laboratory Standards Institute,cmu,EHR; electronic health record,IFCC; International Federation of Clinical Chemistry and Laboratory Medicine,LC-MS/MS; Liquid chromatography tandem mass spectrometry,LIS; Laboratory informatics system,markdown,Mixtools,non lu,pubmed,R markdown,R markdown tutorial,Reference interval,RI; reference interval,SDI; Standard deviation index,SDR; Standard deviation ratio,Testo; Testosterone,TukeyHSD; Tukey multiple pairwise-comparisons,z5; Critical z-score},
  file = {/home/igor/Zotero/storage/YYP9JQTY/Bunch_2022_Indirect reference intervals using an R pipeline.pdf}
}

@article{chapmanExpectedPosterioriScoring2022,
  title = {Expected a Posteriori Scoring in {{PROMIS}}(®).},
  author = {Chapman, Robert},
  date = {2022-06-03},
  journaltitle = {J Patient Rep Outcomes},
  volume = {6},
  number = {1},
  eprint = {35657454},
  eprinttype = {pmid},
  pages = {59},
  location = {{Germany}},
  issn = {2509-8020},
  doi = {10.1186/s41687-022-00464-9},
  abstract = {BACKGROUND: The Patient-Reported Outcome Measurement Information System(®) (PROMIS(®)) was developed to reliably measure health-related quality of life  using the patient's voice. To achieve these aims, PROMIS utilized Item Response  Theory methods in its development, validation and implementation. PROMIS measures  are typically scored using a specific method to calculate scores, called Expected  A Posteriori estimation. BODY: Expected A Posteriori scoring methods are  flexible, produce accurate scores and can be efficiently calculated by  statistical software. This work seeks to make Expected A Posteriori scoring  methods transparent and accessible to a larger audience through description,  graphical demonstration and examples. Further applications and practical  considerations of Expected A Posteriori scoring are presented and discussed. All  materials used in this paper are made available through the R Markdown  reproducibility framework and are intended to be reviewed and reused. Commented  statistical code for the calculation of Expected A Posteriori scores is included.  CONCLUSION: This work seeks to provide the reader with a summary and  visualization of the operation of Expected A Posteriori scoring, as implemented  in PROMIS. As PROMIS is increasingly adopted and implemented, this work will  provide a basis for making psychometric methods more accessible to the PROMIS  user base.},
  langid = {english},
  pmcid = {PMC9166925},
  keywords = {cmu,markdown,non lu,pubmed,R markdown},
  file = {/home/igor/Zotero/storage/7R2KLGBS/Chapman_2022_Expected a posteriori scoring in PROMIS(®).pdf}
}

@article{considineToolEncourageMinimum2019,
  title = {A {{Tool}} to {{Encourage Minimum Reporting Guideline Uptake}} for {{Data Analysis}} in {{Metabolomics}}.},
  author = {Considine, Elizabeth C. and Salek, Reza M.},
  date = {2019-03-05},
  journaltitle = {Metabolites},
  volume = {9},
  number = {3},
  eprint = {30841575},
  eprinttype = {pmid},
  location = {{Switzerland}},
  issn = {2218-1989},
  doi = {10.3390/metabo9030043},
  abstract = {Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been  shown to be unclear and incomplete. Major omissions and a lack of logical flow  render the data analysis' sections in metabolomics studies impossible to follow,  and therefore replicate or even imitate. Here, we propose possible reasons why  the original reporting guidelines have had poor adherence and present an approach  to improve their uptake. We present in this paper an R markdown reporting  template file that guides the production of text and generates workflow diagrams  based on user input. This R Markdown template contains, as an example in this  instance, a set of minimum information requirements specifically for the data  pre-treatment and data analysis section of biomarker discovery metabolomics  studies, (gleaned directly from the original proposed guidelines by Goodacre at  al). These minimum requirements are presented in the format of a questionnaire  checklist in an R markdown template file. The R Markdown reporting template  proposed here can be presented as a starting point to encourage the data analysis  section of a metabolomics manuscript to have a more logical presentation and to  contain enough information to be understandable and reusable. The idea is that  these guidelines would be open to user feedback, modification and updating by the  metabolomics community via GitHub.},
  langid = {english},
  pmcid = {PMC6468746},
  keywords = {cmu,data analysis,markdown,minimum guidelines,non lu,pubmed,R markdown,reporting,reproducibility},
  file = {/home/igor/Zotero/storage/RSRUDDYS/Considine_Salek_2019_A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in.pdf}
}

@article{daasDynamicPublicationMedia2022,
  title = {Dynamic Publication Media with the {{COPASI R Connector}} ({{CoRC}}).},
  author = {Daas, Johanna C. J. and Förster, Jonas D. and Pahle, Jürgen},
  date = {2022-06},
  journaltitle = {Math Biosci},
  volume = {348},
  eprint = {35452633},
  eprinttype = {pmid},
  pages = {108822},
  location = {{United States}},
  issn = {1879-3134 0025-5564},
  doi = {10.1016/j.mbs.2022.108822},
  abstract = {In this article we show how dynamic publication media and the COPASI R Connector (CoRC) can be combined in a natural and synergistic way to communicate  (biochemical) models. Dynamic publication media are becoming a popular tool for  authors to effectively compose and publish their work. They are built from  templates and the final documents are created dynamically. In addition, they can  also be interactive. Working with dynamic publication media is made easy with the  programming environment R via its integration with tools such as R Markdown,  Jupyter and Shiny. Additionally, the COmplex PAthway SImulator COPASI  (http://www.copasi.org), a widely used biochemical modelling toolkit, is  available in R through the use of the COPASI R Connector (CoRC,  https://jpahle.github.io/CoRC). Models are a common tool in the mathematical  biosciences, in particular kinetic models of biochemical networks in  (computational) systems biology. We focus on three application areas of dynamic  publication media and CoRC: Documentation (reproducible workflows), Teaching  (creating self-paced lessons) and Science Communication (immersive and engaging  presentation). To illustrate these, we created six dynamic document examples in  the form of R Markdown and Jupyter notebooks, hosted on the platforms GitHub,  shinyapps.io, Google Colaboratory. Having code and output in one place, creating  documents in template-form and the option of interactivity make the combination  of dynamic documents and CoRC a versatile tool. All our example documents are  freely available at https://jpahle.github.io/DynamiCoRC under the Creative  Commons BY 4.0 licence.},
  langid = {english},
  keywords = {*Software,*Systems Biology,cmu,COPASI,CoRC,Dynamic publication media,Jupyter,Kinetics,markdown,non lu,pubmed,R markdown,Systems biology},
  file = {/home/igor/Zotero/storage/WJ5FCFZY/Daas et al_2022_Dynamic publication media with the COPASI R Connector (CoRC).pdf}
}

@unpublished{deletrazModeTexteMarkdown2022,
  type = {Atelier},
  title = {En mode texte\,: Markdown, Stylo, Pandoc, Notebook…},
  author = {Deletraz, Gaëlle and Rabaud, Julien},
  date = {2022},
  url = {https://markdown-somate2022.netlify.app},
  urldate = {2023-04-17},
  eventtitle = {So-Mate 2022},
  langid = {fre},
  venue = {{Pau}},
  keywords = {markdown,non lu,pandoc,Publication scientifique}
}

@online{eyssetteUtiliserMarkdownPour2023,
  type = {Mastodon post},
  title = {Utiliser le Markdown pour tout faire.Le diaporama (fait en markdown bien sûr !) qui m'a servi de support lors de mon atelier pour la Journée…},
  author = {Eyssette, Cédric (@eyssette@scholar.social)},
  date = {2023-04-07},
  url = {https://scholar.social/@eyssette/110158455766516456},
  urldate = {2023-04-17},
  abstract = {Utiliser le Markdown pour tout faire.Le diaporama (fait en markdown bien sûr !) qui m'a servi de support lors de mon atelier pour la Journée du Libre Éducatif 2023.https://eyssette.forge.aeif.fr/marp-slides/slides/2022-2023/utiliser-le-markdown-pour-tout-faire\#Markdown\#JDLE\#JDLE2023\#TeamEduc\#MastoProf},
  langid = {french},
  organization = {{Mastodon}},
  keywords = {fediverse,markdown,mastodon,non lu},
  file = {/home/igor/Zotero/storage/7FYFNED6/110158455766516456.html}
}

@unpublished{eyssetteUtiliserMarkdownPour2023a,
  title = {Utiliser le markdown pour tout faire},
  author = {Eyssette, Cédric},
  date = {2023},
  url = {https://eyssette.forge.aeif.fr/marp-slides/slides/2022-2023/utiliser-le-markdown-pour-tout-faire},
  urldate = {2023-04-17},
  langid = {fre},
  keywords = {non lu},
  file = {/home/igor/Zotero/storage/I7CTE3X5/utiliser-le-markdown-pour-tout-faire.html}
}

@online{fressinaudGuideMarkdown2022,
  type = {Site d'une application web},
  title = {Guide Markdown},
  author = {Fressinaud, Marien},
  date = {2022-07-11},
  url = {https://flus.fr/carnet/markdown.html},
  urldate = {2023-04-17},
  abstract = {Un guide simple pour apprendre Markdown.},
  langid = {fre},
  organization = {{flus}},
  keywords = {documentation,markdown,non lu},
  file = {/home/igor/Zotero/storage/96VGWVYV/markdown.html}
}

@article{graysonMarkdownDynamicInterface2022,
  title = {R {{Markdown}} as a Dynamic Interface for Teaching: {{Modules}} from Math and Biology Classrooms.},
  author = {Grayson, Kristine L. and Hilliker, Angela K. and Wares, Joanna R.},
  date = {2022-07},
  journaltitle = {Math Biosci},
  volume = {349},
  eprint = {35623397},
  eprinttype = {pmid},
  pages = {108844},
  location = {{United States}},
  issn = {1879-3134 0025-5564},
  doi = {10.1016/j.mbs.2022.108844},
  abstract = {Advancing technologies, including interactive tools, are changing classroom pedagogy across academia. Here, we discuss the R Markdown interface, which allows  for the creation of partial or complete interactive classroom modules for courses  using the R programming language. R Markdown files mix sections of R code with  formatted text, including LaTeX, which are rendered together to form complete  reports and documents. These features allow instructors to create classroom  modules that guide students through concepts, while providing areas for coding  and text response by students. Students can also learn to create their own  reports for more independent assignments. After presenting the features and uses  of R Markdown to enhance teaching and learning, we present examples of materials  from two courses. In a Computational Modeling course for math students, we used R  Markdown to guide students through exploring mathematical models to understand  the principle of herd immunity. In a Data Visualization and Communication course  for biology students, we used R Markdown for teaching the fundamentals of R  programming and graphing, and for students to learn to create reproducible data  investigations. Through these examples, we demonstrate the benefits of R Markdown  as a dynamic teaching and learning tool.},
  langid = {english},
  pmcid = {PMC9487201},
  keywords = {*Learning,*Students,Biology/education,cmu,Data visualization,Herd immunity,Humans,markdown,non lu,Pedagogy,pubmed,R markdown,Teaching programming},
  file = {/home/igor/Zotero/storage/JQGHBHB5/Grayson et al_2022_R Markdown as a dynamic interface for teaching.pdf}
}

@online{gruberMarkdown2004,
  type = {Site personnel},
  title = {Markdown},
  author = {Gruber, John},
  date = {2004-12-17},
  url = {https://daringfireball.net/projects/markdown/},
  urldate = {2023-04-17},
  langid = {english},
  organization = {{Daring Fireball}},
  keywords = {documentation,markdown,non lu},
  file = {/home/igor/Zotero/storage/J7WP5B3D/markdown.html}
}

@article{hershbergJBrowseRInterfaceJBrowse2021,
  title = {{{JBrowseR}}: An {{R}} Interface to the {{JBrowse}} 2 Genome Browser.},
  author = {Hershberg, Elliot A. and Stevens, Garrett and Diesh, Colin and Xie, Peter and De Jesus Martinez, Teresa and Buels, Robert and Stein, Lincoln and Holmes, Ian},
  date = {2021-11-05},
  journaltitle = {Bioinformatics},
  volume = {37},
  number = {21},
  eprint = {34196689},
  eprinttype = {pmid},
  pages = {3914--3915},
  location = {{England}},
  issn = {1367-4811 1367-4803},
  doi = {10.1093/bioinformatics/btab459},
  abstract = {MOTIVATION: Genome browsers are an essential tool in genome analysis. Modern genome browsers enable complex and interactive visualization of a wide variety of  genomic data modalities. While such browsers are very powerful, they can be  challenging to configure and program for bioinformaticians lacking expertise in  web development. RESULTS: We have developed an R package that provides an  interface to the JBrowse 2 genome browser. The package can be used to configure  and customize the browser entirely with R code. The browser can be deployed from  the R console, or embedded in Shiny applications or R Markdown documents.  AVAILABILITY AND IMPLEMENTATION: JBrowseR is available for download from CRAN,  and the source code is openly available from the Github repository at  https://github.com/GMOD/JBrowseR/.},
  langid = {english},
  pmcid = {PMC8570803},
  keywords = {*Genome,*Genomics,cmu,markdown,non lu,pubmed,R markdown,Software},
  file = {/home/igor/Zotero/storage/LYEIF748/Hershberg et al_2021_JBrowseR.pdf}
}

@article{jaglaSCHNAPPsSingleCell2021,
  title = {{{SCHNAPPs}} - {{Single Cell sHiNy APPlication}}(s).},
  author = {Jagla, Bernd and Libri, Valentina and Chica, Claudia and Rouilly, Vincent and Mella, Sebastien and Puceat, Michel and Hasan, Milena},
  date = {2021-12},
  journaltitle = {J Immunol Methods},
  volume = {499},
  eprint = {34742775},
  eprinttype = {pmid},
  pages = {113176},
  location = {{Netherlands}},
  issn = {1872-7905 0022-1759},
  doi = {10.1016/j.jim.2021.113176},
  abstract = {Single-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues.  Data produced by scRNAseq is technically complex and requires analytical  workflows that are an active field of bioinformatics research, whereas a wealth  of biological background knowledge is needed to guide the investigation. Thus,  there is an increasing need to develop applications geared towards  bench-scientists to help them abstract the technical challenges of the analysis  so that they can focus on the science at play. It is also expected that such  applications should support closer collaboration between bioinformaticians and  bench-scientists by providing reproducible science tools. We present SCHNAPPs, a  Graphical User Interface (GUI), designed to enable bench-scientists to  autonomously explore and interpret scRNAseq data and associated annotations. The  R/Shiny-based application allows following different steps of scRNAseq analysis  workflows from Seurat or Scran packages: performing quality control on cells and  genes, normalizing the expression matrix, integrating different samples,  dimension reduction, clustering, and differential gene expression analysis.  Visualization tools for exploring each step of the process include violin plots,  2D projections, Box-plots, alluvial plots, and histograms. An R-markdown report  can be generated that tracks modifications and selected visualizations. The  modular design of the tool allows it to easily integrate new visualizations and  analyses by bioinformaticians. We illustrate the main features of the tool by  applying it to the characterization of T cells in a scRNAseq and Cellular  Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) experiment of  two healthy individuals.},
  langid = {english},
  keywords = {*Sequence Analysis; RNA,*Single-Cell Analysis,*Software,CITE-Seq,cmu,Humans,Leukocytes; Mononuclear/*cytology/immunology,markdown,multi-omics data analysis,non lu,pubmed,R markdown,scRNA-seq,Shiny application},
  file = {/home/igor/Zotero/storage/PEARJ76P/Jagla et al_2021_SCHNAPPs - Single Cell sHiNy APPlication(s).pdf}
}

@article{kariyawasamDashboardstyleInteractivePlots2021,
  title = {Dashboard-Style Interactive Plots for {{RNA-seq}} Analysis Are {{R Markdown}} Ready with {{Glimma}} 2.0.},
  author = {Kariyawasam, Hasaru and Su, Shian and Voogd, Oliver and Ritchie, Matthew E. and Law, Charity W.},
  date = {2021-12},
  journaltitle = {NAR Genom Bioinform},
  volume = {3},
  number = {4},
  eprint = {34988439},
  eprinttype = {pmid},
  pages = {lqab116},
  location = {{England}},
  issn = {2631-9268},
  doi = {10.1093/nargab/lqab116},
  abstract = {Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma  that brings improved interactivity and reproducibility using high-level  visualization frameworks for R and JavaScript. Glimma 2.0 plots are now readily  embeddable in R Markdown, thus allowing users to create reproducible reports  containing interactive graphics. The revamped multidimensional scaling plot  features dashboard-style controls allowing the user to dynamically change the  colour, shape and size of sample points according to different experimental  conditions. Interactivity was enhanced in the MA-style plot for comparing  differences to average expression, which now supports selecting multiple genes,  export options to PNG, SVG or CSV formats and includes a new volcano plot  function. Feature-rich and user-friendly, Glimma makes exploring data for gene  expression analysis more accessible and intuitive and is available on  Bioconductor and GitHub.},
  langid = {english},
  pmcid = {PMC8693569},
  keywords = {cmu,markdown,non lu,pubmed,R markdown},
  file = {/home/igor/Zotero/storage/ZQD2WXNN/Kariyawasam et al_2021_Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready.pdf}
}

@online{pandocWritingThesisThinking2023,
  type = {Mastodon post},
  title = {Writing a \#thesis? {{Thinking}} about Doing It in \#{{Markdown}}? {{No}} Problem, {{Tom Pollard}} Has You Covered.{{https://github.com/tompollard/phd\_thesis\_ma…}}},
  shorttitle = {Writing a \#thesis?},
  author = {{pandoc}, (@pandoc@fosstodon.org)},
  date = {2023-04-12},
  url = {https://fosstodon.org/@pandoc/110184589069297715},
  urldate = {2023-04-17},
  abstract = {Writing a \#thesis? Thinking about doing it in \#Markdown? No problem, Tom Pollard has you covered.https://github.com/tompollard/phd\_thesis\_markdown},
  langid = {english},
  organization = {{Mastodon}},
  keywords = {fediverse,markdown,mastodon,non lu,pandoc,Publication scientifique},
  file = {/home/igor/Zotero/storage/T354LYSW/110184589069297715.html}
}

@online{perretFormatTexte2022,
  type = {Site personnel},
  title = {Format texte},
  author = {Perret, Arthur},
  date = {2022-12-11},
  url = {https://www.arthurperret.fr/cours/format-texte.html},
  urldate = {2023-04-18},
  abstract = {Cette page explique ce qu’est le format texte et donne des arguments en faveur de son utilisation. Elle ouvre vers d’autres ressources pour explorer l’écosystème du format texte.},
  langid = {fre},
  organization = {{Arthur Perret}},
  keywords = {markdown,non lu,plain text},
  note = {\begin{quotation}

\par
[C]’est un fichier qui ne contient des caractères. (Perret, 2022)
\par
\end{quotation}},
  file = {/home/igor/Zotero/storage/3IM8K4DU/format-texte.html}
}

@online{perretMarkdown2022,
  type = {Site personnel},
  title = {Markdown},
  author = {Perret, Arthur},
  date = {2022-12-11},
  publisher = {{arthurperret.fr/}},
  url = {https://www.arthurperret.fr/cours/markdown.html},
  urldate = {2023-04-17},
  abstract = {Cette page explique ce qu’est Markdown, le plus populaire des langages de balisage léger, et renvoie vers un tutoriel interactif en français.},
  langid = {french},
  organization = {{Arthur Perret}},
  keywords = {documentation,markdown,non lu},
  file = {/home/igor/Zotero/storage/HZNSDQ7S/markdown.html}
}

@software{pollardTemplateWritingPhD2023,
  title = {Template for Writing a {{PhD}} Thesis in {{Markdown}}},
  author = {Pollard, Tom},
  date = {2023-04-17T04:40:26Z},
  origdate = {2015-02-10T10:32:25Z},
  url = {https://github.com/tompollard/phd_thesis_markdown},
  urldate = {2023-04-17},
  abstract = {Template for writing a PhD thesis in Markdown},
  keywords = {markdown,non lu,pandoc,Publication scientifique,thesis-template}
}

@article{schneiderFacilitatingOpenScience2022,
  title = {Facilitating Open Science Practices for Research Syntheses: {{PreregRS}} Guides Preregistration.},
  author = {Schneider, Jürgen and Backfisch, Iris and Lachner, Andreas},
  date = {2022-03},
  journaltitle = {Res Synth Methods},
  volume = {13},
  number = {2},
  eprint = {34921744},
  eprinttype = {pmid},
  pages = {284--289},
  location = {{England}},
  issn = {1759-2887 1759-2879},
  doi = {10.1002/jrsm.1540},
  abstract = {Researchers increasingly engage in adopting open science practices in the field of research syntheses, such as preregistration. Preregistration is a central open  science practice in empirical research to enhance transparency in the research  process and it gains steady adoption in the context of conducting research  synthesis. From an interdisciplinary perspective, frameworks and particularly  templates are lacking which support researchers preparing a preregistration. To  this end, we introduce preregRS, a template to guide researchers across  disciplines through the process of preregistering research syntheses. We utilized  an R Markdown template file to provide a framework that structures the process of  preparing a preregistration. Researchers can write up the preregistration using  the template file similar to filling out a form, with the template providing  additional hints and further information for the decisions along the framework.  We integrated the R Markdown template in an R package for easy installation and  use, but also provide a browser-based option for users granting low-barrier  access. PreregRS constitutes a first step to facilitate and support  preregistration with research syntheses for all disciplines. It further adds to  establishing open science practices in conducting research syntheses.},
  langid = {english},
  keywords = {*Surveys and Questionnaires,cmu,Empirical Research,markdown,non lu,open science,preregistration,pubmed,R markdown,R package}
}

@article{yuVeridicalDataScience2020,
  title = {Veridical Data Science.},
  author = {Yu, Bin and Kumbier, Karl},
  date = {2020-02-25},
  journaltitle = {Proc Natl Acad Sci U S A},
  volume = {117},
  number = {8},
  eprint = {32054788},
  eprinttype = {pmid},
  pages = {3920--3929},
  location = {{United States}},
  issn = {1091-6490 0027-8424},
  doi = {10.1073/pnas.1901326117},
  abstract = {Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability  (PCS) framework for veridical data science. Our framework, composed of both a  workflow and documentation, aims to provide responsible, reliable, reproducible,  and transparent results across the data science life cycle. The PCS workflow uses  predictability as a reality check and considers the importance of computation in  data collection/storage and algorithm design. It augments predictability and  computability with an overarching stability principle. Stability expands on  statistical uncertainty considerations to assess how human judgment calls impact  data results through data and model/algorithm perturbations. As part of the PCS  workflow, we develop PCS inference procedures, namely PCS perturbation intervals  and PCS hypothesis testing, to investigate the stability of data results relative  to problem formulation, data cleaning, modeling decisions, and interpretations.  We illustrate PCS inference through neuroscience and genomics projects of our own  and others. Moreover, we demonstrate its favorable performance over existing  methods in terms of receiver operating characteristic (ROC) curves in  high-dimensional, sparse linear model simulations, including a wide range of  misspecified models. Finally, we propose PCS documentation based on R Markdown or  Jupyter Notebook, with publicly available, reproducible codes and narratives to  back up human choices made throughout an analysis. The PCS workflow and  documentation are demonstrated in a genomics case study available on Zenodo.},
  langid = {english},
  pmcid = {PMC7049126},
  keywords = {cmu,computation,data science,markdown,non lu,prediction,pubmed,R markdown,stability},
  file = {/home/igor/Zotero/storage/GWVCYDA6/Yu_Kumbier_2020_Veridical data science.pdf}
}