An R interface to United States Environmental 
    Protection Agency (EPA) Environmental Compliance 
    History Online ('ECHO') Application Program Interface
    (API). 'ECHO' provides information about EPA permitted 
    facilities, discharges, and other reporting info 
    associated with permitted entities. Data are obtained 
    from <https://echo.epa.gov/>.

Extends the Changes-in-Changes model a la Athey and Imbens
    (2006) <doi:10.1111/j.1468-0262.2006.00668.x> to multiple cohorts and
    time periods, which generalizes difference-in-differences estimation
    techniques to the entire distribution. Computes quantile treatment
    effects for every possible two-by-two combination in ecic(). Then,
    aggregating all bootstrap runs adds the standard errors in
    summary_ecic(). Results can be plotted with plot_ecic() aggregated
    over all cohort-group combinations or in an event-study style for
    either individual periods or individual quantiles.

Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided and calculation of isotopic gross structures
    is also supported.

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>.