The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <doi:10.48550/arXiv.2009.10982>.
| Version: | 1.0 |
| Depends: | R (≥ 4.0) |
| Published: | 2021-04-10 |
| DOI: | 10.32614/CRAN.package.PCL |
| Author: | Andrew Ying [aut, cre], Yifan Cui [ctb], AmirEmad Ghassami [ctb] |
| Maintainer: | Andrew Ying <aying9339 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | PCL results |
| Reference manual: | PCL.pdf |
| Package source: | PCL_1.0.tar.gz |
| Windows binaries: | r-devel: PCL_1.0.zip, r-release: PCL_1.0.zip, r-oldrel: PCL_1.0.zip |
| macOS binaries: | r-release (arm64): PCL_1.0.tgz, r-oldrel (arm64): PCL_1.0.tgz, r-release (x86_64): PCL_1.0.tgz, r-oldrel (x86_64): PCL_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=PCL to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.