ewlgcpSDM (effort-weighted Log-Gaussian Cox Process species distribution models) is a package for inferring species distributions using presence-only data and point process models. In particular, it implements the Log-Gaussian Cox process and adjusts for sampling effort by weighting the thinned surface through and estimate of the effort. The effort surface is derived through a method analogous to Target-Group Background selection Phillips et al. 2009, whereas the sampling effort is approximated by grouping all observations of a target group of similar species. The implementation is adapted from the method proposed by Simpson et al. (2016) using INLA (Rue et al. 2009). Some of the results from an upcoming paper can be seen here.
First, INLA (not on CRAN) has to be installed (see here for instructions) or do:
install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)Then, ewlgcpSDM can be installed directly from GitHub like this:
remotes::install_github("BiodiversiteQuebec/ewlgcpSDM")Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J. and Ferrier, S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19(1): 181-197 https://doi.org/10.1890/07-2153.1
Rue, H., Martino, S. and Chopin, N. 2009. Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations, Journal of the Royal Statistical Society Series B: Statistical Methodology, 71(2): 319–392 https://doi.org/10.1111/j.1467-9868.2008.00700.x
Simpson, D., Illian, J. B., Lindgren, F., Sørbye, S. H. and Rue, H. 2016. Going off grid: computationally efficient inference for log-Gaussian Cox processes. Biometrika, 103(1): 49-70 https://doi.org/10.1093/biomet/asv064