Package: eco
Version: 3.1-1
Date: 2007-6-25
Title: R Package for Ecological Inference in 2x2 Tables
Author: Kosuke Imai <kimai@princeton.edu>,
Ying Lu <ying.lu@colorado.edu>,
Aaron Strauss <abstraus@Princeton.EDU>.
Maintainer: Ying Lu <ying.lu@colorado.edu>
Depends: R (>= 2.0), MASS
Description: 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.
LazyLoad: yes
LazyData: yes
License: GPL (version 2 or later)
URL: http://imai.princeton.edu/research/eco.html
Packaged: Thu Jun 28 21:03:08 2007; hornik

Package: eco
Version: 3.1-2
Date: 2009-1-29
Title: R Package for Ecological Inference in 2x2 Tables
Author: Kosuke Imai <kimai@princeton.edu>,
Ying Lu <ying.lu@colorado.edu>,
Aaron Strauss <abstraus@Princeton.EDU>.
Maintainer: Ying Lu <ying.lu@colorado.edu>
Depends: R (>= 2.0), MASS
Description: 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.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
URL: http://imai.princeton.edu/research/eco.html
Packaged: Fri Jan 30 21:40:39 2009; kimai