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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) <doi:10.48550/arXiv.2205.05715>.
Version: | 0.1.3 |
Depends: | R (≥ 3.5.0) |
Imports: | data.table, foreach, glmnet, lightgbm |
Published: | 2022-12-20 |
DOI: | 10.32614/CRAN.package.cbl |
Author: | David Watson [aut, cre] |
Maintainer: | David Watson <david.s.watson11 at gmail.com> |
License: | GPL (≥ 3) |
URL: | https://github.com/dswatson/cbl |
NeedsCompilation: | no |
Materials: | NEWS |
CRAN checks: | cbl results |
Reference manual: | cbl.pdf |
Package source: | cbl_0.1.3.tar.gz |
Windows binaries: | r-devel: cbl_0.1.3.zip, r-release: cbl_0.1.3.zip, r-oldrel: cbl_0.1.3.zip |
macOS binaries: | r-release (arm64): cbl_0.1.3.tgz, r-oldrel (arm64): cbl_0.1.3.tgz, r-release (x86_64): cbl_0.1.3.tgz, r-oldrel (x86_64): cbl_0.1.3.tgz |
Old sources: | cbl archive |
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These binaries (installable software) and packages are in development.
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