An implementation of feature selection, weighting and ranking via simultaneous perturbation
    stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of
    features that yield the best predictive performance using some error measures such as mean 
    squared error (for regression problems) and accuracy rate (for classification problems).

A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <doi:10.1016/j.spasta.2018.07.005>: simulation of spatial ARCH-type processes (spARCH, log/exponential-spARCH, complex-spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.

A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.

Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.

Model based clustering with spherical data using mixtures of elliptically symmetric distributions, namely mixtures of spherical elliptically symmetric projected Cauchy (SESPC) or mixtures of elliptically symmetric angular Gaussian (ESAG) distributions. The relevant paper is: Perdikis T., Alharbi N. and Tsagris M. (2026). <doi:10.48550/arXiv.2605.27496>.