Performs clustering of quantitative variables,
    assuming that clusters lie in low-dimensional subspaces. Segmentation of
    variables, number of clusters and their dimensions are selected based on
    BIC. Candidate models are identified based on many runs of K-means
    algorithm with different random initializations of cluster centers.

Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024)
    <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional 
    Vector Auto-regressive Models". This package provides tools for detecting change points 
    in the transition matrices of VAR models, effectively identifying shifts in temporal 
    and cross-correlations within high-dimensional time series data.

Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.

Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.

Provides statistical methods for assessing homoscedasticity
    (equality of variances) across groups. The package implements
    classical and robust tests for variance homogeneity, together with
    supporting measures and utilities for exploratory analysis and
    hypothesis testing. These tools can be used as diagnostic procedures
    prior to analyses that assume equal variances.