Work with Ecological Metadata Language ('EML') files. 
    'EML' is a widely used metadata standard in the ecological and
    environmental sciences, described in Jones et al. (2006),
    <doi:10.1146/annurev.ecolsys.37.091305.110031>.

This is a utility for transforming Ecological Metadata Language
        ('EML') files into 'JSON-LD' and back into 'EML.'  Doing so creates a
        list-based representation of 'EML' in R, so that 'EML' data can easily
        be manipulated using standard 'R' tools. This makes this package an
        effective backend for other 'R'-based tools  working with 'EML.' By
        abstracting away the complexity of 'XML' Schema, developers can
        build around native 'R' list objects and not have to worry about satisfying
        many of the additional constraints of set by the schema (such as element
        ordering, which is handled automatically). Additionally, the 'JSON-LD' 
        representation enables the use of developer-friendly 'JSON' parsing and
        serialization that may facilitate the use of 'EML' in contexts outside of 'R,'
        as well as the informatics-friendly serializations such as 'RDF' and
        'SPARQL' queries.

Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.

End-member modelling analysis of grain-size data is an approach 
    to unmix a data set's underlying distributions and their contribution to 
    the data set. EMMAgeo provides deterministic and robust protocols for 
    that purpose.

Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and other displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, in Searle, Speed, and Milliken (1980) Population marginal means 
  in the linear model: An alternative to least squares means, The American 
  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.