R: A language and environment for statistical computing.

By R. Team. 2014

348,290 citations recorded by the source corpus.

Subfields: Statistics and Probability, Software Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Stefan Wager
2. Susan Athey
3. Paul-Christian Burkner
4. Cynthia Rudin
5. Peter Buhlmann
6. Andrew Gelman
7. Jan Peters
8. Kosuke Imai

In Software Engineering

1. R. Kaplan
2. Weiming Yan
3. David M. Condon
4. Hang Sun
5. R. Kays
6. D. Piñero
7. G. Manzano-García
8. Paolo D'Odorico

Citations in the corpus

• Interpreting Classifiers through Attribute Interactions in Datasets (2017, methodology): “All experiments were run in R (R Core Team, 2015) and our method is released as the ASTRID R-package, available for download 3 .”
• Forest Floor Visualizations of Random Forests (2016, neutral): “To demonstrate that the visualizations in the result section 4 provide correct representations of the data structure, it is beneficial to use simulated (toy) data from a given hidden function.”
• Interpretable classification models for recidivism prediction (2015, methodology): “We trained all methods other than SLIM using publicly available packages in R 3.1.1 (R Core Team 2014) without imposing any time constraints.”
• Kernel‐based tests for joint independence (2016, methodology): “Fn(x1,...,xn)−1(1−α) o. 4 Implementation We now discuss an efficient implementation ofthe proposed tests and briefly comment on the choice of kernel. All methods are available for the R-language (R Core Team, 2014) as package dHSIC. 4.1 dHSIC estimator An efficient implementation of the dHSIC\ estimator ”
• Causal inference by using invariant prediction: identification and confidence intervals (2015, methodology): “The methods are available in the package InvariantCausalPrediction for the R language (R Core Team, 2014).”
• Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “We trained SLIM scoring systems using the CPLEX 12.6.0 API and models with baseline methods using publicly available packages in R 3.1.1 (R Core Team, 2014).”
• Local Linear Forests (2018, methodology): “The popular core R function loess (R Core Team 2019) allows only 1–4 predictors, while locfit (Loader 2013) crashes on the simulation from (1) with d ≥ 7.)”
• Structural Basis of Dual Specificity of Sinorhizobium meliloti Clr, a cAMP and cGMP Receptor Protein (2023, methodology): “Sequences flanking each center of a peak derived from the ChIP-seq analysis were defined and summarized as FASTA sequences using the R package (82).”
• Stan: A Probabilistic Programming Language (2017, methodology): “The sampler and its configuration are described at greater length in the manual (Stan Development Team 2014).”