This is the repository for the R-package of the gen3sis engine project-gen3sis git.
gen3sis is a spatially-explicit eco-evolutionary mechanistic model with a modular implementation. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes.
gen3sis is licensed under a GPLv3 License deriving from ETHZ 2020 <doi.org/10.5905/ethz-1007-251> and has package authorship according to: http://epub.wu.ac.at/3269/1/Report114.pdf
gen3sis is avabaile on CRAN. You can install the latest CRAN release via
install.packages("gen3sis")
you can also install the latest development release from GitHub via
devtools::install_github(repo = "project-gen3sis/R-package",
dependencies = TRUE, build_vignettes = TRUE)
Below the status of the automatic CI R-CMD-check tests
Load and run a simulation with the desired config and landscapes. Exemple data is provided with the package
library("gen3sis")
datapath <- system.file(file.path("extdata", "WorldCenter"), package = "gen3sis")
sim <- run_simulation(config = file.path(datapath, "config/config_worldcenter.R"),
landscape = file.path(datapath, "landscape"),
output_directory = tempdir(),
verbose=0)
A summary statistics is stored at ‘sim’ more data can be save using the oberver function
Plot the summary statistics of a simulation
plot_summary(sim)
Make sure you have the latest gen3sis version
#print package version
paste("gen3sis version:", packageVersion("gen3sis"))
In short, the main branches of the gen3sis repo are: * master – reflects the current CRAN release. Only hotfixes or release-ready changes are merged here, typically just before CRAN submission. * development – serves as the main working branch. All new features, improvements, and fixes should be merged here from separate feature or bugfix branches.
Great that you are contributing! For guidelines on contributing to this project, please refer to the CONTRIBUTING.md file on github.
We thank the developers of the following methods and dependencies:
Rcpp Dirk Eddelbuettel and James Joseph Balamuta (2018). Extending R with C++: A Brief Introduction to Rcpp. The American Statistician. 72(1). URL https://doi.org/10.1080/00031305.2017.1375990.
BH Dirk Eddelbuettel, John W. Emerson and Michael J. Kane (2021). BH: Boost C++ Header Files. R package. https://CRAN.R-project.org/package=BH
Matrix Douglas Bates and Martin Maechler (2019). Matrix: Sparse and Dense Matrix Classes and Methods. R package. https://CRAN.R-project.org/package=Matrix
raster Robert J. Hijmans (2021). raster: Geographic Data Analysis and Modeling. R package. https://CRAN.R-project.org/package=raster
gdistance van Etten, J. (2017). R package gdistance: Distances and routes on geographical grids. Journal of Statistical Software, 76(1), 1–21. https://doi.org/10.18637/jss.v076.i13
sp Roger S. Bivand, Edzer Pebesma, Virgilio Gomez-Rubio, 2013. Applied spatial data analysis with R, Second edition. Springer, NY. https://asdar-book.org/
stringr Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package. https://CRAN.R-project.org/package=stringr
testthat Hadley Wickham (2011). testthat: Get Started with Testing. The R Journal, vol. 3, no. 1, pp. 5–10, https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf
formatR Yihui Xie (2021). formatR: Format R Code Automatically. R package. https://CRAN.R-project.org/package=formatR
scico Crameri, F. (2018). Scientific colour maps. Zenodo. http://doi.org/10.5281/zenodo.1243862 & Crameri, F., G.E. Shephard, and P.J. Heron (2020). The misuse of colour in science communication, Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7