Package: SparseTSCGM 5.0

SparseTSCGM: Sparse Time Series Chain Graphical Models

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

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

SparseTSCGM_5.0.tar.gz
SparseTSCGM_5.0.zip(r-4.7)SparseTSCGM_5.0.zip(r-4.6)SparseTSCGM_5.0.zip(r-4.5)
SparseTSCGM_5.0.tgz(r-4.6-x86_64)SparseTSCGM_5.0.tgz(r-4.6-arm64)SparseTSCGM_5.0.tgz(r-4.5-x86_64)SparseTSCGM_5.0.tgz(r-4.5-arm64)
SparseTSCGM_5.0.tar.gz(r-4.7-arm64)SparseTSCGM_5.0.tar.gz(r-4.7-x86_64)SparseTSCGM_5.0.tar.gz(r-4.6-arm64)SparseTSCGM_5.0.tar.gz(r-4.6-x86_64)
SparseTSCGM_5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SparseTSCGM/json (API)

# Install 'SparseTSCGM' in R:
install.packages('SparseTSCGM', repos = c('https://fentaw-r.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • mammary - Microarray gene expression time course data for mammary gland development in mice

On CRAN:

Conda:

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

1.92 score 2 stars 21 scripts 560 downloads 6 exports 25 dependencies

Last updated from:3332ceb030. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK131
linux-devel-x86_64OK130
source / vignettesOK146
linux-release-arm64OK138
linux-release-x86_64OK141
macos-release-arm64OK132
macos-release-x86_64OK303
macos-oldrel-arm64OK209
macos-oldrel-x86_64OK396
windows-develOK97
windows-releaseOK145
windows-oldrelOK124
wasm-releaseOK93

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

Dependencies:abindclicodacorpcorcpp11glassogluehugeigraphlatticelifecyclelongitudinalmagrittrMASSMatrixmvtnormnetworkpillarpkgconfigRcpprlangstatnet.commontibbleutf8vctrs