Transforms away factors with many levels prior to doing an OLS.
  Useful for estimating linear models with multiple group fixed effects, and for
  estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024>
  Includes support for instrumental variables, conditional F statistics for weak instruments,
  robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>).
  Since version 3.0, it provides dedicated functions to estimate Poisson models.

Provides tools to generate and analyze 3-level, 4-level and
    5-level linear factorial block designs, including complete factorial layouts,
    fractional factorial layouts, confounded factorial layouts, and 
    design-characteristic summaries. The package includes utilities for
    recursive construction, defining-contrast identification, alias and
    confounding summaries, incidence matrix construction, and selected
    design-characteristic diagnostics. The methodological framework follows
    foundational work on factorial block designs, including Gupta (1983)
    <doi:10.1111/j.2517-6161.1983.tb01253.x>.

Various algorithms related to linguistic fuzzy logic: mining for
    linguistic fuzzy association rules, composition of fuzzy relations,
    performing Mamdani, implicative, and perception-based logical deduction
    (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE).
    The package also contains basic fuzzy-related algebraic functions capable of
    handling missing values in different styles (Bochvar, Sobocinski, Kleene
    etc.), computation of Sugeno integrals and the fuzzy transform.

Fast and accurate inference of 
             gene-environment associations (GEA) in genome-wide studies 
             (Caye et al., 2019, <doi:10.1093/molbev/msz008>). 
             We developed a least-squares estimation approach for confounder and effect sizes 
             estimation that provides a unique framework for several categories of genomic data, 
             not restricted to genotypes. 
             The speed of the new algorithm is several times faster than the existing GEA approaches, 
             then our previous version of the 'LFMM' program present in the 'LEA' package 
             (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).