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.

There are an increasing number of mega-phylogenies available nowadays,
    with many of them being sets of thousands of posterior distribution phylogenies.
    For ecological studies, we may need to randomly select many such posterior phylogenies
    to conduct analyses. This data package serves this purpose by providing a small number (100 or 50)
    of randomly selected posterior phylogenies (if available) so that we can readily use 
    them for our downstream analyses without repeating the downloading and selecting processes.

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.

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