Computes Strongest Neighbor Coherence (SNC), a structural diagnostic that replaces Cronbach's alpha using top-k correlation structure.
  For methodology, see Wells (2025) <https://github.com/TheotherDrWells/snc>.

Efficient estimation of multivariate skew-normal distribution in closed form.

Fitting of non-parametric production frontiers for use in efficiency analysis.
    Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a 
    non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for 
    multiple inputs and a single output using constrained kernel smoothing as in 
    Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints 
    on the estimated frontier.

Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.

Create correlation networks using St. Nicolas House Analysis ('SNHA'). 
    The package can be used for visualizing multivariate data similar to Principal
    Component Analysis or Multidimensional Scaling using a ranking approach.
    In contrast to 'MDS' and 'PCA', 'SNHA' uses a network approach to explore
    interacting variables. 
    For details see 'Hermanussen et. al. 2021', <doi:10.3390/ijerph18041741>.

Implements the Simple Non-Iterative Clustering algorithm for 
    superpixel segmentation of multi-band images, as introduced by Achanta 
    and Susstrunk (2017) <doi:10.1109/CVPR.2017.520>. Supports both standard 
    image arrays and geospatial raster objects, with a design that can be 
    extended to other spatial data frameworks. The algorithm groups adjacent 
    pixels into compact, coherent regions based on spectral similarity and 
    spatial proximity. A high-performance implementation supports images 
    with arbitrary spectral bands.