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 6281 articles , 6 standard articles )
Showing results 1 to 20 of 6281.
Sorted by year (- Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
- Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
- Alfaro, Esteban (ed.); Gámez, Matías (ed.); García, Noelia (ed.): Ensemble classification methods with applications in R (2019)
- Allévius, Benjamin; Höhle, Michael: An unconditional space-time scan statistic for ZIP-distributed data (2019)
- Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
- Amrei Stammann, Daniel Czarnowske: Binary Choice Models with High-Dimensional Individual and Time Fixed Effects (2019) arXiv
- Anderson, David F.; Higham, Desmond J.; Leite, Saul C.; Williams, Ruth J.: On constrained Langevin equations and (bio)chemical reaction networks (2019)
- Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
- Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
- Andrew Thomas Jones, Hien Duy Nguyen, Jessica Juanita Bagnall: BoltzMM: an R package for maximum pseudolikelihoodestimation of fully-visible Boltzmann machines (2019) not zbMATH
- Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
- Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv
- Athey, Susan; Tibshirani, Julie; Wager, Stefan: Generalized random forests (2019)
- Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
- Ben Bond-Lamberty, Kalyn Dorheim, Ryna Cui, Russell Horowitz, Abigail Snyder, Katherine Calvin, Leyang Feng, Rachel Hoesly, Jill Horing, G. Page Kyle, Robert Link, Pralit Patel, Christopher Roney, Aaron Staniszewski, Sean Turner, Min Chen, Felip Feijoo, Corinne Hartin, Mohamad Hejazi, Gokul Iyer, Sonny Kim, Yaling Liu, Cary Lynch, Haewon McJeon, Steven Smith, Stephanie Waldhoff, Marshall Wise, Leon Clarke : Ben Bond-Lamberty , Kalyn Dorheim, Ryna Cui, Russell Horowitz, Abigail Snyder, Katherine Calvin, Leyang Feng, Rachel Hoesly, Jill Horing, G. Page Kyle, Robert Link, Pralit Patel, Christopher Roney, Aaron Staniszewski, Sean Turner, Min Chen, Felip Feijoo, Corinne Hartin, Mohamad Hejazi, Gokul Iyer, Sonny Kim, Yaling Liu, Cary Lynch, Haewon McJeon, Steven Smith, Stephanie Waldhoff, Marshall Wise, Leon Clarke (2019) not zbMATH
- Berrar, Daniel; Lopes, Philippe; Dubitzky, Werner: Incorporating domain knowledge in machine learning for soccer outcome prediction (2019)
- Blitzstein, Joseph K.; Hwang, Jessica: Introduction to probability (2019)
- Blostein, Martin; Miljkovic, Tatjana: On modeling left-truncated loss data using mixtures of distributions (2019)
- Bogomolov, Marina; Davidov, Ori: Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models (2019)
- Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
Further publications can be found at: http://journal.r-project.org/