Efficient implementation of sparse group lasso with optional
    bound constraints on the coefficients; see
    <doi:10.18637/jss.v110.i06>.  It supports the use of a sparse design
    matrix as well as returning coefficient estimates in a sparse matrix.
    Furthermore, it correctly calculates the degrees of freedom to allow
    for information criteria rather than cross-validation with very large
    data. Finally, the interface to compiled code avoids unnecessary
    copies and allows for the use of long integers.

SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de

Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.

Provides an implementation of the Sparse ICA method in Wang et al. (2024) <doi:10.1080/01621459.2024.2370593> for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency.

Computation of sparse portfolios for financial index tracking, i.e., joint
    selection of a subset of the assets that compose the index and computation
    of their relative weights (capital allocation). The level of sparsity of the
    portfolios, i.e., the number of selected assets, is controlled through a
    regularization parameter. Different tracking measures are available, namely,
    the empirical tracking error (ETE), downside risk (DR), Huber empirical
    tracking error (HETE), and Huber downside risk (HDR). See vignette for a
    detailed documentation and comparison, with several illustrative examples.
    The package is based on the paper:
    K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional
    Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1,
    pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.