Package: cbl 0.1.2
cbl: Causal Discovery under a Confounder Blanket
Methods for learning causal relationships among a set of foreground variables X based on signals from a (potentially much larger) set of background variables Z, which are known non-descendants of X. The confounder blanket learner (CBL) uses sparse regression techniques to simultaneously perform many conditional independence tests, with complementary pairs stability selection to guarantee finite sample error control. CBL is sound and complete with respect to a so-called "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) <arxiv:2205.05715>.
Authors:
cbl_0.1.2.tar.gz
cbl_0.1.2.zip(r-4.7)cbl_0.1.2.zip(r-4.6)cbl_0.1.2.zip(r-4.5)
cbl_0.1.2.tgz(r-4.6-any)cbl_0.1.2.tgz(r-4.5-any)
cbl_0.1.2.tar.gz(r-4.7-any)cbl_0.1.2.tar.gz(r-4.6-any)
cbl_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
cbl/json (API)
| # Install 'cbl' in R: |
| install.packages('cbl', repos = c('https://dswatson.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dswatson/cbl/issues
- bipartite - Simulated data
Last updated from:52eca9d3a1. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 124 | ||
| source / vignettes | OK | 165 | ||
| linux-release-x86_64 | NOTE | 110 | ||
| macos-release-arm64 | NOTE | 165 | ||
| macos-oldrel-arm64 | NOTE | 205 | ||
| windows-devel | NOTE | 88 | ||
| windows-release | NOTE | 128 | ||
| windows-oldrel | NOTE | 87 | ||
| wasm-release | OK | 95 |
Exports:cbl
Dependencies:codetoolsdata.tabledigestdoRNGforeachglmnetiteratorsjsonlitelatticelightgbmMatrixR6RcppRcppEigenrngtoolsshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simulated data | bipartite |
| Confounder blanket learner | cbl |
| Computer the consistency lower bound | epsilon_fn |
| Feature selection subroutine | l0 |
| CPSS upper bound | minD |
| CPSS utility functions | r.TailProbs |
| Infer causal direction using stability selection | ss_fn |
| Complementary pairs subsampling loop | sub_loop |
