eco is a publicly available R package that implements the
Bayesian and likelihood methods proposed in Imai, Lu, and Strauss
(2008) 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.

Kosuke Imai <kimai@princeton.edu>, Ying Lu <yl46@nyu.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 (2008) 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 (2008) 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.