cran

R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. Please consult the R project homepage for further information. CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use the CRAN mirror nearest to you to minimize network load


References in zbMATH (referenced in 224 articles )

Showing results 1 to 20 of 224.
Sorted by year (citations)

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  1. Fasiolo, Matteo; Wood, Simon N.; Hartig, Florian; Bravington, Mark V.: An extended empirical saddlepoint approximation for intractable likelihoods (2018)
  2. Fong, Christian; Hazlett, Chad; Imai, Kosuke: Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements (2018)
  3. Imani, Mahdi; Braga-Neto, Ulisses M.: Particle filters for partially-observed Boolean dynamical systems (2018)
  4. Perrot-Dockès, Marie; Lévy-Leduc, Céline; Sansonnet, Laure; Chiquet, Julien: Variable selection in multivariate linear models with high-dimensional covariance matrix estimation (2018)
  5. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  6. Shin, Minsuk; Bhattacharya, Anirban; Johnson, Valen E.: Scalable Bayesian variable selection using nonlocal prior densities in ultrahigh-dimensional settings (2018)
  7. Adam Kaplan, Eric F. Lock: Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival (2017) arXiv
  8. Amir Nikooienejad, Wenyi Wang, Valen E. Johnson: Bayesian Variable Selection in High Dimensional Survival Time Cancer Genomic Datasets using Nonlocal Priors (2017) arXiv
  9. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  10. Ashley Petersen, Noah Simon, Daniela Witten: SCALPEL: Extracting Neurons from Calcium Imaging Data (2017) arXiv
  11. Bilodeau, Martin; Nangue, Aurélien Guetsop: Tests of mutual or serial independence of random vectors with applications (2017)
  12. Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
  13. Chakar, S.; Lebarbier, E.; Lévy-Leduc, C.; Robin, S.: A robust approach for estimating change-points in the mean of an $\mathrmAR(1)$ process (2017)
  14. Chang, Jinyuan; Zhou, Wen; Zhou, Wen-Xin; Wang, Lan: Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering (2017)
  15. Chrapary, Hagen; Dalitz, Wolfgang; Neun, Winfried; Sperber, Wolfram: Design, concepts, and state of the art of the swMATH service (2017)
  16. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
  17. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  18. Frederik Vissing Mikkelsen, Niels Richard Hansen: Learning Large Scale Ordinary Differential Equation Systems (2017) arXiv
  19. Giacobino, Caroline; Sardy, Sylvain; Diaz-Rodriguez, Jairo; Hengartner, Nick: Quantile universal threshold (2017)
  20. Gillian M. Raab, Beata Nowok, Chris Dibben: Guidelines for Producing Useful Synthetic Data (2017) arXiv

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Further publications can be found at: http://journal.r-project.org/