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>.

Mainly for maximum likelihood estimation of nonparametric
             and semiparametric mixture models, but can also be used
             for fitting finite mixtures. The algorithms are
             developed in Wang (2007)
             <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010)
             <doi:10.1007/s11222-009-9117-z>.

Two methods for performing a constrained principal
        component analysis (PCA), where non-negativity and/or sparsity
        constraints are enforced on the principal axes (PAs). The
        function 'nsprcomp' computes one principal component (PC) after
        the other. Each PA is optimized such that the corresponding PC
        has maximum additional variance not explained by the previous
        components. In contrast, the function 'nscumcomp' jointly
        computes all PCs such that the cumulative variance is maximal.
        Both functions have the same interface as the 'prcomp' function
        from the 'stats' package (plus some extra parameters), and both
        return the result of the analysis as an object of class
        'nsprcomp', which inherits from 'prcomp'. See
        <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/>
        and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more
        details.