Package: SparseTSCGM 4.0

Fentaw Abegaz

SparseTSCGM: Sparse Time Series Chain Graphical Models

Computes sparse vector autoregressive coefficients and precision matrices for time series chain graphical models. Fentaw Abegaz and Ernst Wit (2013) <doi:10.1093/biostatistics/kxt005>.

Authors:Fentaw Abegaz [aut, cre], Ernst Wit [aut]

SparseTSCGM_4.0.tar.gz
SparseTSCGM_4.0.zip(r-4.5)SparseTSCGM_4.0.zip(r-4.4)SparseTSCGM_4.0.zip(r-4.3)
SparseTSCGM_4.0.tgz(r-4.5-x86_64)SparseTSCGM_4.0.tgz(r-4.5-arm64)SparseTSCGM_4.0.tgz(r-4.4-x86_64)SparseTSCGM_4.0.tgz(r-4.4-arm64)SparseTSCGM_4.0.tgz(r-4.3-x86_64)SparseTSCGM_4.0.tgz(r-4.3-arm64)
SparseTSCGM_4.0.tar.gz(r-4.5-noble)SparseTSCGM_4.0.tar.gz(r-4.4-noble)
SparseTSCGM_4.0.tgz(r-4.4-emscripten)SparseTSCGM_4.0.tgz(r-4.3-emscripten)
SparseTSCGM.pdf |SparseTSCGM.html✨
SparseTSCGM/json (API)

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:
  • mammary - Microarray gene expression time course data for mammary gland development in mice

On CRAN:

Conda:

archivedpackagesr-universe

1.70 score 5 stars 187 downloads 6 exports 27 dependencies

Last updated 2 days agofrom:e88e9bac65 (on package/SparseTSCGM). Checks:8 OK, 4 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKApr 01 2025
R-4.5-win-x86_64NOTEApr 01 2025
R-4.5-mac-x86_64NOTEApr 01 2025
R-4.5-mac-aarch64NOTEApr 01 2025
R-4.5-linux-x86_64NOTEApr 01 2025
R-4.4-win-x86_64OKApr 01 2025
R-4.4-mac-x86_64OKApr 01 2025
R-4.4-mac-aarch64OKApr 01 2025
R-4.4-linux-x86_64OKApr 01 2025
R-4.3-win-x86_64OKApr 01 2025
R-4.3-mac-x86_64OKApr 01 2025
R-4.3-mac-aarch64OKApr 01 2025

Exports:plot.tscgmplot.tscgm.ar2print.tscgmsim.datasparse.tscgmsummary.tscgm

Dependencies:abindclicodacorpcorcpp11fansiglassogluehugeigraphlatticelifecyclelongitudinalmagrittrMASSMatrixmvtnormnetworkpillarpkgconfigRcppRcppEigenrlangstatnet.commontibbleutf8vctrs