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

Keywords for this software

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References in zbMATH (referenced in 335 articles )

Showing results 1 to 20 of 335.
Sorted by year (citations)

1 2 3 ... 15 16 17 next

  1. Allévius, Benjamin; Höhle, Michael: An unconditional space-time scan statistic for ZIP-distributed data (2019)
  2. Amalan Mahendran; Pushpakanthie Wijekoon: fitODBOD: An R Package to Model Binomial Outcome Data using Binomial Mixture and Alternate Binomial Distributions (2019) not zbMATH
  3. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  4. Athey, Susan; Tibshirani, Julie; Wager, Stefan: Generalized random forests (2019)
  5. Belkić, Dževad: All the trinomial roots, their powers and logarithms from the Lambert series, Bell polynomials and Fox-Wright function: illustration for genome multiplicity in survival of irradiated cells (2019)
  6. Chatterjee, Snigdhansu: The scale enhanced wild bootstrap method for evaluating climate models using wavelets (2019)
  7. Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Vialaneix: Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics (2019) arXiv
  8. Diggle, Peter J.; Giorgi, Emanuele: Model-based geostatistics for global public health. Methods and applications (2019)
  9. Emmanuel Caron, Jérôme Dedecker, Michel Bertrand: Linear regression with stationary errors : the R package slm (2019) arXiv
  10. Francisco Bischoff, Pedro Pereira Rodrigues: tsmp: An R Package for Time Series with Matrix Profile (2019) arXiv
  11. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  12. Hadrien Lorenzo, Jérôme Saracco, Rodolphe Thiébaut: Supervised Learning for Multi-Block Incomplete Data (2019) arXiv
  13. Hay-Jahans, Christopher: R companion to elementary applied statistics (2019)
  14. Hyeyoung Maeng, Piotr Fryzlewicz: Detecting linear trend changes and point anomalies in data sequences (2019) arXiv
  15. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  16. Mai, Qing; Yang, Yi; Zou, Hui: Multiclass sparse discriminant analysis (2019)
  17. Michael Messer: Bivariate change point detection: joint detection of changes in expectation and variance (2019) arXiv
  18. Mihai Tivadar: OasisR: An R Package to Bring Some Order to the World of Segregation Measurement (2019) not zbMATH
  19. Paul F. Petrowski; Elizabeth G. King; Timothy M. Beissinger: An R Framework for the Partitioning of Linkage Disequilibrium between and Within Populations (2019) not zbMATH
  20. Picheny, Victor; Binois, Mickael; Habbal, Abderrahmane: A Bayesian optimization approach to find Nash equilibria (2019)

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