A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.

Get data from 'Linkedin Advertising API' <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>.
    You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and
    also you can load ad analytics data from your 'Linkedin' Ad account.

Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.

Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.

Listings are often part of the submission of clinical trial
    data in regulatory settings.  We provide a framework for the specific
    formatting features often used when displaying large datasets in that
    context.

Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.

Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.