The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the
             algorithm are implemented, for numeric and relational data and for
             contingency tables as described, respectively, in Kohonen (2001)
             <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005)
             <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
             <doi:10.1016/j.neunet.2004.07.010>. The package also contains many
             plotting features (to help the user interpret the results), can 
             handle (and impute) missing values and is delivered with a 
             graphical user interface based on 'shiny'.

It's a collection of functions for Multiplicity Correction and Multiple Testing.

Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf>  heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.

Implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data.
    The package includes functions to estimate the optimal map size based on various quality measures
    and to generate a model using the selected dimensions.
    It also performs hierarchical clustering on the map nodes or other data to group similar units.
    Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024)
    <doi:10.1002/xrs.3388>.