Because your linear models deserve better than console output.
  A sleek color palette and kable styling to make your regression results look sharper than they are.
  Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms,
  along with a unified interface for model fitting and fabulous LaTeX and console output formatting.
  See the package website at 
  <https://finitesample.space/snazzier>.

Download data (tables and datasets) from the Swiss
     National Bank (SNB; <https://www.snb.ch/en>), the Swiss
     central bank.  The package is lightweight and comes with few
     dependencies; suggested packages are used only if data is to
     be transformed into particular data structures, for instance
     into 'zoo' objects.  Downloaded data can optionally be
     cached, to avoid repeated downloads of the same files.

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>.