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* The RBBGCMuso Package
#+AUTHOR: Roland HOLLÓS, Dóra HIDY, Zoltán BARCZA
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.
RBBGCMuso is an R package which supports the easy but powerful application of 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 Bione-BGCMuSo optimized Monte-Carlo simulation and global sensitivity analysis. If you would like 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.
You can install the RBBGCMuso package in several ways depending on the operating system you use. Up to now RBBGCMuso was tested only in Linux and Windows environment, so Mac OS X compatibility cannot be granted yet. In MS Windows you can install the package from binary or from source installer. In Linux you can only install the software from source.
*** Installation in Windows
You can allways install the latest RBBGCMuso by copy the following line into the R console
You can always install the latest RBBGCMuso by copying the following line into the R console (using R or R Studio):
#+BEGIN_SRC R :eval no
source("https://raw.githubusercontent.com/hollorol/RBBGCMuso/master/installWin.R")
#+END_SRC
*** Installation in Linux or from Source
If you want to install the RBBGCMuso package in Linux, you have several ways.
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]])
2) Install devtools package and copy the following line into an R session
*** Installation in Linux or from Source in Linux or Windows
If you would like to install the RBBGCMuso package in Linux environment, you have two options.
a) Clone this repository, then build and run the package (further information is available here: [[http://kbroman.org/pkg_primer/pages/build.html][package build and install]])
OR
b) Install the devtools package firts:
#+BEGIN_SRC R :eval no
install.packages("devtools")
#+END_SRC
Then copy the following line into the R session and execute it:
#+BEGIN_SRC R :eval no
devtools::install_github("hollorol/RBBGCMuso/RBBGCMuso")
#+END_SRC
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]].
Please note that the last point also works in Windows after you have installed the [[https://cran.r-project.org/bin/windows/Rtools/][Rtools]] Windows software.
** 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 [[http://agromo.agrar.mta.hu/bbgc/files/Manual_BBGC_MuSo_v5.pdf][Biome-BGCMuSo's actual userguide]]
In order to use the RBBGCMuso framework, you have to set up the environment, as you would normally do if you use the model without the RBBGCMuso framework. Please read the corresponding documentation at the [[http://agromo.agrar.mta.hu/bbgc/files/Manual_BBGC_MuSo_v5.pdf][Biome-BGCMuSo's actual user's guide]]
*** 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.
You can run the model in spinup, in normal, or in both phase (including the so-called transient run). Using the so-called calibMuso functcion you will be able to execute the the model in both spinup or normal phase.
*** Visualization of the model output
*** Study the effect of ecophysiological parameters using parameterSweep