From 317d0c76e2750117e5d8cbf46ecdb49f32619730 Mon Sep 17 00:00:00 2001 From: hollorol Date: Wed, 12 Dec 2018 11:35:53 +0100 Subject: [PATCH] Update README.org --- README.org | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/README.org b/README.org index e165bbe..f632f36 100644 --- a/README.org +++ b/README.org @@ -7,11 +7,27 @@ 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. ** 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 -*** Istallation in Linux -*** Installation from Source +You can allways install the latest RBBGCMuso by copy the following line into the R console +#+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 +#+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]]. + ** Quick usage *** Running the model + *** Visualization of the model output *** Study the effect of ecophysiological parameters using parameterSweep *** Monte-Carlo experiments