Companion package to the paper: An analytic approach for 
    interpretable predictive models in high dimensional data, in the presence of 
    interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. 
    This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.

Functions for easy building of error correction models (ECM) for time series regression.

Programmatic interface to the European Centre for Medium-Range
    Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>)
    and Copernicus's Climate Data Store (CDS; 
    <https://cds.climate.copernicus.eu>). Allows for easy downloads of weather 
    forecasts and climate reanalysis data in R.

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, 
    and the optimization follows the following procedure of four functions (1) 
    sim_beta() estimates statistical power and type 2 error by using Permutational 
    Multivariate Analysis of Variance, (2) plot_power() represents the results 
    of the previous function, (3) scompvar() calculates the variation components 
    necessary for (4) sim_cbo() to calculate the optimal combination of number 
    of sites and samples depending on either an economical budget or on a desired 
    statistical accuracy.

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>.