Package: mixture 2.1.1

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]

mixture_2.1.1.tar.gz
mixture_2.1.1.tar.gz(r-4.5-noble)mixture_2.1.1.tar.gz(r-4.4-noble)
mixture_2.1.1.tgz(r-4.4-emscripten)mixture_2.1.1.tgz(r-4.3-emscripten)
mixture.pdf |mixture.html
mixture/json (API)

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

Peer review:

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

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

gslopenblascppopenmp

3.38 score 1 stars 10 packages 20 scripts 920 downloads 2 mentions 18 exports 5 dependencies

Last updated 11 months agofrom:e6f0d3e23a. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 25 2024
R-4.5-linux-x86_64OKNov 25 2024

Exports:ARIe_stepget_best_modelghpcmgpcmmain_loopmain_loop_ghmain_loop_stmain_loop_tmain_loop_vgMAPpcmstpcmtpcmvgpcmz_ig_kmeansz_ig_random_hardz_ig_random_soft

Dependencies:BHlatticeRcppRcppArmadilloRcppGSL