Amelia II "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries).
This program is designed to improve the estimation of causal effects via an extremely powerful method of matching that is widely applicable and exceptionally easy to understand and use (if you understand how to draw a histogram, you will understand this method). The program implements the Coarsened Exact Matching (CEM) algorithm described in:
A stand-alone, easy-to-use program for running event count and duration regression models, developed by and/or discussed in a series of journal articles by me. (Event count models have a dependent variable measured as the number of times something happens, such as the number of uncontested seats per state or the number of wars per year. Duration models explain dependent variables measured as the time until some event, such as the number of months a parliamentary cabinet endures.)
This page contains Gauss statistical, utility, and graphic procedures. Larger packages I've written are available at my statistical software page. You may prefer a zip file containing all the procedures below (last update: 8/20/2005).
JudgeIt allows a user to construct a model of a two-party election system over multiple election cycles, derive quantities of interest about the system through statistical estimation and simulation, and produce output summary statistics and graphical plots of those quantities.
"At MatchIt, we don't make parametric models, we make parametric models work better." MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods.
The ReadMe software package for R takes as input a set of text documents (such as speeches, blog posts, newspaper articles, judicial opinions, movie reviews, etc.), a categorization scheme chosen by the user (e.g., ordered positive to negative sentiment ratings, unordered policy topics, or any other mutually exclusive and exhaustive set of categories), and a small subset of text documents hand classified into the given categories.
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend.
YourCast is (open source and free) software that makes forecasts by running sets of linear regressions together in a variety of sophisticated ways. YourCast avoids the bias that results when stacking datasets from separate cross-sections and assuming constant parameters, and the inefficiency that results from running independent regressions in each cross-section.
Click here to go to the Zelig homepage. Zelig is a general purpose statistical package built on R, with a large and diverse array of methods from all theories of inference. All documentation follows the same style and mathematical notation so that if you understand one method, you'll be able to learn any other one easily. Zelig commands for all models involve the same simple three commands with the same syntax.