R
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/
This software is also referenced in ORMS.
This software is also referenced in ORMS.
Keywords for this software
References in zbMATH (referenced in 7694 articles , 6 standard articles )
Showing results 1 to 20 of 7694.
Sorted by year (- Ramachandran, Kandethody M.; Tsokos, Chris P.: Mathematical statistics with applications in R (2021)
- Aaron R. Wolen; Chris H.J. Hartgerink; Ryan Hafen; Brian G. Richards; Courtney K. Soderberg; Timothy P. York: osfr: An R Interface to the Open Science Framework (2020) not zbMATH
- Abid, Rahma; Kokonendji, Célestin C.; Masmoudi, Afif: Geometric Tweedie regression models for continuous and semicontinuous data with variation phenomenon (2020)
- Alan V. Di Vittorio , Chris R. Vernon, Shijie Shu: Moirai Version 3: A Data Processing System to Generate Recent Historical Land Inputs for Global Modeling Applications at Various Scales (2020) not zbMATH
- Albert, Jim; Hu, Jingchen: Probability and Bayesian modeling (2020)
- Al-Gounmeein, Remal Shaher; Ismail, Mohd Tahir: Forecasting the exchange rate of the Jordanian dinar versus the US dollar using a Box-Jenkins seasonal ARIMA model (2020)
- Alodat, M. T.; Shakhatreh, Mohammed K.: Gaussian process regression with skewed errors (2020)
- Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
- Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola, Peter Gething, Daniel J. Weiss: disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling (2020) arXiv
- Annarosa Quarello, Olivier Bock, Emilie Lebarbier: A new segmentation method for the homogenisation of GNSS-derived IWV time-series (2020) arXiv
- Arashi, M.; Nadarajah, S.: A paradoxical argument about domination (2020)
- Aravkin, Aleksandr; Davis, Damek: Trimmed statistical estimation via variance reduction (2020)
- Ascione, Giacomo; Leonenko, Nikolai; Pirozzi, Enrica: Fractional Erlang queues (2020)
- Aslam, Muhammad; Ali, Sajid; Yousaf, Rahila; Shah, Ismail: Mixture of transmuted Pareto distribution: properties, applications and estimation under Bayesian framework (2020)
- Bagdonavičius, Vilijandas; Petkevičius, Linas: A new multiple outliers identification method in linear regression (2020)
- Bailer, A. John: Statistical programming in SAS (2020)
- Bakar, K. Shuvo: Interpolation of daily rainfall data using censored Bayesian spatially varying model (2020)
- Bartok, Larissa; Burzler, Matthias A.: How to assess rater rankings? A theoretical and a simulation approach using the sum of the pairwise absolute row differences (PARDs) (2020)
- Basellini, Ugofilippo; Kjærgaard, Søren; Camarda, Carlo Giovanni: An age-at-death distribution approach to forecast cohort mortality (2020)
- Basso, S.; Ceselli, Alberto; Tettamanzi, Andrea: Random sampling and machine learning to understand good decompositions (2020)
Further publications can be found at: http://journal.r-project.org/