Box-constrained multiobjective optimization using the 
        elitist non-dominated sorting genetic algorithm - NSGA-II.
        Fast non-dominated sorting, crowding distance, tournament 
        selection, simulated binary crossover, and polynomial 
        mutation are called in the main program. The methods are 
        described in Deb et al. (2002) <doi:10.1109/4235.996017>.

An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective 
    feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm
    that solves multiple optimization problems simultaneously by applying a non-dominated sorting 
    technique. It uses a reference points based selection operator to explore 
    solution space and preserve diversity. See the original paper by K. Deb and 
    H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.

Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.

Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.

National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.

Implementation of Narrowest Significance Pursuit, a general and
    flexible methodology for automatically detecting localised regions in data sequences
    which each must contain a change-point (understood as an abrupt change in the
    parameters of an underlying linear model), at a prescribed global significance level.
    Narrowest Significance Pursuit works with a wide range of distributional assumptions
    on the errors, and yields exact desired finite-sample coverage probabilities,
    regardless of the form or number of the covariates. For details, see P. Fryzlewicz
    (2021) <https://stats.lse.ac.uk/fryzlewicz/nsp/nsp.pdf>.