Package: Glarmadillo 1.1.1

Alessandro Meng

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>.

Authors:Alessandro Meng [aut, cre]

Glarmadillo_1.1.1.tar.gz
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Glarmadillo.pdf |Glarmadillo.html
Glarmadillo/json (API)

# Install 'Glarmadillo' in R:
install.packages('Glarmadillo', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

2.70 score 133 downloads 4 exports 2 dependencies

Last updated 12 months agofrom:d3aedc495c. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 10 2024
R-4.5-linux-x86_64OKDec 10 2024

Exports:find_lambda_by_sparsitygenerate_sparse_cov_matrixgenerate_specific_shape_sparse_cov_matrixglarma

Dependencies:RcppRcppArmadillo

introduction

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Dec 10 2024.

Last update: 2023-12-16
Started: 2023-12-16