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 ...

Provides tools for computing densities and generating random samples from matrix variate distributions, including matrix normal, Wishart, matrix Student-t, matrix Dirichlet and matrix beta distributions. For complete disposition, see Gupta and Nagar ...

We provide a rich collection of linear and nonlinear dimension reduction techniques implemented using 'RcppArmadillo'. The question on what we should use as the target dimension is addressed by intrinsic dimension estimation methods introduced as ...