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noncomplyR  

Bayesian Analysis of Randomized Experiments with Non-Compliance
View on CRAN: Click here


Download and install noncomplyR package within the R console
Install from CRAN:
install.packages("noncomplyR")

Install from Github:
library("remotes")
install_github("cran/noncomplyR")

Install by package version:
library("remotes")
install_version("noncomplyR", "1.0")



Attach the package and use:
library("noncomplyR")
Maintained by
Scott Coggeshall
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-08-24
Latest Update: 2017-08-24
Description:
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) . 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) .
How to cite:
Scott Coggeshall (2017). noncomplyR: Bayesian Analysis of Randomized Experiments with Non-Compliance. R package version 1.0, https://cran.r-project.org/web/packages/noncomplyR. Accessed 26 Jun. 2026.
Previous versions and publish date:
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Complete documentation for noncomplyR
Functions, R codes and Examples using the noncomplyR R package
Some associated functions: cace . compliance_chain . noncomplyR . summarize_chain . vitaminA . 
Some associated R codes: data_aug_functions.R .  Full noncomplyR package functions and examples
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