Package: SAGMM 0.2.5
Andrew Thomas 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:
SAGMM_0.2.5.tar.gz
SAGMM_0.2.5.zip(r-4.7-x86_64)SAGMM_0.2.5.zip(r-4.7-arm64)SAGMM_0.2.5.zip(r-4.6-x86_64)SAGMM_0.2.5.zip(r-4.6-arm64)
SAGMM_0.2.5.tar.gz(r-4.7-arm64)SAGMM_0.2.5.tar.gz(r-4.7-x86_64)SAGMM_0.2.5.tar.gz(r-4.6-arm64)SAGMM_0.2.5.tar.gz(r-4.6-x86_64)
SAGMM_0.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SAGMM/json (API)
| # Install 'SAGMM' in R: |
| install.packages('SAGMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:fbf1f45bd0. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 95 | ||
| linux-devel-x86_64 | OK | 109 | ||
| source / vignettes | OK | 194 | ||
| linux-release-arm64 | OK | 96 | ||
| linux-release-x86_64 | OK | 114 | ||
| windows-devel-arm64 | OK | 197 | ||
| windows-devel-x86_64 | OK | 127 | ||
| windows-release-arm64 | OK | 203 | ||
| windows-release-x86_64 | OK | 103 | ||
| wasm-release | OK | 94 |
Exports:gainFactorsgenerateSimDataSAGMMFit
Dependencies:lowmemtkmeansMASSmclustMixSimRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Return Gamma, a sequence of gain factors | gainFactors |
| Generate data for simulations to test the SAGMM package.. | generateSimData |
| SAGMM: A package for Clustering via Stochastic Approximation and Gaussian Mixture Models. | SAGMM-package SAGMM |
| Clustering via Stochastic Approximation and Gaussian Mixture Models (GMM) | SAGMMFit |