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.


References in zbMATH (referenced in 25 articles )

Showing results 1 to 20 of 25.
Sorted by year (citations)

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  1. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016)
  2. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  3. Hofner, Benjamin; Schmid, Matthias; Edler, Lutz: Reproducible research in statistics: a review and guidelines for the Biometrical Journal (2016)
  4. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016)
  5. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  6. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  7. Matthias Templ; Alexander Kowarik; Bernhard Meindl: Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro (2015)
  8. Ewald, Roland; Uhrmacher, Adelinde M.: SESSL: a domain-specific language for simulation experiments (2014)
  9. Philip Leifeld: texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables (2013)
  10. Toby Hocking; Thomas Wutzler; Keith Ponting; Philippe Grosjean: Sustainable, Extensible Documentation Generation Using inlinedocs (2013)
  11. Beyersmann, Jan; Allignol, Arthur; Schumacher, Martin: Competing risks and multistate models with R (2012)
  12. Lenth, Russell; Højsgaard, Søren: Reproducible statistical analysis with multiple languages (2011)
  13. Rufibach, Kaspar: Selection models with monotone weight functions in meta analysis (2011)
  14. Vasishth, Shravan; Broe, Michael: The foundations of statistics: A simulation-based approach (2011)
  15. Horton, Nicholas J.; Kleinman, Ken: Using R for data management, statistical analysis, and graphics. (2010)
  16. Baggerly, Keith A.; Coombes, Kevin R.: Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology (2009)
  17. Eckel, Sandrah P.; Peng, Roger D.: Interacting with local and remote data repositories using the stashR package (2009)
  18. Falcon, Seth: Caching code chunks in dynamic documents. The weaver package (2009)
  19. Göhlmann, Hinrich; Talloen, Willem: Gene expression studies using Affymetrix microarrays. (2009)
  20. Sawitzki, Günther: Computational statistics. An introduction to R (2009)

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