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>.

Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.

Programmatic interface to the 'SNOTEL' snow data
  (<https://www.nrcs.usda.gov/programs-initiatives/sswsf-snow-survey-and-water-supply-forecasting-program>). Provides easy downloads of snow 
  data into your R work space or a local directory. Additional post-processing 
  routines to extract snow season indexes are provided.