Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
              stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating
              parameters of the symmetric stable distribution within the Bayesian framework.

Provides various functions for parameter estimation of one-dimensional
    stable distributions and their mixtures. It implements a diverse set of
    estimation methods, including quantile-based approaches, regression methods
    based on the empirical characteristic function (empirical, kernel, and
    recursive), and maximum likelihood estimation. For mixture models, it provides
    stochastic expectation–maximization (SEM) algorithms and Bayesian estimation
    methods using sampling and importance sampling to overcome the long burn-in
    period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes
    tools and statistical tests for analyzing whether a dataset follows a stable
    distribution. Some of the implemented methods are described in
    Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) <doi:10.1080/02664763.2024.2434627>.

Flexible time classes for time series analysis and forecasting with
    mixed temporal granularities. Supports linear and cyclical time 
    representations in discrete and continuous forms, with timezone support, 
    across multiple calendar systems including Gregorian and ISO week date 
    calendars. Time points are stored numerically relative to a chronon; an 
    atomic time granule defined by time units of a calendar. Calendrical 
    arithmetic enables conversion between time granules (e.g. days to months) 
    and calendar systems. Multi-unit arithmetic allows for temporal analysis
    with other granules of common calendars (e.g. fortnights are 2-week units).
    Time vectors of different granularities (e.g. monthly and quarterly) can be
    combined in a single vector, making 'mixtime' ideal for data that changes
    observation frequency over time or requires temporal reconciliation across 
    scales. The package is extensible, allowing users to define custom calendars 
    that build upon civil and astronomical time systems.