MAINTENANCE MODE - this repo is being moved to a monorepo with better integration between features
This package is a clean-room, feature-equivalent re-write in Julia of most
of the functionalities of the fauxcurrence package for R. The
original code is licensed under the GPL, and this package is licensed under the
MIT. For this reason, the original code, and any document distributed with it,
has not been consulted during the implementation; the work is entirely based on
the published article. As detailed in the following sections, the two packages
do not have feature parity, but there is an overlap in the most significant
functions.
The citation for the original fauxcurrence paper is
Osborne, O. G., Fell, H. G., Atkins, H., van Tol, J., Phillips, D., Herrera‐Alsina, L., Mynard, P., Bocedi, G., Gubry‐Rangin, C., Lancaster, L. T., Creer, S., Nangoy, M., Fahri, F., Lupiyaningdyah, P., Sudiana, I. M., Juliandi, B., Travis, J. M. J., Papadopulos, A. S. T., & Algar, A. C. (2022). Fauxcurrence: Simulating multi‐species occurrences for null models in species distribution modelling and biogeography. Ecography. https://doi.org/10.1111/ecog.05880
This package allows to rapidly (and in a memory-efficient) way generate simulated occurrence data that have the same statistical properties as empirical datasets. It has been expanded to allow users to fine-tune the pairwise dependencies of species distributions on one another. This packages allows to generate synthetic datasets that can be used as either null models, or as benchmark data to evaluate methods to work with point occurrence data.
The stabe release of the package is available from the Julia general registry,
and can be installed with ]add Fauxcurrences. The development branch can be
checked out with ]add Fauxcurrences#main.
There is an online documentation for the Fauxcurrences.jl package, also
available as a Jupyter notebook. The documentation is a full-run of the
package functions, which also serves as an integration test. The methods are all
documented using the usual Julia docstrings, accessibles at the REPL.
Contributions are welcome through issues or pull requests.
Interoperability: this package uses SimpleSDMLayers, Distances, and GBIF
as backends, making it fit very snuggly with the rest of the (Eco)Julia
ecosystem, and working towards integration of tools to build SDMs at scale.
Expandability: the package is built on modular functions, to ensure that custom workflows can be built, while maintaining a general interface.
Performance: by relying on pre-allocated matrices, the operations are fairly fast. Whenever a point changes, only the matrices that are impacted are updated, which results in notable improvements for more taxa.
Licensing: the package uses the more permissive MIT license, imposing fewer constraints on contributors.