Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.

We provide detailed functions for univariate Mixed Tempered Stable distribution.

Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.

Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.

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.

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 (2024) 
    <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.