Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.

Projection pursuit is used to find interesting low-dimensional
    projections of high-dimensional data by optimizing an index over all
    possible projections. The 'spinebil' package contains methods to evaluate
    the performance of projection pursuit index functions using tour methods. 
    A paper describing the methods can be found at <doi:10.1007/s00180-020-00954-8>.

Data visualization tours animates linear projection 
  of multivariate data as its basis (ie. orientation) changes. The 'spinifex' 
  packages generates paths for manual tours by manipulating the contribution of 
  a single variable at a time Cook & Buja (1997) 
  <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand 
  (random walk) and guided (optimizing some objective function) are available 
  in the 'tourr' package Wickham et al. <doi:10.18637/jss.v040.i02>. 
  'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 
  'plotly' graphics, and allows for exporting as an .html widget and as an .gif, 
  respectively. This work is fully discussed in Spyrison & Cook (2020) 
  <doi:10.32614/RJ-2020-027>.

Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding.

Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.

It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, 
    makes so-called spiral graph or spiral chart. 
    It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high 
    resolution. 2. It is efficient for time series data to reveal periodic patterns.