Package: drcarlate 1.2.0

Mingxin Zhang

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>.

Authors:Liang Jiang [aut, cph], Oliver B. Linton [aut, cph], Haihan Tang [aut, cph], Yichong Zhang [aut, cph], Mingxin Zhang [cre]

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NEWS

# Install 'drcarlate' in R:
install.packages('drcarlate', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • data_table - Data used to reproduce Table 5 results in Jiang et. al.

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 3 scripts 181 downloads 17 exports 22 dependencies

Last updated 1 years agofrom:c429da0a40. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-linuxOKNov 07 2024

Exports:ATEDGPATEJLTZATEOutputATETrueValueCovAdptRndfeasiblePostLassoMatToolFuncDGPJLTZLinearLogitLogisticRegnorminvOutputpihatsplinebasisstanEtauTrueValue

Dependencies:clicodetoolsforeachglmnetglueiteratorslatticelifecyclemagrittrMASSMatrixpracmapurrrRcppRcppEigenrlangshapesplus2Rstringistringrsurvivalvctrs

Introduction to drcarlate

Rendered fromIntroduction_to_drcarlate.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2023-06-12
Started: 2023-06-12