Sweave

Sweave is a tool that allows to embed the R code for complete data analyses in latex documents. The purpose is to create dynamic reports, which can be updated automatically if data or analysis change. Instead of inserting a prefabricated graph or table into the report, the master document contains the R code necessary to obtain it. When run through R, all data analysis output (tables, graphs, etc.) is created on the fly and inserted into a final latex document. The report can be automatically updated if data or analysis change, which allows for truly reproducible research.

Keywords for this software

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References in zbMATH (referenced in 44 articles )

Showing results 1 to 20 of 44.
Sorted by year (citations)

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  1. Haim Bar, HaiYing Wang: Reproducible Science with LaTeX (2020) arXiv
  2. Michael J. Kane, Simon Urbanek: On the Programmatic Generation of Reproducible Documents (2020) arXiv
  3. Shi, Lei; Feng, Xiaoliang; Qi, Longxing; Xu, Yanlong; Zhai, Sulan: Modeling and predicting the influence of PM(_2.5) on children’s respiratory diseases (2020)
  4. Jeffrey Andrews; Jaymeson Wickins; Nicholas Boers; Paul McNicholas: teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution (2018) not zbMATH
  5. Pedro M. Valero Mora: bookdown: Authoring Books and Technical Documents with R Markdown (2018) not zbMATH
  6. Coelho, L.P.: Jug: Software for Parallel Reproducible Computation in Python (2017) not zbMATH
  7. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016) not zbMATH
  8. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  9. Hofner, Benjamin; Schmid, Matthias; Edler, Lutz: Reproducible research in statistics: a review and guidelines for the Biometrical Journal (2016)
  10. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016) not zbMATH
  11. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  12. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  13. Kendall, Wilfrid S.: Introduction to coupling-from-the-past using R (2015)
  14. Matthias Templ; Alexander Kowarik; Bernhard Meindl: Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro (2015) not zbMATH
  15. Ewald, Roland; Uhrmacher, Adelinde M.: SESSL: a domain-specific language for simulation experiments (2014)
  16. Gandrud, Christopher: Reproducible research with R and RStudio (2014)
  17. Philip Leifeld: texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables (2013) not zbMATH
  18. Toby Hocking; Thomas Wutzler; Keith Ponting; Philippe Grosjean: Sustainable, Extensible Documentation Generation Using inlinedocs (2013) not zbMATH
  19. Beyersmann, Jan; Allignol, Arthur; Schumacher, Martin: Competing risks and multistate models with R (2012)
  20. Gerlinde Dinges; Alexander Kowarik; Bernhard Meindl; Matthias Templ: An Open Source Approach for Modern Teaching Methods: The Interactive TGUI System (2011) not zbMATH

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Further publications can be found at: https://www.stat.uni-muenchen.de/~leisch/papers/fl-publications.html