An Object-Oriented Framework for Statistical Simulation: The R Package simFrame. Simulation studies are widely used by statisticians to gain insight into the quality of developed methods. Usually some guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary in order to draw meaningful conclusions. The R package simFrame is an object-oriented framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with a minimal effort of programming. Its object-oriented implementation provides clear interfaces for extensions by the user. Since statistical simulation is an embarrassingly parallel process, the framework supports parallel computing to increase computational performance. Furthermore, an appropriate plot method is selected automatically depending on the structure of the simulation results. In this paper, the implementation of simFrame is discussed in great detail and the functionality of the framework is demonstrated in examples for different simulation designs.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Ann-Kristin Kreutzmann; Sören Pannier; Natalia Rojas-Perilla; Timo Schmid; Matthias Templ; Nikos Tzavidis: The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators (2019) not zbMATH
  2. Oleg Sofrygin; Mark van der Laan; Romain Neugebauer: simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data (2017) not zbMATH
  3. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  4. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Robust groupwise least angle regression (2016)
  5. Marius Hofert; Martin Mächler: Parallel and Other Simulations in R Made Easy: An End-to-End Study (2016) not zbMATH
  6. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Sparse least trimmed squares regression for analyzing high-dimensional large data sets (2013)
  7. Andreas Alfons; Matthias Templ: Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken (2013) not zbMATH
  8. Alfons, Andreas; Baaske, Wolfgang E.; Filzmoser, Peter; Mader, Wolfgang; Wieser, Roland: Robust variable selection with application to quality of life research (2011)
  9. Alfons, Andreas; Kraft, Stefan; Templ, Matthias; Filzmoser, Peter: Simulation of close-to-reality population data for household surveys with application to EU-SILC (2011)
  10. Andreas Alfons; Matthias Templ; Peter Filzmoser: An Object-Oriented Framework for Statistical Simulation: The R Package simFrame (2010) not zbMATH