The methods discussed in this package are new non-parametric methods
    based on sequential normal scores 'SNS' (Conover et al (2017)
    <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
    usually time series data, which may occur singly or in batches,
    and may be univariate or multivariate. These methods are designed
    to detect changes in the process, which may occur as changes in location
    (mean or median), changes in scale (standard deviation, or variance), or
    other changes of interest in the distribution of the observations,
    over the time observed. They usually apply to large data sets,
    so computations need to be simple enough to be done in a reasonable
    time on a computer, and easily updated as each new observation
    (or batch of observations) becomes available. Some examples and more detail
    in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.

Implementations self-normalization (SN) based algorithms for 
    change-points estimation in time series data. This comprises nested 
    local-window algorithms for detecting changes in both univariate and 
    multivariate time series developed in Zhao, Jiang and Shao (2022) 
    <doi:10.1111/rssb.12552>.

Contains functions to perform various models and
    methods for test equating (Kolen and Brennan, 2014 
    <doi:10.1007/978-1-4939-0317-7> ; Gonzalez and Wiberg, 2017 
    <doi:10.1007/978-3-319-51824-4> ; von Davier et. al, 2004 
    <doi:10.1007/b97446>). It currently implements the traditional mean, linear 
    and equipercentile equating methods. Both IRT observed-score and true-score 
    equating are also supported, as well as the mean-mean, mean-sigma, Haebara 
    and Stocking-Lord IRT linking methods. It also supports newest methods such 
    that local equating, kernel equating (using Gaussian, logistic, 
    Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT 
    parameter linking methods based on asymmetric item characteristic functions. 
    Functions to obtain both standard error of equating (SEE) and standard error 
    of equating differences between two equating functions (SEED) are also 
    implemented for the kernel method of equating.