Analyse species-habitat associations in R. Therefore, information about the location 
  of the species (as a point pattern) is needed together with environmental conditions 
  (as a categorical raster). To test for significance habitat associations, one of 
  the two components is randomized. Methods are mainly based on Plotkin et al. (2000) 
  <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.

Provides functions to retrieve, process, analyze, and
    quality-control marine physical, chemical, and biological data. The
    main focus is on Swedish monitoring data available through the 'SHARK'
    database <https://shark.smhi.se/en/>, with additional API support for 'Nordic
    Microalgae' <https://nordicmicroalgae.org/>, 'Dyntaxa'
    <https://artfakta.se/>, World Register of Marine Species ('WoRMS') <https://www.marinespecies.org>,
    'AlgaeBase' <https://www.algaebase.org>, OBIS 'xylookup' web service 
    <https://iobis.github.io/xylookup/> and Intergovernmental Oceanographic Commission (IOC) - 
    UNESCO databases on harmful algae  <https://www.marinespecies.org/hab/> and toxins
    <https://toxins.hais.ioc-unesco.org/>.

Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. 
    This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020)
    "Can multi-species shark longline fisheries be managed sustainably using size limits? 
    Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847–1860. 
    <doi:10.1111/1365-2664.13659>.

In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) <doi:10.18637/jss.v112.i05>), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) <doi:10.1093/bioinformatics/btad635>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.