Package: dppmix 0.1.2

dppmix: Determinantal Point Process Mixture Models

Multivariate Gaussian mixture model with a determinant point process prior to promote the discovery of parsimonious components from observed data. See Xu, Mueller, Telesca (2016) <doi:10.1111/biom.12482>.

Authors:Yanxun Xu [aut], Peter Mueller [aut], Donatello Telesca [aut], David J. H. Shih [aut, cre]

dppmix_0.1.2.tar.gz
dppmix_0.1.2.zip(r-4.7-x86_64)dppmix_0.1.2.zip(r-4.7-arm64)dppmix_0.1.2.zip(r-4.6-x86_64)dppmix_0.1.2.zip(r-4.6-arm64)dppmix_0.1.2.zip(r-4.5-x86_64)
dppmix_0.1.2.tgz(r-4.6-x86_64)dppmix_0.1.2.tgz(r-4.6-arm64)dppmix_0.1.2.tgz(r-4.5-x86_64)dppmix_0.1.2.tgz(r-4.5-arm64)
dppmix_0.1.2.tar.gz(r-4.7-arm64)dppmix_0.1.2.tar.gz(r-4.7-x86_64)dppmix_0.1.2.tar.gz(r-4.6-arm64)dppmix_0.1.2.tar.gz(r-4.6-x86_64)
dppmix_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
dppmix/json (API)

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

Bug tracker:https://bitbucket.org/djhshih/dppmix

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.30 score 332 downloads 2 exports 4 dependencies

Last updated from:4b8241c854. Checks:13 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING135
linux-devel-x86_64WARNING137
source / vignettesOK258
linux-release-arm64WARNING143
linux-release-x86_64WARNING132
macos-release-arm64WARNING117
macos-release-x86_64WARNING303
macos-oldrel-arm64WARNING91
macos-oldrel-x86_64WARNING229
windows-devel-arm64WARNING365
windows-devel-x86_64WARNING182
windows-release-arm64WARNING364
windows-release-x86_64WARNING144
windows-oldrel-x86_64WARNING128
wasm-releaseOK127

Exports:dppmix_mcmc_mvnormrmvnorm_clusters

Dependencies:matrixStatsmvtnormRcppRcppArmadillo