Unverified Commit d4191241 authored by Romain Reuillon's avatar Romain Reuillon Committed by GitHub
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Merge pull request #5 from merriam/merriam-patch-1

Minor README typos.
parents 5c6dedf5 e1a5f27f
......@@ -17,13 +17,13 @@ All the accepted submissions will be part of the market place and will be compil
## Available workflows ##
- [Advanced methods](https://github.com/guillaumecherel/TutorialEAForModelling): Advanced methods for calibrating, validating and analyzing complex systems models.
- [Advanced methods](https://github.com/guillaumecherel/TutorialEAForModelling): advanced methods for calibrating, validating and analyzing complex systems models.
- [Ants model](ants): a NetLogo model calibrated using the Evolutionary/Genetic Algorithms.
- [Fire simulation](fire): a fire simulation model in Netlogo with a design of experiments studying its density factor.
- [FSL segmentation](fsl-fast): brain segmentation using FSL.
- [Java hello wold](java-hello): an example of how to embed Java code in OpenMOLE.
- [OpenMOLE plugin](hello-plugin): two workflows using two different OpenMOLE plugins.
- [Pi Monte Carlo approximation](pi): a workflow using a ScalaTask to approximate the value of Pi. The Design of Experiments changes the seed of the random number generator.
- [R hello world](R-hello): a *Hello world* in R ranging over 100 of different inputs.
- [Fire simulation](fire): a fire simulation model in NetLogo with a design of experiments studying its density factor
- [FSL segmentation](fsl-fast): brain segmentation using [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki)
- [Java hello world](java-hello): an example of how to embed Java code in OpenMOLE
- [OpenMOLE plugin](hello-plugin): two workflows using two different OpenMOLE plugins
- [Pi Monte Carlo approximation](pi): a workflow using a ScalaTask to approximate the value of pi. The Design of Experiments changes the seed of the random number generator.
- [R hello world](R-hello): an example of how to embed R code in OpenMole. This workflow executes an R program with 100 different inputs, makes a computation, and saves to a file.
- [Random Forest classifier](randomforest): This workflow explores the parameters of a random forest image classifier written in Python using scikit-learn.
- [SimPopLocal model](simpoplocal): A geographical model calibrated using genetic algorithms.
- [SimPopLocal model](simpoplocal): a geographical model calibrated using genetic algorithms.
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