Package: SAGMM 0.2.4

Andrew T. Jones

SAGMM: Clustering via Stochastic Approximation and Gaussian Mixture Models

Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.

Authors:Andrew T. Jones, Hien D. Nguyen

SAGMM_0.2.4.tar.gz
SAGMM_0.2.4.tar.gz(r-4.5-noble)SAGMM_0.2.4.tar.gz(r-4.4-noble)
SAGMM_0.2.4.tgz(r-4.4-emscripten)SAGMM_0.2.4.tgz(r-4.3-emscripten)
SAGMM.pdf |SAGMM.html✨
SAGMM/json (API)
NEWS

# Install 'SAGMM' in R:
install.packages('SAGMM', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

openblascpp

1.00 score 3 scripts 153 downloads 1 mentions 3 exports 6 dependencies

Last updated 6 years agofrom:698d3e53c3. Checks:1 OK, 1 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 12 2025
R-4.5-linux-x86_64NOTEFeb 12 2025

Exports:gainFactorsgenerateSimDataSAGMMFit

Dependencies:lowmemtkmeansMASSmclustMixSimRcppRcppArmadillo

Citation

To cite package ‘SAGMM’ in publications use:

Jones AT, Nguyen HD (2019). SAGMM: Clustering via Stochastic Approximation and Gaussian Mixture Models. R package version 0.2.4, https://CRAN.R-project.org/package=SAGMM.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {SAGMM: Clustering via Stochastic Approximation and
      Gaussian Mixture Models},
    author = {Andrew T. Jones and Hien D. Nguyen},
    year = {2019},
    note = {R package version 0.2.4},
    url = {https://CRAN.R-project.org/package=SAGMM},
  }