Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) <doi:10.1111/1468-0262.00390>. Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1.

Given a list of substance compositions, a list of substances 
  involved in a process, and a list of constraints in addition to mass 
  conservation of elementary constituents, the package contains functions 
  to build the substance composition matrix, to analyze the uniqueness of 
  process stoichiometry, and to calculate stoichiometric coefficients if 
  process stoichiometry is unique. 
  (See Reichert, P. and Schuwirth, N., A generic framework for deriving 
  process stoichiometry in enviromental models, Environmental Modelling 
  and Software 25, 1241-1251, 2010 for more details.)

User tools for working with The STOICH (Stoichiometric Traits of Organisms 
    in their Chemical Habitats) Project database <https://snr-stoich.unl.edu/>. 
    This package is designed to aid in data discovery, filtering, pairing water 
    samples with organism samples, and merging data tables to assist users in 
    preparing data for analyses. For additional examples see "Additional Examples"
    and the readme file at <https://github.com/STOICH-project/STOICH-utilities>.

Provides functionality for working with tensors, alternating
           forms, wedge products, Stokes's theorem, and related concepts
	   from the exterior calculus.  Uses 'disordR' discipline
	   (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>).  The
	   canonical reference would be M. Spivak
	   (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds".  To cite
	   the package in publications please use Hankin (2022)
           <doi:10.48550/arXiv.2210.17008>.

Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).