Welcome to ClientVPS Mirrors

CRAN: Package PCL

PCL: Proximal Causal Learning

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

Documentation:

Reference manual: PCL.html , PCL.pdf

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=PCL to link to this page.

Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.

This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.