Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Linear Regression Stability to Significance Reversal
Andrej-Nikolai Spiess <a.spiess@uke.uni-hamburg.de>
Michal Burdukiewicz <michalburdukiewicz@gmail.com>
Stefan Roediger <stefan.roediger@b-tu.de>
Tests linear regressions for significance reversal through leave-one(multiple)-out and shifting/addition of response values. The paradigm of the package is loosely based on the somewhat forgotten "dfstat" criterion (Belsley, Kuh & Welsch 1980 <doi:10.1002/0471725153.ch2>), which tests influential values in linear models from their effect on statistical inference, i.e. changes in p-value.