Title: Bayesian Instrumental Variable Estimation and Model
Determination via Conditional Bayes Factors
Author: Alex Lenkoski, Anna Karl, Andreas Neudecker
Description: This package provices functions to estimate the parameters
for linear regression equations using gibbs sampling and

Description: This package allows one incorporate instrument and
covariate uncertainty into instrumental variable regression.

Description: This package allows one incorporate instrument and covariate uncertainty into instrumental variable regression.

Title: Profiling Compliers and Non-Compliers for Instrumental Variable
Author: Moritz Marbach <moritz.marbach@gess.ethz.ch> [aut, cre]
Description: Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2019) <doi:10.2139/ssrn.3380247>.

Description: Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <arXiv:2103.06328>.

Title: Hypothesis Testing in Cluster-Randomized Encouragement Designs
Description: An implementation of randomization-based hypothesis
testing for three different estimands in a cluster-randomized
encouragement experiment. The three estimands include (1) testing
a cluster-level constant proportional treatment effect (Fisher's
sharp null hypothesis), (2) pooled effect ratio, and (3) average
cluster effect ratio. To test the third estimand, user needs to install
'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to

Title: Ensures Mutually Consistent Beliefs When Using IVs
person("Frank", "DiTraglia", email = "francis.ditraglia@economics.ox.ac.uk", role = "aut"),
person("Mallick", "Hossain", email = "emallickhossain@gmail.com", role = c("aut", "cre")))
Description: Uses data and researcher's beliefs on measurement error and
instrumental variable (IV) endogeneity to generate the space of consistent
beliefs across measurement error, instrument endogeneity, and instrumental
Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.
Imports: AER, coda, data.table, graphics, MASS, Rcpp (>= 0.11.6), rgl,
Suggests: testthat, haven, MCMCpack, knitr, rmarkdown