Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.

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.