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

• O Hagen, B Flueck, F Fopp, JS Cabral, F Hartig, M Pontarp, TF Rangel, L Pellissier (2021) gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity. PLOS Biology. doi:10.1371/journal.pbio.3001340

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

DEVELOPMENT

MASTER

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"))

The main branches of the gen3sis repo are:

• master
• development

The master branch should always reflect the state of the current release of gen3sis on CRAN. The development branch contains the working additions/changes to the code that are to be included in the next release. You should not work on either of these branches directly. Rather, to make changes or work on a new feature, you should create a separate branch off the development branch. While working on your branch, frequently merge changes from development to stay up to date. Once your work is ready, and before you merge your branch into development, make sure to merge any changes from development and verify the code is compiling and tests are passing. Once these checks have been done, create a pull request to merge your branch into development. You can request reviewers for your pull request directly via GitHub. After your pull request is approved, or if it has not been reviewed within 30 days, it will be merged into development. The branch hotfix-development exists for small (one commit only) changes that are not worth creating a new branch for (for instance, small bugfixes, readme or help files edits, etc.). A pull request can then be created to merge those changes into development. New features should never be merged directly into master. Only hotfixes to the current release may be merged into master. For hotfixes, create a separate branch from master, make the fix and verify it, and then merge the hotfix branch into master and development. Similarly to above, the hotfix-master branch exists for small (one commit only) bugfixes to the current release. A pull request can then be created to merge those changes into master and development. The gen3sis workflow is inspired by the RevBayes workflow: https://revbayes.github.io/developer

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