NOTE: Development continues in the new package batchtools
To cite BatchJobs or BatchExperiments in publications use:
Bernd Bischl, Michel Lang, Olaf Mersmann, Joerg Rahnenfuehrer, Claus Weihs (2015). BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments. Journal
of Statistical Software, 64(11), 1-25. URL https://www.jstatsoft.org/v64/i11/.
A BibTeX entry for LaTeX users is
@Article{,
title = {{BatchJobs} and {BatchExperiments}: Abstraction Mechanisms for Using {R} in Batch Environments},
author = {Bernd Bischl and Michel Lang and Olaf Mersmann and J{\"o}rg Rahnenf{\"u}hrer and Claus Weihs},
journal = {Journal of Statistical Software},
year = {2015},
volume = {64},
number = {11},
pages = {1--25},
url = {https://www.jstatsoft.org/v64/i11/},
}
- Extends BatchJobs with functionality required for comprehensive computer experiments
- Abstraction to link algorithms to problems and thereby define computer experiments
- Associate statistical designs with parameters of problems and algorithms
- Support for statistical replications of experiments
- Collect parameters and results into a clearly represented data frame with one simple function call
- Extend your study later with further problems, algorithms, parameters or replications
- Clear separation between all stages: your methods under consideration, experiment defintions and execution layer.
- Readable and succinct code for your experiments
- Internally handled seeds guarantee reproducibility
To install the latest stable release from CRAN:
install.packages("BatchExperiments")To install the development version use devtools:
library(devtools);
install_github("tudo-r/BatchJobs")
install_github("tudo-r/BatchExperiments")Please see the documentation of BatchJobs to set up your system for parallel execution. Currently the best introduction to the package is our technical report. For more detailed information on the functions please use the R documentation. You can also peek into some examples provided in the wiki.
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