CHEMIST: Causal Inference with High-Dimensional Error-Prone Covariates and Misclassified Treatments

We aim to deal with the average treatment effect (ATE), where the data are subject to high-dimensionality and measurement error. This package primarily contains two functions, which are used to generate artificial data and estimate ATE with high-dimensional and error-prone data accommodated.

Version: 0.1.5
Depends: R (≥ 3.3.1), MASS
Imports: stats, XICOR, LaplacesDemon
Published: 2023-05-01
Author: Wei-Hsin Hsu [aut, cre], Li-Pang Chen [aut]
Maintainer: Wei-Hsin Hsu <anson60214 at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: CHEMIST results

Documentation:

Reference manual: CHEMIST.pdf

Downloads:

Package source: CHEMIST_0.1.5.tar.gz
Windows binaries: r-devel: CHEMIST_0.1.5.zip, r-release: CHEMIST_0.1.5.zip, r-oldrel: CHEMIST_0.1.5.zip
macOS binaries: r-release (arm64): CHEMIST_0.1.5.tgz, r-oldrel (arm64): CHEMIST_0.1.5.tgz, r-release (x86_64): CHEMIST_0.1.5.tgz
Old sources: CHEMIST archive

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