Allows management of 'Meetup' groups via the <https:www.meetup.com/meetup_api/>.
    Provided are a set of functions that enable fetching information of joined meetups, attendance,
    and members. This package requires the use of an API key.

A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.

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.