Provides API access to data from the U.S. Energy Information Administration ('EIA') <https://www.eia.gov/>.
    Use of the EIA's API and this package requires a free API key obtainable at <https://www.eia.gov/opendata/register.php>.
    This package includes functions for searching the EIA data directory and returning time series and geoset time series datasets.
    Datasets returned by these functions are provided by default in a tidy format, or alternatively, in more raw formats.
    It also offers helper functions for working with EIA date strings and time formats and for inspecting different summaries of series metadata.
    The package also provides control over API key storage and caching of API request results.

Provides a function to query and extract data from the 'US Energy Information Administration' ('EIA') API V2  <https://www.eia.gov/opendata/>. The 'EIA' API provides a variety of information, in a time series format, about the energy sector in the US. The API is open, free, and requires an access key and registration at <https://www.eia.gov/opendata/>.

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates.
   References:
   Brown, P. and Payne, C. (1986). ''Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453–460. <DOI:10.1080/01621459.1986.10478290>.
   Forcina, A., Gnaldi, M. and Bracalente, B. (2012). ''A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109–119. <DOI:10.1007/s10260-011-0184-x>.

Model fitting and species biotic interaction network topology selection for explicit
  interaction community models. Explicit interaction community models are an extension of binomial
  linear models for joint modelling of species communities, that incorporate both the effects of
  species biotic interactions and the effects of missing covariates. Species interactions are modelled
  as direct effects of each species on each of the others, and are estimated alongside the effects of
  missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood
  fitting function, and a genetic algorithm for selecting the most parsimonious species interaction
  network topology.