Implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953).  The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models).  For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models.

A system for calculating the optimal sampling effort, based on the ideas of 
    "Ecological cost-benefit optimization" as developed by A. Underwood (1997, 
    ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with
    prep_data() which formats and arranges the initial data, and then the 
    optimization follows the following procedure of four functions: (1) prep_data()
    takes the original dataset and creates simulated sets that can be used as a 
    basis for estimating statistical power and type II error. (2) sim_beta() is used
    to estimate the statistical power for the different sampling efforts specified
    by the user. (3) sim_cbo() calculates then the optimal sampling effort, based
    on the statistical power and the sampling costs. Additionally, (4) scompvar() 
    calculates the variation components necessary for (5) Underwood_cbo() to calculate
    the optimal combination of number of sites and samples depending on either 
    an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() 
    helps the user visualize the results of sim_beta().

Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) <doi:10.1126/science.1229931>, including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) <doi:10.1038/nature20584>, Forest Change (Hansen et al., 2013) <doi:10.1126/science.1244693>, and Continuous Tree Cover data (Sexton et al., 2013) <doi:10.1080/17538947.2013.786146>.