References in zbMATH (referenced in 20 articles )

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

  1. H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez: Wrangling multivariate spatio-temporal data with the R package cubble (2022) arXiv
  2. Adrian Richter; Carsten Oliver Schmidt; Markus Krüger; Stephan Struckmann: dataquieR: assessment of data quality in epidemiological research (2021) not zbMATH
  3. Peder Bacher, Hjörleifur G. Bergsteinsson, Linde Frölke, Mikkel L. Sørensen, Julian Lemos-Vinasco, Jon Liisberg, Jan Kloppenborg Møller, Henrik Aalborg Nielsen, Henrik Madsen: onlineforecast: An R package for adaptive and recursive forecasting (2021) arXiv
  4. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  5. Chambaz, Antoine; Benkeser, David: A ride in targeted learning territory (2020)
  6. Cousineau, Denis: How many decimals? Rounding descriptive and inferential statistics based on measurement precision (2020)
  7. Haim Bar, HaiYing Wang: Reproducible Science with LaTeX (2020) arXiv
  8. Irizarry, Rafael A.: Introduction to data science. Data analysis and prediction algorithms with R (2020)
  9. Laa, Ursula; Cook, Dianne: Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics (2020)
  10. Sayani Gupta, Rob J Hyndman, Dianne Cook, Antony Unwin: Visualizing probability distributions across bivariate cyclic temporal granularities (2020) arXiv
  11. Anne Petersen; Claus Ekstrøm: dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R (2019) not zbMATH
  12. Becker, Gabriel; Moore, Sara E.; Lawrence, Michael: trackr: a framework for enhancing discoverability and reproducibility of data visualizations and other artifacts in R (2019)
  13. David Miller and Eric Rexstad and Len Thomas and Laura Marshall and Jeffrey Laake: Distance Sampling in R (2019) not zbMATH
  14. Coeurjolly, Jean-Francois; Porcu, Emilio: Fast and exact simulation of complex-valued stationary Gaussian processes through embedding circulant matrix (2018)
  15. Martijn Tennekes: tmap: Thematic Maps in R (2018) not zbMATH
  16. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  17. Templ, Matthias: Statistical disclosure control for microdata. Methods and applications in R (2017)
  18. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  19. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  20. Gandrud, Christopher: Reproducible research with R and RStudio (2014)