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>.

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, 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.

Functions and data sets inspired by data sharpening -
             data perturbation to achieve improved performance in
             nonparametric estimation, as described in Choi, E., Hall, P.
             and Rousson, V. (2000).  
             Capabilities for enhanced local linear regression function
             and derivative estimation are included, as well as an
             asymptotically correct iterated data sharpening estimator
             for any degree of local polynomial regression estimation.
             A cross-validation-based bandwidth selector is included which,
             in concert with the iterated sharpener, will often provide
             superior performance, according to a median integrated squared
             error criterion.  Sample data sets are provided to illustrate
             function usage.