Generates synthetic tabular data from real datasets using
    Gaussian copula models, with parametric marginal selection for
    numerical columns and a cumulative-frequency embedding that brings
    categorical and boolean columns into the same joint copula. Includes
    a metadata system with column types and primary keys, declarative
    constraints enforced via rejection sampling, conditional sampling,
    and quality, validity and privacy reports modeled on those of the
    'SDMetrics' library. Inspired by the Python 'SDV' (Synthetic Data
    Vault) library by 'DataCebo'; see Patki, Wedge and Veeramachaneni
    (2016) "The Synthetic Data Vault" <doi:10.1109/DSAA.2016.49>.

A Bayesian-weighted estimator and two unweighted estimators are
 developed to estimate the number of newly found rare species in additional
 ecological samples. Among these methods, the Bayesian-weighted estimator
 and an unweighted (Chao-derived) estimator are of high accuracy and
 recommended for practical applications.
 Technical details of the proposed estimators have been well described
 in the following paper: Shen TJ, Chen YH (2018) A Bayesian
 weighted approach to predicting the number of newly discovered
 rare species. Conservation Biology, In press.

SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to play around with the pathways of interest.

Processing and analysis of targeted sequencing data. The package provides a user-friendly interface for core 'VSEARCH' (Rognes et al. (2016), <doi:10.7717/peerj.2584>) functions, in addition to tools for visualization and parameter tuning.

Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.