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.

Download and read datasets from the Swiss National
     Science Foundation (SNF, FNS, SNSF; <https://snf.ch>).  The
     package is lightweight and without dependencies.  Downloaded
     data can optionally be cached, to avoid repeated downloads of
     the same files.  There are also utilities for comparing
     different versions of datasets, i.e. to report added, removed
     and changed entries.

Consolidated data simulation, sample size calculation and
    analysis functions for several snSMART (small sample sequential,
    multiple assignment, randomized trial) designs under one library.  See
    Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian
    analysis of small n sequential multiple assignment randomized trials
    (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732
    <doi:10.1002/sim.7900>.