55 lines
2.5 KiB
Org Mode
55 lines
2.5 KiB
Org Mode
#+BEGIN_HTML
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<img width="200px" align="right" position="absolute" style="position: absolute; top: 0; right: 0; border: 0;" src="https://raw.githubusercontent.com/hollorol/RBBGCMuso/master/images/logo.jpg" alt="Fork me on GitHub">
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#+END_HTML
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* The RBBGCMuso Package
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#+AUTHOR: Roland HOLLÓS, Dóra HIDY, Zoltán BARCZA
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RBBGCMuso is an R package which helps you to use the [[http://agromo.agrar.mta.hu/bbgc/][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.
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** Installation
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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.
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*** Installation in Windows
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You can allways install the latest RBBGCMuso by copy the following line into the R console
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#+BEGIN_SRC R :eval no
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source("https://raw.githubusercontent.com/hollorol/RBBGCMuso/master/installWin.R")
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#+END_SRC
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*** Installation in Linux or from Source
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If you want to install the RBBGCMuso package in Linux, you have several ways.
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1) Clone this repository, and build and run the package (further information here: [[http://kbroman.org/pkg_primer/pages/build.html][package build and install]])
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2) Install devtools package and copy the following line into an R session
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#+BEGIN_SRC R :eval no
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devtools::install_github("hollorol/RBBGCMuso/RBBGCMuso")
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#+END_SRC
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Please note, that the last point also works in Windows after you installed the [[https://cran.r-project.org/web/packages/devtools/index.html][devtools]] package and [[https://cran.r-project.org/bin/windows/Rtools/][Rtools]].
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** Quick usage
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*** Running the model
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*** Visualization of the model output
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*** Study the effect of ecophysiological parameters using parameterSweep
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*** Monte-Carlo experiments
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*** Sensitivity analysis
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*** Perform Quick experiments
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** Advanced usage
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*** copyMusoExampleTo
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*** setupMuso
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*** musoData
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*** musoMapping
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*** musoMappingFind
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*** spinupMuso
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*** normalMuso
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*** calibMuso
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*** plotMuso
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*** plotMusoWithData
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*** musoQuckEffect
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*** musoMonte
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*** musoSensi
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** Contact
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** Acknowledgements
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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).
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