Colleagues,
Cyrus Samii and I are pleased to announce the first release of "ri: R
package for performing randomization-based inference for experiments,"
available on CRAN
(http://cran.r-project.org/web/packages/ri/index.html).
The package provides a set of tools for conducting exact or
approximate randomization-based inference for experiments. The primary
functionality of the package lies in the generation, manipulation and
use of permutation matrices implied by given experimental designs.
Among other features, the package facilitates estimation of average
treatment effects, constant effects variance estimation, randomization
inference for significance testing against sharp null hypotheses and
visualization of data and results. The ri package may be used with a
variety of experimental designs, including block randomization,
cluster randomization, restricted randomization and user-specified
alternatives. Estimation options include weighted and unweighted
difference-in-means, regression, Horvitz-Thompson, and instrumental
variables.
The package is also designed to be a supplement to the new textbook
from Alan Gerber and Don Green, _Field Experiments: Design, Analysis,
and Interpretation_
(http://books.wwnorton.com/books/978-0-393-97995-4/). Example code and
datasets associated with the book are also available online
(http://isps.research.yale.edu/research/data/fedai/).
The package is still in beta (version 0.9), and we would greatly
appreciate your feedback on its functionality.
Best,
Peter Aronow ([log in to unmask])
Cyrus Samii ([log in to unmask])
--
Peter M. Aronow
Department of Political Science, Yale University
http://j.mp/paronow
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