Package: SAGMM 0.2.5

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 Thomas Jones [aut, cre], Hien Duy Nguyen [aut]

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.zip(r-4.5-x86_64)
SAGMM_0.2.5.tgz(r-4.6-x86_64)SAGMM_0.2.5.tgz(r-4.6-arm64)SAGMM_0.2.5.tgz(r-4.5-x86_64)SAGMM_0.2.5.tgz(r-4.5-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://andrewthomasjones.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/andrewthomasjones/sagmm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

2.70 score 8 scripts 264 downloads 1 mentions 3 exports 6 dependencies

Last updated from:57d4b6b240. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK144
linux-devel-x86_64OK136
source / vignettesOK277
linux-release-arm64OK138
linux-release-x86_64OK136
macos-release-arm64OK122
macos-release-x86_64OK232
macos-oldrel-arm64OK111
macos-oldrel-x86_64OK376
windows-devel-arm64OK220
windows-devel-x86_64OK157
windows-release-arm64OK222
windows-release-x86_64OK199
windows-oldrel-x86_64OK127
wasm-releaseOK121

Exports:gainFactorsgenerateSimDataSAGMMFit

Dependencies:lowmemtkmeansMASSmclustMixSimRcppRcppArmadillo