Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, 
    alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following
    Coster (1993) <doi:10.1214/aos/1176349410>. Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) 
    <doi:10.3390/math9131455>. Regular fraction identification follows Guo, Simpson and Pignatiello (2007) 
    <doi:10.1080/00224065.2007.11917691>. Alias structure computation follows
    Rios-Lira et al.(2021) <doi:10.3390/math9233053>.

Multiple imputation using 'XGBoost', subsampling, and predictive mean 
    matching as described in Deng and Lumley (2023) 
    <doi:10.1080/10618600.2023.2252501>.  The package supports various types of 
    variables, offers flexible settings, and enables saving an imputation model to impute
    new data. Data processing and memory usage have been optimised to speed up 
    the imputation process.

Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of  multiple scaled generalized hyperbolic distributions, the 'cMSGHD'  is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.

Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.