Implementation of Bayesian models for estimating object lengths and morphological relationships between object lengths using photographic data collected from drones.  The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, <doi:10.3354/meps13814>).

Functions for Estimating a (c)DCC-GARCH Model in large dimensions
    based on a publication by Engle et,al (2017) <doi:10.1080/07350015.2017.1345683> and Nakagawa et,al (2018) <doi:10.3390/ijfs6020052>.
    This estimation method is consist of composite likelihood method by Pakel et al. (2014) <http://paneldataconference2015.ceu.hu/Program/Cavit-Pakel.pdf>
    and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (<doi:10.1016/S0047-259X(03)00096-4>,
    <doi:10.1214/12-AOS989>, <doi:10.1016/j.jmva.2015.04.006>).

Hamiltonian Monte Carlo for both continuous and discontinuous posterior 
    distributions with a customizable trajectory length termination criterion. 
    See Nishimura et al. (2020) <doi:10.1093/biomet/asz083> for the original 
    Discontinuous Hamiltonian Monte Carlo; Hoffman et al. (2014) 
    <doi:10.48550/arXiv.1111.4246> and Betancourt (2016) <doi:10.48550/arXiv.1601.00225> 
    for the definition of possible Hamiltonian Monte Carlo termination criteria.

High-level functions to render 'LaTeX' fragments in plots, 
             including as labels and data symbols in 'ggplot2' plots, plus
             low-level functions to 
             author 'LaTeX' fragments (to produce 'LaTeX' documents), 
             typeset 'LaTeX' documents (to produce 'DVI' files), 
             read 'DVI' files (to produce "DVI" objects), 
             and render "DVI" objects.

Miscellaneous functions used for x-engineering (feature engineering) or 
  for supporting in other packages maintained by 'Shichen Xie'.

Implementation of a scalable, highly configurable, and 
        e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms.
        Multiple representations (binary, real-coded, permutation, and 
        derivation-tree), a rich collection of genetic operators, 
        as well as an extended processing pipeline are provided 
        for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)),
        differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005)
        <doi:10.1007/3-540-31306-0>), simulated annealing (Aarts, E., and Korst, J.
        (1989, ISBN:0-471-92146-7)), grammar-based genetic programming 
        (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution 
        (Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>), 
        and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in  
        Arabinia, H. (2006, ISBN:978-193-241596-3).
        All algorithms reuse basic adaptive mechanisms for performance optimization.
        For 'xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
        Sequential or parallel execution (on multi-core machines, 
        local clusters, and high-performance computing environments) 
        is available for all algorithms. See 
        <https://github.com/ageyerschulz/xega/tree/main/examples/executionModel>.