Functions for analyzing dichotomous choice contingent 
   valuation (CV) data. It provides functions for estimating parametric and 
   nonparametric models for single-, one-and-one-half-, and double-bounded 
   CV data. For details, see Aizaki et al. (2022) <doi:10.1007/s42081-022-00171-1>.

Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCC-MIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <doi:10.1093/jjfinec/nbl005>, and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) <doi:10.1080/07350015.2011.652048>. 'dccmidas' offers the possibility of including standard GARCH <doi:10.1016/0304-4076(86)90063-1>, GARCH-MIDAS <doi:10.1162/REST_a_00300> and Double Asymmetric GARCH-MIDAS <doi:10.1016/j.econmod.2018.07.025> models in the univariate estimation. Moreover, also the scalar and diagonal BEKK <doi:10.1017/S0266466600009063> models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.

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>.