Similarity of dissolution profiles is assessed using the
    similarity factor f2 according to the EMA guideline (European
    Medicines Agency 2010) "On the investigation of bioequivalence".
    Dissolution profiles are regarded as similar if the f2 value is
    between 50 and 100. For the applicability of the similarity factor f2,
    the variability between profiles needs to be within certain limits.
    Often, this constraint is violated. One possibility in this situation
    is to resample the measured profiles in order to obtain a bootstrap
    estimate of f2 (Shah et al. (1998) <doi:10.1023/A:1011976615750>).
    Other alternatives are the model-independent non-parametric
    multivariate confidence region (MCR) procedure (Tsong et al. (1996)
    <doi:10.1177/009286159603000427>) or the T2-test for equivalence
    procedure (Hoffelder (2016)
    <https://www.ecv.de/suse_item.php?suseId=Z|pi|8430>). Functions for
    estimation of f1, f2, bootstrap f2, MCR / T2-test for equivalence
    procedure are implemented.

Calculate multiple or pairwise dissimilarity for orders q = 0-N 
    (CqN; Chao et al. 2008 <doi:10/fcvn63>) for a set of species assemblages or 
    interaction networks.

Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.

Efficiently creates, manipulates, and subsets "dist" objects, commonly used in cluster analysis. Designed to minimise unnecessary conversions and computational overhead while enabling seamless interaction with distance matrices.

Tools to fit sample selection models in case of discrete response 
  variables, through a parametric formulation which represents a natural 
  extension of the well-known Heckman selection model are provided in the 
  package. The response variable can be of Bernoulli, Poisson or Negative 
  Binomial type. The sample selection mechanism allows to choose among a 
  Normal, Logistic or Gumbel distribution.