Package: mixture 2.2.0

Paul D. McNicholas

mixture: Mixture Models for Clustering and Classification

An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.

Authors:Nik Pocuca [aut], Ryan P. Browne [aut], Paul D. McNicholas [aut, cre], Alexa A. Sochaniwsky [aut]

mixture_2.2.0.tar.gz
mixture_2.2.0.tar.gz(r-4.7-arm64)mixture_2.2.0.tar.gz(r-4.7-x86_64)mixture_2.2.0.tar.gz(r-4.6-arm64)mixture_2.2.0.tar.gz(r-4.6-x86_64)
mixture_2.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mixture/json (API)

# Install 'mixture' in R:
install.packages('mixture', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • sx2 - Skewed Simulated Data 1
  • sx3 - Skewed Simulated Data 2
  • x2 - Simulated Data

On CRAN:

Conda:

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

gslopenblascppopenmp

3.39 score 1 stars 17 packages 24 scripts 801 downloads 2 mentions 22 exports 5 dependencies

Last updated from:2ffa4184c9. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK163
linux-devel-x86_64OK165
source / vignettesOK220
linux-release-arm64OK176
linux-release-x86_64OK179
wasm-releaseOK192

Exports:ARIdmgdmghdmstdmvge_stepget_best_modelghpcmgpcmmain_loopmain_loop_ghmain_loop_stmain_loop_tmain_loop_vgMAPpcmstpcmtpcmvgpcmz_ig_kmeansz_ig_random_hardz_ig_random_soft

Dependencies:BHlatticeRcppRcppArmadilloRcppGSL