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) . 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) .


Reference manual

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1.0 by Scott Coggeshall, 4 years ago

Browse source code at

Authors: Scott Coggeshall [aut, cre]

Documentation:   PDF Manual  

GPL-2 license

Imports MCMCpack, stats

Suggests knitr

See at CRAN