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 representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms.

Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) <doi:10.1111/2041-210X.14225>.