Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.

The official implementation of the Global Livestock Environmental Assessment
  Model (GLEAM) of the Food and Agriculture Organization of the United Nations (FAO) in R. 
  GLEAM-X provides a modular, transparent framework for simulating livestock production 
  systems and quantifying their environmental impacts.
  Methodological background: MacLeod et al. (2017) "Invited review: A position on the Global
  Livestock Environmental Assessment Model (GLEAM)" <doi:10.1017/S1751731117001847>. Further
  information: <https://www.fao.org/gleam/en/>.

Download legal entity reference data from the 'Global Legal
    Entity Identifier Foundation' ('GLEIF') API. Retrieve Legal Entity
    Identifier ('LEI') records, their direct and ultimate parent and child
    relationships, accredited issuers ('Local Operating Units'), and
    mappings from 'LEI' codes to other identifiers such as 'ISIN', 'BIC',
    and 'MIC'. See <https://www.gleif.org/en/lei-data/gleif-api> for
    further details.

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.