multicore: Parallel processing of R code on machines with multiple cores or CPUs This package provides a way of running parallel computations in R on machines with multiple cores or CPUs. Jobs can share the entire initial workspace and it provides methods for results collection.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- McKinley, Trevelyan J.; Morters, Michelle; Wood, James L.N.: Bayesian model choice in cumulative link ordinal regression models (2015)
- Hieke, Stefanie; Binder, Harald; Nieters, Alexandra; Schumacher, Martin: minPtest: a resampling based gene region-level testing procedure for genetic case-control studies (2014)
- Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias: Model-based boosting in R: a hands-on tutorial using the R package mboost (2014)
- Karl, Andrew T.; Eubank, Randy; Milovanovic, Jelena; Reiser, Mark; Young, Dennis: Using rngstreams for parallel random number generation in C++ and R (2014)
- Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
- Haller, Bernhard; Schmidt, Georg; Ulm, Kurt: Applying competing risks regression models: an overview (2013)
- Eugster, Manuel J.A.; Knaus, Jochen; Porzelius, Christine; Schmidberger, Markus; Vicedo, Esmeralda: Hands-on tutorial for parallel computing with R (2011)
- Schmidberger, Markus; Vicedo, Esmeralda; Mansmann, Ulrich: Empirical study for the agreement between statistical methods in quality assessment and control of microarray data (2011)
- Hothorn, Torsten; Bühlmann, Peter; Kneib, Thomas; Schmid, Matthias; Hofner, Benjamin: Model-based boosting 2.0 (2010)