Query, extract, and plot genealogical data from The Mathematics
    Genealogy Project <https://mathgenealogy.org/>. Data is gathered from the
    'WebSocket' server run by the 'geneagrapher-core' project
    <https://github.com/davidalber/geneagrapher-core>.

Allows users familiar with MATLAB to use MATLAB-named functions in
    R. Several basic MATLAB functions are written in this package to mimic the
    behavior of their original counterparts, with more to come as this package
    grows.

Provides users to call MATLAB from using the "system" command.
    Allows users to submit lines of code or MATLAB m files.
    This is in comparison to 'R.matlab', which creates a MATLAB server.

A collection of matrix functions for teaching and learning matrix
    linear algebra as used in multivariate statistical methods. Many of these functions are
    designed for tutorial purposes in learning matrix algebra ideas using R. In some
    cases, functions are provided for concepts available elsewhere in R, but where
    the function call or name is not obvious. In other cases, functions are provided
    to show or demonstrate an algorithm. In addition, a collection of functions are
    provided for drawing vector diagrams in 2D and 3D and for rendering matrix
    expressions and equations in LaTeX.

A set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.

A general framework for computing powers of matrices.  A
   key feature is the capability for users to write callback functions,
   called after each iteration, thus enabling customization for specific
   applications.  Diverse types of matrix classes/matrix multiplication
   are accommodated.  If the multiplication type computes in parallel,
   then the package computation is also parallel.

Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) <https://jmlr.org/papers/v24/23-0030.html>.