Package: eco
Version: 3.1-4
Date: 2009-07-12
Title: R Package for Ecological Inference in 2x2 Tables
Author: Kosuke Imai <kimai@princeton.edu>, Ying Lu <yl46@nyu.edu>,
Aaron Strauss <abstraus@Princeton.EDU>.
Maintainer: Ying Lu <yl46@nyu.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/software/eco.html
Packaged: 2009-07-13 01:26:09 UTC; ying
Repository: CRAN
Date/Publication: 2009-07-18 15:41:13

Package: eco
Version: 3.1-5
Date: 2011-04-26
Title: R Package for Ecological Inference in 2x2 Tables
Author: Kosuke Imai <kimai@princeton.edu>, Ying Lu <yl46@nyu.edu>,
Aaron Strauss <abstraus@Princeton.EDU>.
Maintainer: Ying Lu <yl46@nyu.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/software/eco.html
Packaged: 2011-04-26 11:15:21 UTC; kimai
Repository: CRAN
Date/Publication: 2011-04-26 11:50:22