Visualizations for SHAP (SHapley Additive exPlanations), such
    as waterfall plots, force plots, various types of importance plots,
    dependence plots, and interaction plots.  These plots act on a
    'shapviz' object created from a matrix of SHAP values and a
    corresponding feature dataset. Wrappers for the R packages 'xgboost',
    'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and
    'kernelshap' are added for convenience.  By separating visualization
    and computation, it is possible to display factor variables in graphs,
    even if the SHAP values are calculated by a model that requires
    numerical features. The plots are inspired by those provided by the
    'shap' package in Python, but there is no dependency on it.

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.