Fast computation of the distance covariance 'dcov' and distance correlation 'dcor'.  The computation cost is only O(n log(n)) for the distance correlation (see Chaudhuri, Hu (2019) <arXiv:1810.11332> <doi:10.1016/j.csda.2019.01.016>). The functions are written entirely in C++ to speed up the computation.

Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering 
    big data (gaussian mixture models for both multivariate and univariate datasets). This version 
    implements the faster alternative-EM* that expedites convergence via structure based data segregation. 
    The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, 
    Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, 
    Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

Prepare the results of a DCE to be analysed through choice models.'DCEmgmt' reshapes DCE data from wide to long format considering the special characteristics of a DCE. 'DCEmgmt' includes the function 'DCEestm' which estimates choice models once the database has been reshaped with 'DCEmgmt'.

Design, conduct and analyze 'DCEs' from a virtual interface in shiny. Reference: Perez-Troncoso, D. (2022) <https://github.com/danielpereztr/DCEtool>.

Data cloud geometry (DCG) applies random walks in finding community structures for social networks. 
    Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).

Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing 
    attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic 
    dynamic systems which can behave like an erratic and apparently random motion. A relevant field
    inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour 
    from empirical time series data. One of the main features of chaos is the well known initial value 
    sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify 
    the initial value sensitive property estimating the Lyapunov exponents. The DChaos package 
    provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos 
    based on the Lyapunov exponent in order to know if the data generating process behind time series 
    behave chaotically or not.