R Package for Ecological Inference in 2x2 Tables
Kosuke Imai <kimai@princeton.edu>,
Ying Lu <ying.lu@colorado.edu>,
Aaron Strauss <abstraus@Princeton.EDU>.
eco is a publicly available R package that implements the Bayesian
and likelihood methods proposed in Imai, Lu, and Strauss (2006) for
ecological inference in 2x2 tables as well as the method of
bounds introduced by Duncan and Davis (1953). The package fits both
parametric and nonparametric models using either the
Expectation-Maximization algorithms (for likelihood models) or the
Markov chain Monte Carlo algorithms (for Bayesian models). For all
models, the individual-level data can be incorporated into their
estimation whenever such data are available. The package also
provides a functionality which allows one to quantify the effect of
data aggregation on parameter estimation and hypothesis testing
under the parametric model.

eco is a publicly available R package that implements the
Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2006)
for ecological inference in $2 \times 2$ tables as well as the
method of bounds introduced by Duncan and Davis (1953). The package
fits both parametric and nonparametric models using either the
Expectation-Maximization algorithms (for likelihood models) or the
Markov chain Monte Carlo algorithms (for Bayesian models).  For all
models, the individual-level data can be directly incorporated into
the estimation whenever such data are available. Along with
in-sample and out-of-sample predictions, the package also provides a
functionality which allows one to quantify the effect of data
aggregation on parameter estimation and hypothesis testing under the
parametric likelihood models.

eco is a publicly available R package that implements the
Bayesian and likelihood methods proposed in Imai, Lu, and Strauss
(Forthcoming) for ecological inference in $2 \times 2$ tables as
well as the method of bounds introduced by Duncan and Davis (1953).
The package fits both parametric and nonparametric models using
either the Expectation-Maximization algorithms (for likelihood
models) or the Markov chain Monte Carlo algorithms (for Bayesian
models).  For all models, the individual-level data can be directly
incorporated into the estimation whenever such data are available.
Along with in-sample and out-of-sample predictions, the package also
provides a functionality which allows one to quantify the effect of data
aggregation on parameter estimation and hypothesis testing under the
parametric likelihood models.