drcarlate: Improving Estimation Efficiency in CAR with Imperfect Compliance

We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) <doi:10.48550/arXiv.2201.13004>.

Version: 1.2.0
Depends: R (≥ 2.10)
Imports: pracma, MASS, stringr, splus2R, glmnet, stats, purrr
Suggests: knitr, rmarkdown
Published: 2023-06-12
Author: Liang Jiang [aut, cph], Oliver B. Linton [aut, cph], Haihan Tang [aut, cph], Yichong Zhang [aut, cph], Mingxin Zhang [cre]
Maintainer: Mingxin Zhang <21110680035 at m.fudan.edu.cn>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: drcarlate results


Reference manual: drcarlate.pdf
Vignettes: Introduction to drcarlate


Package source: drcarlate_1.2.0.tar.gz
Windows binaries: r-devel: drcarlate_1.2.0.zip, r-release: drcarlate_1.2.0.zip, r-oldrel: drcarlate_1.2.0.zip
macOS binaries: r-release (arm64): drcarlate_1.2.0.tgz, r-oldrel (arm64): drcarlate_1.2.0.tgz, r-release (x86_64): drcarlate_1.2.0.tgz, r-oldrel (x86_64): drcarlate_1.2.0.tgz


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