Package: noncomplyR 1.0

noncomplyR: Bayesian Analysis of Randomized Experiments with Non-Compliance

Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.

Authors:Scott Coggeshall [aut, cre]

noncomplyR_1.0.tar.gz
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noncomplyR.pdf |noncomplyR.html✨
noncomplyR/json (API)

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

Peer review:

Datasets:
  • vitaminA - Vitamin A Randomized Trial Data Set

On CRAN:

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

2.00 score 7 scripts 126 downloads 3 exports 10 dependencies

Last updated 7 years agofrom:1a9f8c2b42. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winOKNov 15 2024
R-4.5-linuxOKNov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:cacecompliance_chainsummarize_chain

Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival

Introduction to noncomplyR

Scott Coggeshall

Rendered fromnoncomplyR.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2017-08-24
Started: 2017-08-24