book.bib

https://bookdown.org/yihui/bookdown/citations.html

https://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html

-References in alphabetical order of the key ----------------------------------


@BOOK{Bryan2016-yy,
  title    = "Happy Git and {GitHub} for the useR",
  author   = "Bryan, Jenny",
  abstract = "Using Git and GitHub with R, Rstudio, and R Markdown",
  month    =  jun,
  year     =  2016
}


@BOOK{Daniel2013-qq,
  title     = "Biostatistics: A foundation for analysis in the health sciences",
  author    = "Daniel, Wayne W and Cross, Chad L",
  publisher = "Wiley",
  edition   = "Tenth",
  month     =  jan,
  year      =  2013,
  address   = "Hoboken, NJ"
}


@BOOK{Field2013-zo,
  title     = "Discovering statistics using {R}",
  author    = "Field, Andy and Miles, Jeremy and Field, Zoe",
  publisher = "Sage",
  month     =  dec,
  year      =  2013
}


@MISC{GitHub2022-iw,
  title        = "Licensing a repository",
  booktitle    = "{GitHub} Docs",
  author       = "{GitHub}",
  abstract     = "Public repositories on GitHub are often used to share open
                  source software. For your repository to truly be open source,
                  you'll need to license it so that others are free to use,
                  change, and distribute the software.",
  month        =  may,
  year         =  2022,
  howpublished = "\url{https://ghdocs-prod.azurewebsites.net/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/licensing-a-repository}",
  note         = "Accessed: 2022-5-15",
  language     = "en"
}


@BOOK{Grolemund2017-qp,
  title    = "{R} for Data Science",
  author   = "Grolemund, Garrett and Wickham, Hadley",
  abstract = "This book will teach you how to do data science with R: You'll
              learn how to get your data into R, get it into the most useful
              structure, transform it, visualise it and model it. In this book,
              you will find a practicum of skills for data science. Just as a
              chemist learns how to clean test tubes and stock a lab, you'll
              learn how to clean data and draw plots---and many other things
              besides. These are the skills that allow data science to happen,
              and here you will find the best practices for doing each of these
              things with R. You'll learn how to use the grammar of graphics,
              literate programming, and reproducible research to save time.
              You'll also learn how to manage cognitive resources to facilitate
              discoveries when wrangling, visualising, and exploring data.",
  month    =  jan,
  year     =  2017
}


@BOOK{Hernan2020-uu,
  title     = "Causal Inference: What If",
  author    = "Hern{\'a}n, Miguel A and Robins, James M",
  publisher = "CRC Press",
  month     =  jul,
  year      =  2020
}


@MISC{Ismay2019-iw,
  title        = "Chapter 1 Getting Started with Data in {R}",
  booktitle    = "Statistical Inference via Data Science",
  author       = "Ismay, Chester and Kim, Albert Y",
  abstract     = "An open-source and fully-reproducible electronic textbook for
                  teaching statistical inference using tidyverse data science
                  tools.",
  month        =  nov,
  year         =  2019,
  howpublished = "\url{https://moderndive.com/}",
  note         = "Accessed: 2020-5-12"
}


@BOOK{Lash2021-mb,
  title     = "Modern Epidemiology",
  author    = "Lash, Timothy L and VanderWeel, Tyler J and Haneuse, Sebastien
               and Rothman, Kenneth J",
  publisher = "Wolters Kluwer",
  edition   = "fourth",
  year      =  2021
}


@BOOK{Matthews2014-ah,
  title     = "Successful Scientific Writing: A {Step-by-Step} Guide for the
               Biological and Medical Sciences",
  author    = "Matthews, Janice R and Matthews, Robert W",
  abstract  = "Thoroughly revised and updated, the new edition of this
               acclaimed and best-selling guide offers a rich blend of
               practical advice and real-life examples. The authors draw on
               fifty years of experience, providing detailed step-by-step
               guidance designed to help students and researchers write and
               present scientific manuscripts more successfully through
               knowledge, practice, and an efficient approach. Retaining the
               user-friendly style of the previous editions, this fourth
               edition has been broadened to include detailed information
               relevant to today's digital world. It covers all aspects of the
               writing process, from first drafts, literature retrieval, and
               authorship to final drafts and electronic publication. A new
               section provides extensive coverage of ethical issues, from
               plagiarism and dual publication to honesty in reporting
               statistics. Both the text and 30 hands-on exercises include
               abundant examples applicable to a variety of writing contexts,
               making this a powerful tool for researchers and students across
               a range of disciplines.",
  publisher = "Cambridge University Press",
  month     =  nov,
  year      =  2014,
  language  = "en"
}


@INBOOK{noauthor_2023-pr,
  title     = "Epistemology",
  booktitle = "{Merriam-Webster} Dictionary",
  abstract  = "The meaning of EPISTEMOLOGY is the study or a theory of the
               nature and grounds of knowledge especially with reference to its
               limits and validity.",
  publisher = "Merriam-Webster",
  month     =  oct,
  year      =  2023,
  language  = "en"
}


@MISC{Oregon_State_University2017-jv,
  title        = "What are good file naming conventions?",
  booktitle    = "Web Technology Training",
  author       = "{Oregon State University}",
  abstract     = "Spaces in File Names Generally, in the computer science and
                  IT worlds, it is typically frowned on when files, web
                  addresses - or really any kind of programming at all - are
                  named with spaces inside of the title. The reasoning behind
                  this is very simple. Empty space signifies the ``end'' of a
                  character string. Spaces inside of a URL or a linked file
                  basically generate a faulty syntax that the server reads as
                  the end of a character string. The server sees ``the end''
                  and stops processing. When the full string is not processed,
                  it can not be properly represented on your computer screen.",
  month        =  jul,
  year         =  2017,
  howpublished = "\url{https://webtech.training.oregonstate.edu/faq/what-are-good-file-naming-conventions}",
  note         = "Accessed: 2020-5-14"
}


@BOOK{Pearl2016-lu,
  title     = "Causal Inference in Statistics: A Primer",
  author    = "Pearl, Judea and Glymour, Madelyn and Jewell, Nicholas P",
  publisher = "John Wiley \& Sons Ltd",
  year      =  2016
}


@BOOK{Pearl2018-im,
  title     = "The Book of Why: The New Science of Cause and Effect",
  author    = "Pearl, Judea and Mackenzie, Dana",
  abstract  = "A Turing Award-winning computer scientist and statistician shows
               how understanding causality has revolutionized science and will
               revolutionize artificial intelligence``Correlation is not
               causation.'' This mantra, chanted by scientists for more than a
               century, has led to a virtual prohibition on causal talk. Today,
               that taboo is dead. The causal revolution, instigated by Judea
               Pearl and his colleagues, has cut through a century of confusion
               and established causality--the study of cause and effect--on a
               firm scientific basis. His work explains how we can know easy
               things, like whether it was rain or a sprinkler that made a
               sidewalk wet; and how to answer hard questions, like whether a
               drug cured an illness. Pearl's work enables us to know not just
               whether one thing causes another: it lets us explore the world
               that is and the worlds that could have been. It shows us the
               essence of human thought and key to artificial intelligence.
               Anyone who wants to understand either needs The Book of Why.",
  publisher = "Basic Books",
  month     =  may,
  year      =  2018,
  language  = "en"
}


@ARTICLE{Peng2011-bg,
  title    = "Reproducible research in computational science",
  author   = "Peng, Roger D",
  abstract = "Computational science has led to exciting new developments, but
              the nature of the work has exposed limitations in our ability to
              evaluate published findings. Reproducibility has the potential to
              serve as a minimum standard for judging scientific claims when
              full independent replication of a study is not possible.",
  journal  = "Science",
  volume   =  334,
  number   =  6060,
  pages    = "1226--1227",
  month    =  dec,
  year     =  2011,
  language = "en"
}


@ARTICLE{Peng2021-xk,
  title    = "Reproducible Research: A Retrospective",
  author   = "Peng, Roger D and Hicks, Stephanie C",
  abstract = "Advances in computing technology have spurred two extraordinary
              phenomena in science: large-scale and high-throughput data
              collection coupled with the creation and implementation of
              complex statistical algorithms for data analysis. These two
              phenomena have brought about tremendous advances in scientific
              discovery but have raised two serious concerns. The complexity of
              modern data analyses raises questions about the reproducibility
              of the analyses, meaning the ability of independent analysts to
              recreate the results claimed by the original authors using the
              original data and analysis techniques. Reproducibility is
              typically thwarted by a lack of availability of the original data
              and computer code. A more general concern is the replicability of
              scientific findings, which concerns the frequency with which
              scientific claims are confirmed by completely independent
              investigations. Although reproducibility and replicability are
              related, they focus on different aspects of scientific progress.
              In this review, we discuss the origins of reproducible research,
              characterize the current status of reproducibility in public
              health research, and connect reproducibility to current concerns
              about the replicability of scientific findings. Finally, we
              describe a path forward for improving both the reproducibility
              and replicability of public health research in the future.",
  journal  = "Annu. Rev. Public Health",
  volume   =  42,
  pages    = "79--93",
  month    =  apr,
  year     =  2021,
  keywords = "data analysis; replicability; reproducibility",
  language = "en"
}


@BOOK{Porta2008-ij,
  title     = "A Dictionary of Epidemiology",
  editor    = "Porta, Miquel",
  publisher = "Oxford University Press",
  year      =  2008
}



@MANUAL{R_Development_Core_Team2020-sj,
  title    = "An Introduction to {R}",
  author   = "{R Development Core Team}",
  abstract = "An Introduction to R",
  month    =  apr,
  year     =  2020
}


% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Rothman1976-vw,
  title     = "Causes",
  author    = "Rothman, K J",
  abstract  = "The conceptual framework for causes presented here is intended
               neither as a review nor an expansion of knowledge, but rather as
               a viewpoint which bridges the gap between metaphysical notions
               of cause and basic epidemiologic parameters. The focus, then, is
               …",
  journal   = "Am. J. Epidemiol.",
  publisher = "academic.oup.com",
  volume    =  104,
  number    =  6,
  pages     = "587--592",
  month     =  dec,
  year      =  1976,
  language  = "en"
}


@MISC{RStudio2020-fe,
  title        = "{RStudio}",
  booktitle    = "{RStudio}",
  author       = "{RStudio}",
  abstract     = "Take control of your R code",
  year         =  2020,
  howpublished = "\url{https://rstudio.com/products/rstudio/}",
  note         = "Accessed: 2020-5-15"
}


@MISC{RStudio2020-yx,
  title        = "{R} Markdown",
  booktitle    = "{RStudio}",
  author       = "{RStudio}",
  abstract     = "Turn your analyses into high quality documents, reports,
                  presentations and dashboards with R Markdown. Use a
                  productive notebook interface to weave together narrative
                  text and code to produce elegantly formatted output. Use
                  multiple languages including R, Python, and SQL. R Markdown
                  supports a reproducible workflow for dozens of static and
                  dynamic output formats including HTML, PDF, MS Word, Beamer,
                  HTML5 slides, Tufte-style handouts, books, dashboards, shiny
                  applications, scientific articles, websites, and more.",
  year         =  2020,
  howpublished = "\url{https://rmarkdown.rstudio.com/}",
  note         = "Accessed: 2020-5-17"
}


@MISC{RStudio2021-zl,
  title        = "{FAQ}: Tips for writing R-related questions",
  booktitle    = "{RStudio} Community",
  author       = "{RStudio}",
  abstract     = "First and foremost, please ask! A core goal of community is
                  to be a friendly place to chat about topics related to data
                  science, R, and RStudio. We know that posting to technical
                  forums can be intimidating. But know that many here would
                  love to see you overcome your inhibition and engage with us.
                  Here are a few tips some folks here think might be helpful.
                  Before you post Check Out R Documentation - R has built in
                  documentation on packages and functions. For example typing
                  ?lm into your R...",
  month        =  sep,
  year         =  2021,
  howpublished = "\url{https://community.rstudio.com/t/faq-tips-for-writing-r-related-questions/6824}",
  note         = "Accessed: 2022-1-14",
  language     = "en"
}


@MISC{Stack_Overflow2022-ga,
  title        = "What are tags, and how should {I} use them?",
  author       = "{Stack Overflow}",
  month        =  jan,
  year         =  2022,
  howpublished = "\url{https://stackoverflow.com/help/tagging}",
  note         = "Accessed: 2022-1-10"
}


@MISC{Stack_Overflow2022-uc,
  title        = "How do {I} ask a good question?",
  booktitle    = "Stack Overflow",
  author       = "{Stack Overflow}",
  month        =  jan,
  year         =  2022,
  howpublished = "\url{https://stackoverflow.com/help/how-to-ask}",
  note         = "Accessed: 2022-1-14"
}


@BOOK{Westreich2019-lg,
  title     = "Epidemiology by Design",
  author    = "Westreich, Daniel",
  publisher = "Oxford University Press",
  month     =  nov,
  year      =  2019,
  address   = "New York"
}


@ARTICLE{Wickham2014-gy,
  title    = "Tidy Data",
  author   = "Wickham, Hadley",
  abstract = "A huge amount of effort is spent cleaning data to get it ready
              for analysis, but there has been little research on how to make
              data cleaning as easy and effective as possible. This paper
              tackles a small, but important, component of data cleaning: data
              tidying. Tidy datasets are easy to manipulate, model and
              visualize, and have a specific structure: each variable is a
              column, each observation is a row, and each type of observational
              unit is a table. This framework makes it easy to tidy messy
              datasets because only a small set of tools are needed to deal
              with a wide range of un-tidy datasets. This structure also makes
              it easier to develop tidy tools for data analysis, tools that
              both input and output tidy datasets. The advantages of a
              consistent data structure and matching tools are demonstrated
              with a case study free from mundane data manipulation chores.",
  journal  = "Journal of Statistical Software, Articles",
  volume   =  59,
  number   =  10,
  pages    = "1--23",
  year     =  2014
}


@INCOLLECTION{Wickham2019-yt,
  title     = "Style guide",
  booktitle = "Advanced {R}",
  author    = "Wickham, Hadley",
  month     =  apr,
  year      =  2019
}


@MANUAL{Wickham2020-dx,
  title    = "Programming with dplyr",
  author   = "Wickham, Hadley and Fran{\c c}ois, Romain and Henry, Lionel and
              M{\"u}ller, Kirill and {RStudio}",
  abstract = "Most dplyr verbs use ``tidy evaluation'', a special type of
              non-standard evaluation. In this vignette, you'll learn the two
              basic forms, data masking and tidy selection, and how you can
              program with them using either functions or for loops.",
  year     =  2020
}


@INCOLLECTION{Wickham2023-ta,
  title     = "Workflow: code style",
  booktitle = "{R} for Data Science",
  author    = "Wickham, Hadley and {\c C}etinkaya-Rundel, Mine and Grolemund,
               Garrett",
  edition   = "second",
  month     =  jul,
  year      =  2023
}


@BOOK{Xie2020-st,
  title    = "{R} Markdown: The Definitive Guide",
  author   = "Xie, Yihui and Allaire, J J and Grolemund, Garrett",
  abstract = "The first official book authored by the core R Markdown
              developers that provides a comprehensive and accurate reference
              to the R Markdown ecosystem. With R Markdown, you can easily
              create reproducible data analysis reports, presentations,
              dashboards, interactive applications, books, dissertations,
              websites, and journal articles, while enjoying the simplicity of
              Markdown and the great power of R and other languages.",
  month    =  apr,
  year     =  2020
}