Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should
    provide most standard Kriging / Gaussian process regression features
    (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages).
    'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 
    'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by 
    Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.

Performs Wavelet Lifting Transforms focusing on signal denoising 
    and functional data analysis (FDA). Implements a hybrid architecture with 
    a zero-allocation 'C++' core for high-performance processing. Features include 
    unified offline (batch) denoising, causal (real-time) filtering using a 
    ring buffer engine, and adaptive recursive thresholding.

Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.

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>.