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.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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

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

1.62 score 2 stars 21 scripts 215 downloads 6 exports 27 dependencies

Last updated 4 years agofrom:2320b4d53e. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-linux-x86_64NOTENov 07 2024

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

Dependencies:abindclicodacorpcorcpp11fansiglassogluehugeigraphlatticelifecyclelongitudinalmagrittrMASSMatrixmvtnormnetworkpillarpkgconfigRcppRcppEigenrlangstatnet.commontibbleutf8vctrs