Interactive graphical user interface (GUI) for the package 
    'AdhereR', allowing the user to access different data sources, to explore 
    the patterns of medication use therein, and the computation of various  
    measures of adherence. It is implemented using Shiny and HTML/CSS/JavaScript.

Implements tools for manipulation of digital
             		images and the Propagation Separation approach
             		by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1>
                        for smoothing digital images, see Polzehl and Tabelow (2007)
                        <DOI:10.18637/jss.v019.i01>.

Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.

Implements a constrained version of hierarchical agglomerative 
    clustering, in which each observation is associated to a position, and only 
    adjacent clusters can be merged. Typical application fields in 
    bioinformatics include Genome-Wide Association Studies or Hi-C data 
    analysis, where the similarity between items is a decreasing function of 
    their genomic distance. Taking advantage of this feature, the implemented 
    algorithm is time and memory efficient. This algorithm is described in 
    Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.

For a binary classification the adjusted sensitivity and specificity 
             are measured for a given fixed threshold. If the threshold for either 
             sensitivity or specificity is not given, the crossing point between 
             the sensitivity and specificity curves are returned. For bootstrap 
             procedures, mean and CI bootstrap values of sensitivity, specificity, 
             crossing point between specificity and specificity as well as AUC 
             and AUCPR can be evaluated.