Glarmadillo: Solve the Graphical Lasso Problem with 'Armadillo'
Efficiently implements the Graphical Lasso algorithm,
utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm
introduces an L1 penalty to derive sparse inverse covariance matrices from
observations of multivariate normal distributions. Features include the
generation of random and structured sparse covariance matrices, beneficial
for simulations, statistical method testing, and educational purposes in
graphical modeling. A unique function for regularization parameter selection
based on predefined sparsity levels is also offered, catering to users with
specific sparsity requirements in their models. The methodology for sparse
inverse covariance estimation implemented in this package is based on the
work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.
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