Package: mDAG 1.2.3
mDAG: Inferring Causal Network from Mixed Observational Data Using a Directed Acyclic Graph
Learning a mixed directed acyclic graph based on both continuous and categorical data.
Authors:
mDAG_1.2.3.tar.gz
mDAG_1.2.3.zip(r-4.7)mDAG_1.2.3.zip(r-4.6)mDAG_1.2.3.zip(r-4.5)
mDAG_1.2.3.tgz(r-4.6-x86_64)mDAG_1.2.3.tgz(r-4.6-arm64)mDAG_1.2.3.tgz(r-4.5-x86_64)mDAG_1.2.3.tgz(r-4.5-arm64)
mDAG_1.2.3.tar.gz(r-4.7-arm64)mDAG_1.2.3.tar.gz(r-4.7-x86_64)mDAG_1.2.3.tar.gz(r-4.6-arm64)mDAG_1.2.3.tar.gz(r-4.6-x86_64)
mDAG_1.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mDAG/json (API)
| # Install 'mDAG' in R: |
| install.packages('mDAG', repos = c('https://wjzhong.r-universe.dev', 'https://cloud.r-project.org')) |
- example_data - Example data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:d80cd66fa3. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 216 | ||
| linux-devel-x86_64 | OK | 208 | ||
| source / vignettes | OK | 291 | ||
| linux-release-arm64 | OK | 246 | ||
| linux-release-x86_64 | OK | 336 | ||
| macos-release-arm64 | OK | 222 | ||
| macos-release-x86_64 | OK | 450 | ||
| macos-oldrel-arm64 | OK | 205 | ||
| macos-oldrel-x86_64 | OK | 308 | ||
| windows-devel | OK | 218 | ||
| windows-release | OK | 200 | ||
| windows-oldrel | OK | 218 | ||
| wasm-release | OK | 226 |
Exports:mDAG
Dependencies:abindbackportsbase64encbdsmatrixBHBiocGenericsBiocManagerbitbit64bnlearnbootbroombslibcachemcheckmateclicliprclueclustercodetoolscolorspacecorpcorcpp11crayondata.tableDEoptimRdigestdplyrevaluatefarverfastICAfastmapfdrtoolfontawesomeforcatsforeachforeignFormulaformula.toolsfsgenericsggmggplot2glassoglmnetglueGPArotationgraphgridExtragtablegtoolshavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsigraphisobanditeratorsjomojpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclelme4lmtestlogistfmagrittrMASSMatrixmemoisemgcvmgmmicemimeminqamitmlmnormtnlmenloptrnnetnumDerivoperator.toolsordinalpanpbapplypbivnormpcalgpillarpkgconfigplyrpngprettyunitsprogresspsychpurrrqgraphquadprogR6rappdirsRBGLrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasreshape2rlangrmarkdownrobustbaserpartrstudioapiS7sassscalessfsmiscshapestringistringrsurvivaltibbletidyrtidyselecttinytextzdbucminfutf8vcdvctrsviridisLitevroomwithrxfunyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Example data | example_data |
| Inferring Causal Network from Mixed Observational Data Using a Directed Acyclic Graph | mDAG |
