Snow: A parallel computing framework for the R system. This paper presents a simple parallel computing framework for the statistical programming language R. The system focuses on parallelization of familiar higher level mapping functions and emphasizes simplicity of use in order to encourage adoption by a wide range of R users. The paper describes the design and implementation of the system, outlines examples of its use, and presents some possible directions for future developments.

References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  2. Bauer, Verena; Fürlinger, Karl; Kauermann, Göran: A note on parallel sampling in Markov graphs (2019)
  3. González, Miguel; Gutiérrez, Cristina; Martínez, Rodrigo; Minuesa, Carmen; del Puerto, Inés M.: Bayesian analysis for controlled branching processes (2016)
  4. Kustosz, Christoph P.; Leucht, Anne; Müller, Christine H.: Tests based on simplicial depth for AR(1) models with explosion (2016)
  5. Perera, Harsha; Davis, Jack; Swartz, Tim B.: Optimal lineups in Twenty20 cricket (2016)
  6. Gondro, Cedric: Primer to analysis of genomic data using R (2015)
  7. Zucknick, Manuela; Saadati, Maral; Benner, Axel: Nonidentical twins: comparison of frequentist and Bayesian Lasso for Cox models (2015)
  8. Ahn, Jae Youn; Shyamalkumar, Nariankadu D.: Asymptotic theory for the empirical Haezendonck-Goovaerts risk measure (2014)
  9. Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias: Model-based boosting in R: a hands-on tutorial using the R package mboost (2014)
  10. Karl, Andrew T.; Eubank, Randy; Milovanovic, Jelena; Reiser, Mark; Young, Dennis: Using rngstreams for parallel random number generation in C++ and R (2014)
  11. Kastner, Gregor; Frühwirth-Schnatter, Sylvia: Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models (2014)
  12. Rasch, D.; Spangl, B.; Wang, M.: Minimal experimental size in the three way ANOVA cross classification model with approximate F-tests (2012)
  13. Ahn, Jae Youn; Shyamalkumar, Nariankadu D.: Large sample behavior of the CTE and VaR estimators under importance sampling (2011)
  14. Banicescu, Ioana; Cariño, Ricolindo L.; Harvill, Jane L.; Lestrade, John Patrick: Investigating asymptotic properties of vector nonlinear time series models (2011)
  15. Eugster, Manuel J. A.; Knaus, Jochen; Porzelius, Christine; Schmidberger, Markus; Vicedo, Esmeralda: Hands-on tutorial for parallel computing with R (2011)
  16. Ahn, Jae Youn; Shyamalkumar, Nariankadu D.: An asymptotic analysis of the bootstrap bias correction for the empirical CTE (2010)
  17. Bjornson, Robert D.; Carriero, Nicholas J.; Schultz, Martin H.; Shields, Patrick M.; Weston, Stephen B.: NetWorkSpace: A coordination system for high-productivity environments (2009)
  18. Ko, Bangwon; Russo, Ralph P.; Shyamalkumar, Nariankadu D.: A note on nonparametric estimation of the CTE (2009)
  19. Tierney, Luke; Rossini, A. J.; Li, Na: \textttSnow: A parallel computing framework for the R system (2009)
  20. Harvill, Jane L.; Ray, Bonnie K.: Functional coefficient autoregressive models for vector time series (2006)