An S4 update of the 'mefa' package
  using sparse matrices for enhanced efficiency.
  Sparse array-like objects are supported via
  lists of sparse matrices.

To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.

Gradient boosting is a powerful statistical learning method known for its ability to model 
    complex relationships between predictors and outcomes while performing inherent variable selection. 
    However, traditional gradient boosting methods lack flexibility in handling longitudinal data where 
    within-subject correlations play a critical role. In this package, we propose a novel approach 
    Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. 
    'MEGB' incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting 
    framework, allowing it to account for within-individual covariance over time. Additionally, the method 
    efficiently handles scenarios where the number of predictors greatly exceeds the number of observations
    (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies.

Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.

Exploratory model analysis with <http://ggobi.org>.  
    Fit and graphical explore ensembles of linear models.

Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.

Performs multiple empirical likelihood tests. It offers an
    easy-to-use interface and flexibility in specifying hypotheses and
    calibration methods, extending the framework to simultaneous
    inferences.  The core computational routines are implemented using the
    'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for
    parallel computation.  Details of the testing procedures are provided
    in Kim, MacEachern, and Peruggia (2023)
    <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim,
    MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is
    available for further information. This work was supported by the U.S.
    National Science Foundation under Grants No. SES-1921523 and
    DMS-2015552.