RJreferences.bib

@article{Boettiger2015,
  abstract =     {As computational work becomes more and more integral
                  to many aspects of scientific research,
                  computational reproducibility has become an issue of
                  increasing importance to computer systems
                  researchers and domain scientists alike. Though
                  computational reproducibility seems more straight
                  forward than replicating physical experiments, the
                  complex and rapidly changing nature of computer
                  environments makes being able to reproduce and
                  extend such work a serious challenge. In this paper,
                  I explore common reasons that code developed for one
                  research project cannot be successfully executed or
                  extended by subsequent researchers. I review current
                  approaches to these issues, including virtual
                  machines and workflow systems, and their
                  limitations. I then examine how the popular emerging
                  technology Docker combines several areas from
                  systems research - such as operating system
                  virtualization, cross-platform portability, modular
                  re-usable elements, versioning, and a 'DevOps'
                  philosophy, to address these challenges. I
                  illustrate this with several examples of Docker use
                  with a focus on the R statistical environment.},
  archivePrefix ={arXiv},
  arxivId =      {arXiv:1410.0846v1},
  author =       {Boettiger, Carl},
  doi =          {10.1145/2723872.2723882},
  eprint =       {arXiv:1410.0846v1},
  file =         {:home/cboettig/Documents/Mendeley/Boettiger - 2015 -
                  An introduction to Docker for reproducible research,
                  with examples from the R environment.pdf:pdf},
  journal =      {ACM SIGOPS Operating Systems Review},
  keywords =     {Systems,reproducible research},
  number =       1,
  pages =        {71--79},
  title =        {{An introduction to Docker for reproducible
                  research, with examples from the R environment}},
  url =          {https://dl.acm.org/citation.cfm?id=2723882
                  http://arxiv.org/abs/1410.0846},
  volume =       49,
  year =         2015
}

@article{Clark2014,
  author =       {Clark, Dav and Culich, Aaron and Hamlin, Brian and
                  Lovett, Ryan},
  file =         {:Users/cboettig/Library/Application Support/Mendeley
                  Desktop/Downloaded/Clark et al. - 2014 - BCE
                  Berkeley's Common Scientific Compute Environment for
                  Research and Education.pdf:pdf},
  journal =      {Proceedings of the 13th Python in Science Conference
                  (SciPy 2014)},
  mendeley-groups ={Future of Science},
  pages =        {1--8},
  title =        {{BCE: Berkeley's Common Scientific Compute
                  Environment for Research and Education}},
  year =         2014
}

@inproceedings{Jetstream,
  address =      {New York, New York, USA},
  author =       {Stewart, Craig A and Turner, George and Vaughn,
                  Matthew and Gaffney, Niall I. and Cockerill, Timothy
                  M and Foster, Ian and Hancock, David and Merchant,
                  Nirav and Skidmore, Edwin and Stanzione, Daniel and
                  Taylor, James and Tuecke, Steven},
  booktitle =    {Proceedings of the 2015 XSEDE Conference on
                  Scientific Advancements Enabled by Enhanced
                  Cyberinfrastructure - XSEDE '15},
  doi =          {10.1145/2792745.2792774},
  file =         {:home/cboettig/Downloads/a29-stewart.pdf:pdf},
  isbn =         9781450337205,
  keywords =     {atmosphere,big data,by the,cloud computing,committee
                  on future directions,emphasized in the report,for
                  nsf advanced computing,long tail of
                  science,resources has recently been},
  mendeley-groups ={Math Methods and Model Inference},
  pages =        {1--8},
  publisher =    {ACM Press},
  title =        {{Jetstream: A self-provisioned, scalable science and
                  engineering cloud environment}},
  url =          {http://dl.acm.org/citation.cfm?doid=2792745.2792774},
  year =         2015
}

@article{Mine,
  title =        {Infrastructure and tools for teaching computing
                  throughout the statistical curriculum},
  author =       {Cetinkaya-Rundel, Mine and Rundel, Colin W},
  year =         2017,
  month =        aug,
  keywords =     {R markdown, git / github, reproducibility, data
                  science, workflow, R language, Continuous
                  integration, RStudio, teaching, cirriculum},
  abstract =     { Modern statistics is fundamentally a computational
                  discipline, but too often this fact is not reflected
                  in our statistics curricula. With the rise of big
                  data and data science it has become increasingly
                  clear that students both want, expect, and need
                  explicit training in this area of the
                  discipline. Additionally, recent curricular
                  guidelines clearly state that working with data
                  requires extensive computing skills and that
                  statistics students should be fluent in accessing,
                  manipulating, analyzing, and modeling with
                  professional statistical analysis software. Much has
                  been written in the statistics education literature
                  about pedagogical tools and approaches to provide a
                  practical computational foundation for
                  students. This article discusses the computational
                  infrastructure and toolkit choices to allow for
                  these pedagogical innovations while minimizing
                  frustration and improving adoption for both our
                  students and instructors.  },
  volume =       5,
  pages =        {e3181v1},
  journal =      {PeerJ Preprints},
  issn =         {2167-9843},
  url =          {https://doi.org/10.7287/peerj.preprints.3181v1},
  doi =          {10.7287/peerj.preprints.3181v1}
}

@Manual{R,
  title =        {R: A Language and Environment for Statistical
                  Computing},
  author =       {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address =      {Vienna, Austria},
  year =         2012,
  note =         {{ISBN} 3-900051-07-0},
  url =          {http://www.R-project.org/},
}

@MISC{apt_pinning,
  url =          {https://wiki.debian.org/AptPreferences},
  Title =        {APT-preferences overview},
  Year =         2017,
  Author =       {{Debian Project}}
}

@MISC{best_practices,
  url =          {https://docs.docker.com/engine/userguide/eng-image/dockerfile_best-practices/},
  Title =        {Best practices for writing {Dockerfiles}},
  Author =       {Docker},
  Year =         2017
}

@MISC{data8,
  author =       {{UC Berkeley}},
  url =          {http://data.berkeley.edu/education/curriculum-overview},
  canonicaluri = {\url{http://data.berkeley.edu/education/curriculum-overview}},
  title =        {{Curriculum Overview} | {Division of Data Sciences}},
  YEAR =         2017,
  archived =
                  {http://greycite.knowledgeblog.org/?uri=http%3A%2F%2Fdata.berkeley.edu%2Feducation%2Fcurriculum-overview},
}

@MISC{debian_security,
  url =          {https://www.debian.org/releases/testing/},
  Title =        {Debian Release information for "testing"},
  Author =       {{Debian Project}},
  Year =         2017
}

@MISC{edd2014,
  url =          {https://ropensci.org/blog/blog/2014/10/23/introducing-rocker},
  author =       {Carl Boettiger and Dirk Eddelbuettel},
  title =        {{Introducing Rocker}: {Docker for R}},
  YEAR =         2014,
  archived =     {http://greycite.knowledgeblog.org/?uri=https%3A%2F%2Fropensci.org%2Fblog%2Fblog%2F2014%2F10%2F23%2Fintroducing-rocker}
}

@MISC{edd_sanitizers,
  url =          {http://dirk.eddelbuettel.com/code/sanitizers.html},
  title =        {sanitizers},
  YEAR =         2014,
  author =       {Eddelbuettel, Dirk},
  archived =     {http://greycite.knowledgeblog.org/?uri=http%3A%2F%2Fdirk.eddelbuettel.com%2Fcode%2Fsanitizers.html},
}

@article{ihaka:1996,
  Author =       {Ihaka, Ross and Gentleman, Robert},
  Journal =      {Journal of Computational and Graphical Statistics},
  Number =       3,
  Pages =        {299--314},
  Title =        {R: A Language for Data Analysis and Graphics},
  Volume =       5,
  Year =         1996
}

@MISC{jags,
  author =       {Martyn Plummer},
  title =        {{JAGS}: {A} program for analysis of {Bayesian}
                  graphical models using {Gibbs} sampling},
  note =         {Version 4.3.0},
  url =          {http://mcmc-jags.sourceforge.net/},
  year =         2017
}

@Manual{littler,
  title =        {littler: R at the Command-Line via 'r'},
  author =       {Dirk Eddelbuettel and Jeff Horner},
  year =         2017,
  note =         {R package version 0.3.2},
  url =          {https://CRAN.R-project.org/package=littler},
}

@misc{mran,
  author =       {{Revolution Analytics}},
  url =          {https://mran.microsoft.com},
  title =        {{Microsoft R Application Network}},
  year =         2017
}

@MISC{oci,
  url =          {https://www.opencontainers.org/},
  Title =        {{The Open Container Initiative}},
  Author =       {{The Linux Foundation: Projects}},
  Year =         2017
}

@Manual{packrat,
  title =        {packrat: A Dependency Management System for Projects
                  and their R Package Dependencies},
  author =       {Kevin Ushey and Jonathan McPherson and Joe Cheng and
                  Aron Atkins and JJ Allaire},
  year =         2016,
  note =         {R package version 0.4.8-1},
  url =          {https://CRAN.R-project.org/package=packrat},
}

@Manual{rhub,
  year =         2017,
  title =        {rhub: Connect to 'R-hub', from 'R'},
  author =       {Gábor Csárdi},
  note =         {R package version 1.0.1},
  url =          {https://CRAN.R-project.org/package=rhub},
}

@Manual{s6,
  title =        {s6: skarnet.org's small and secure supervision
                  software suite},
  author =       {Laurent Bercot},
  year =         2017,
  url =          {https://skarnet.org/software/s6/},
}

@MISC{shifter,
  author =       {{Lawrence Berkeley National Laboratories}},
  url =          {https://github.com/NERSC/shifter},
  title =        {Shifter},
  YEAR =         2017,
}

@MISC{singularity,
  author =       {{Lawrence Berkeley National Laboratories}},
  url =          {http://singularity.lbl.gov/},
  title =        {Singularity},
  YEAR =         2017,
}

@Manual{tidyverse,
  title =        {tidyverse: Easily Install and Load 'Tidyverse'
                  Packages},
  author =       {Hadley Wickham},
  year =         2017,
  note =         {R package version 1.1.1},
  url =          {https://CRAN.R-project.org/package=tidyverse},
}

@MISC{what-docker,
  url =          {https://www.docker.com/what-docker},
  title =        {What is {Docker}?},
  journal =      {Docker},
  YEAR =         2015,
  archived =     {http://greycite.knowledgeblog.org/?uri=https%3A%2F%2Fwww.docker.com%2Fwhat-docker},
  author =       {Docker}
}

@MISC{wholetale,
  url =          {https://wholetale.org/},
  Title =        {{The Whole Tale}: Merging Science and
                  Cyberinfrastructure Pathways},
  Author =       {Bertram Ludaescher and Kyle Chard and Matthew Turk
                  and Victoria Stodden and Niall Gaffney},
  Year =         2017
}