Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.

Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.

Offers a general framework of multivariate mixed-effects
        models for the joint analysis of multiple correlated outcomes with clustered 
        data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may 
        depend on the values themselves (missing not at random and non-ignorable), 
        or may depend on only the covariates (missing at random and ignorable), or both.
        This package provides functions for two models: 1) mvMISE_b() 
        allows correlated outcome-specific random intercepts with a factor-analytic 
        structure, and 2) mvMISE_e() allows the correlated outcome-specific 
        error terms with a graphical lasso penalty on the error precision matrix. Both functions 
        are motivated by the multivariate data analysis on data with clustered structures 
        from labelling-based quantitative proteomic studies. These models and functions 
        can also be applied to univariate and multivariate analyses of clustered data 
        with balanced or unbalanced design and no missingness.

Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
    data generated from a continuous-time multivariate industrial or natural
    process. Employ PCA-based dimension reduction to extract linear combinations
    of relevant features, reducing computational burdens. For a description of 
    ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
    The multi-state application of ADPCA is from a manuscript under current
    revision entitled "Multi-State Multivariate Statistical Process Control" by
    Odom, Newhart, Cath, and Hering, and is  expected to appear in Q1 of 2018.