| docs | ||
| images | ||
| RBBGCMuso | ||
| untestedFunctions | ||
| .gitignore | ||
| Development_branch.md | ||
| forarcheologists | ||
| installWin.R | ||
| LICENSE | ||
| RBBGCMuso_0.6.1.zip | ||
| README.org | ||
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The RBBGCMuso Package
RBBGCMuso is an R package which helps you to use the Biome-BGCMuSo biogeochemical model in R environment. It also provides some additional tools for the model such as MuSo optimized Monte-Carlo simulation and global sensitivity analysis. If you want to use the framework, please read the following description.
Installation
You can install the RBBGCMuso package in several ways depending on the operating system you use. Now, RBBGCMuso is tested only in Linux and Windows, so OS X compatibility cannot be granted yet. In Windows you can use install the package from binary or from source installer. In Linux you can only install from source.
Installation in Windows
You can allways install the latest RBBGCMuso by copy the following line into the R console
source("https://raw.githubusercontent.com/hollorol/RBBGCMuso/master/installWin.R")Installation in Linux or from Source
If you want to install the RBBGCMuso package in Linux, you have several ways.
- Clone this repository, and build and run the package (further information here: package build and install)
- Install devtools package and copy the following line into an R session
devtools::install_github("hollorol/RBBGCMuso/RBBGCMuso")Please note, that the last point also works in Windows after you installed the devtools package and Rtools.
Quick usage
Preparation
In order to use the RBBGCMuso framework you have to set up the m environment, as you normally would if you use the modell without the framework. Please read the corresponding documentation at the Biome-BGCMuSo's actual userguide
Running the model
You can run the model in spinup, in normal, or in both phase. With calibMuso functcion, you are able to execute the the model in both or in a normal phase.
Visualization of the model output
Study the effect of ecophysiological parameters using parameterSweep
Monte-Carlo experiments
Sensitivity analysis
Perform Quick experiments
Advanced usage
copyMusoExampleTo
setupMuso
musoData
musoMapping
musoMappingFind
spinupMuso
normalMuso
calibMuso
plotMuso
plotMusoWithData
musoQuckEffect
musoMonte
musoSensi
Contact
Acknowledgements
The research was funded by the Széchenyi 2020 programme, the European Regional Development Fund and the Hungarian Government (GINOP-2.3.2-15-2016-00028).