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.

Augments the 'eiCompare' package's Racially Polarized Voting (RPV) 
  functionality to streamline analyses and visualizations used to support 
  voting rights and redistricting litigation. The package implements methods 
  described in Barreto, M., Collingwood, L., Garcia-Rios, S., & Oskooii, K. A. (2022). 
  "Estimating Candidate Support in Voting Rights Act Cases: Comparing Iterative 
  EI and EI-R×C Methods" <doi:10.1177/0049124119852394>.

Estimation of the parameters in a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accommodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification. See Hoff (2007) <arXiv:0711.1146> 
        for details on the model.

Matrix algebra using the 'Eigen' C++ library: determinant,
    rank, inverse, pseudo-inverse, kernel and image, QR decomposition,
    Cholesky decomposition, Schur decomposition, Hessenberg decomposition, 
    linear least-squares problems. Also provides matrix functions such as 
    exponential, logarithm, power, sine and cosine. Complex matrices are 
    supported.