Functions for analysis of data generated from experiments in
    augmented randomised complete block design according to Federer, W.T.
    (1961) <doi:10.2307/2527837>. Computes analysis of variance, adjusted
    means, descriptive statistics, genetic variability statistics etc.
    Further includes data visualization and report generation functions.

Implementation of the augmented
            Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777>
            for analyzing the data with mixed measurement error and misclassification. The main
            function provides a similar summary output as that of glm() function. Both parametric and
            empirical SIMEX are considered in the package.

Extract and process bird sightings records from
    eBird (<http://ebird.org>), an online tool for recording bird
    observations.  Public access to the full eBird database is via the
    eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download>
    for access), a downloadable text file. This package is an interface to
    AWK for extracting data from the EBD based on taxonomic, spatial, or
    temporal filters, to produce a manageable file size that can be
    imported into R.

Efficient algorithms
 <https://jmlr.org/papers/v24/21-0751.html>
 for computing Area Under Minimum,
 directional derivatives, and
 line search optimization of a linear model,
 with objective defined as either
 max Area Under the Curve or
 min Area Under Minimum.

Uses locality sensitive hashing and creates a neighbourhood graph for a data set and calculates the adjusted rank index value for the same. It uses Gaussian random planes to decide the nature of a given point. Datar, Mayur, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni(2004) <doi:10.1145/997817.997857>.

Estimate the AUC using a variety of methods as follows: 
             (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. 
             (2) frequentist parametric methods using the likelihood ratio test based on higher-order 
             asymptotic results, the signed log-likelihood ratio test, the Wald test, 
             or the approximate ''t'' solution to the Behrens-Fisher problem. 
             (3) Bayesian parametric MCMC methods.