Utilizing model-based clustering (unsupervised)
        for functional magnetic resonance imaging (fMRI) data.
        The developed methods (Chen and Maitra (2023) <doi:10.1002/hbm.26425>)
        include 2D and 3D clustering analyses
        (for p-values with voxel locations) and
        segmentation analyses (for p-values alone) for fMRI data where p-values
        indicate significant level of activation responding to stimulate
        of interesting. The analyses are mainly identifying active
        voxel/signal associated with normal brain behaviors.
        Analysis pipelines (R scripts) utilizing this package
        (see examples in 'inst/workflow/') is also implemented with high
        performance techniques.

Multiple imputation using 'XGBoost', subsampling, and predictive mean 
    matching as described in Deng and Lumley (2023) <arXiv:2106.01574>. Our
    method utilizes the capabilities of XGBoost, a highly efficient implementation
    of gradient boosted trees, to capture interactions and non-linear relations
    automatically. Moreover, we have integrated subsampling and predictive mean
    matching to minimize bias and reflect appropriate imputation variability. This
    package supports various types of variables and offers flexible settings for
    subsampling and predictive mean matching. Additionally, it includes diagnostic
    tools for evaluating the quality of the imputed values.

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>.