Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.

Provides a general framework for performing sparse functional
    clustering as originally described in Floriello and Vitelli (2017)
    <doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling
    data misalignment (see Vitelli, 2019, <doi:10.48550/arXiv.1912.00687>).

Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties <doi:10.1007/s11222-013-9424-2>. Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) <doi:10.1080/01621459.2020.1765784>. B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided.

Efficient implementation of sparse group lasso with optional
    bound constraints on the coefficients. 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>.