Ecological Inference in 2x2 Tables
Black Illiteracy Rates in 1910 US Census
Fitting the Parametric Bayesian Model of Ecological Inference in 2x2 T...
Calculating the Bounds for Ecological Inference in RxC Tables
Fitting Parametric Models and Quantifying Missing Information for Ecol...
Fitting the Nonparametric Bayesian Models of Ecological Inference in 2...
Foreign-born literacy in 1930
Foreign-born literacy in 1930, County Level
Electoral Results for the House and Presidential Races in 1988
Out-of-Sample Posterior Prediction under the Parametric Bayesian Model...
Out-of-Sample Posterior Prediction under the Nonparametric Bayesian Mo...
Out-of-Sample Posterior Prediction under the Nonparametric Bayesian Mo...
Out-of-Sample Posterior Prediction under the Parametric Bayesian Model...
Print the Summary of the Results for the Bayesian Parametric Model for...
Print the Summary of the Results for the Maximum Likelihood Parametric...
Print the Summary of the Results for the Bayesian Nonparametric Model ...
Fitting the Parametric Bayesian Model of Ecological Inference in 2x2 T...
Voter Registration in US Southern States
Summarizing the Results for the Bayesian Parametric Model for Ecologic...
Summarizing the Results for the Maximum Likelihood Parametric Model fo...
Summarizing the Results for the Bayesian Nonparametric Model for Ecolo...
Calculate the variance or covariance of the object
Black voting rates for Wallace for President, 1968
Implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 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.