Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.

Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) <https://orbilu.uni.lu/handle/10993/63425>.

Provides an R interface to the 'sparseLM' C library for
    large-scale nonlinear least squares problems with arbitrarily sparse
    Jacobians. The underlying solver implements a sparse variant of the
    Levenberg-Marquardt algorithm for minimizing sum-of-squares objective
    functions, supports user-supplied analytic Jacobians or finite-difference
    approximation, and is designed to exploit sparsity for improved memory use
    and performance. This package exposes the solver in R and uses sparse
    matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines
    through the 'Matrix' package interface. Methods from the C library are
    described in Lourakis (2010) <doi:10.1007/978-3-642-15552-9_4>.

Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.

Provides an S4 class for representing and
    interacting with sparse plus rank matrices. At the moment the
    implementation is quite spare, but the plan is eventually subclass
    Matrix objects.

Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.

Provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) <doi:10.1145/992200.992206>.

Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as
  standard R subsetting and Kronecker products.

The 'sparseMatEst' package provides functions for estimating sparse 
  covariance and precision matrices with error control. A false positive
  rate is fixed corresponding to the probability of falsely including a matrix 
  entry in the support of the estimator.  It uses the binary search
  method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in 
  Kashlak (2019) <arXiv:1903.10988>.