  In this manuscript, we present a common tensor framework which can be used to
generalize one-dimensional numerical tasks to arbitrary dimension $d$ by means
of tensor product formulas. This is useful, for example, in the context of
multivariate interpolation, multidimensional function approximation using
pseudospectral expansions and solution of stiff differential equations on
tensor product domains. The key point to obtain an efficient-to-implement BLAS
formulation consists in the suitable usage of the $\mu$-mode product (also
known as tensor-matrix product or mode- $n$ product) and related operations,
such as the Tucker operator. Their MathWorks MATLAB/GNU Octave implementations
are discussed in the paper, and collected in the package KronPACK. We present
numerical results on experiments up to dimension six from different fields of
numerical analysis, which show the effectiveness of the approach.
