list is a publicly available R package that allows
researchers to conduct multivariate statistical analyses of
survey data with list experiments. In addition, the package
implements the statistical test that is designed to detect
certain failures of list experiments. This survey methodology
is also known as the item count technique or the unmatched
count technique and is an alternative to the commonly used
randomized response method. The package implements the methods
developed by Imai (2011) and Blair and Imai (2012), a Bayesian
MCMC implementation of regression for the standard and multiple
sensitive item list experiment designs including ceiling and
floor effects and a random effects setup, and a Bayesian MCMC
hierarchical regression model with up to three hierarchical
groups.

list is a publicly available R package that allows
researchers to conduct multivariate statistical analyses of
survey data with list experiments. In addition, the package
implements the statistical test that is designed to detect
certain failures of list experiments. This survey methodology
is also known as the item count technique or the unmatched
count technique and is an alternative to the commonly used
randomized response method. The package implements the methods
developed by Imai (2011), Blair and Imai (2012), and Blair,
Imai, and Lyall (2013). This includes a Bayesian MCMC
implementation of regression for the standard and multiple
sensitive item list experiment designs and a random effects
setup, a Bayesian MCMC hierarchical regression model with up to
three hierarchical groups, and the combined list experiment and
endorsement experiment regression model.

Graeme Blair <gblair@Princeton.edu>, Kosuke Imai
<kimai@Princeton.edu>, Bethany Park <bapark@princeton.edu>
list is a publicly available R package that allows
researchers to conduct multivariate statistical analyses of
survey data with list experiments. In addition, the package
implements the statistical test that is designed to detect
certain failures of list experiments. This survey methodology
is also known as the item count technique or the unmatched
count technique and is an alternative to the commonly used
randomized response method. The package implements the methods
developed by Imai (2011), Blair and Imai (2012), Blair,
Imai, and Lyall (2013), and Imai, Park, and Greene (2014).
This includes a Bayesian MCMC implementation of regression
for the standard and multiple sensitive item list experiment
designs and a random effects setup, a Bayesian MCMC hierarchical
regression model with up to three hierarchical groups, the
combined list experiment and endorsement experiment regression
model, and a joint model of the list experiment that enables
the analysis of the list experiment as a predictor in
outcome regression models.