Reverse Engineering Decoupled and Censored Data
An issue occurs when authors may have privy too but normally do not reveal clear information.  Decoupled variables (e.g. separate averages) and numeric censoring (e.g. between ages 10-15) are reoccurring instances found in areas ranging from demographic and epidemiological data to ecological inference problems.  Decoupled variables provide no availability for cross tabulations while censoring obscures the true underlying values.  The revengc R package was developed to reverse engineer this unclear information that is 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).  There are two main functions in revengc and both fit data to a Poisson or Quasi-Poisson distribution.  The estimated_lambda function takes a univariate censored frequency table and approximates its lambda (average) value.  The rec function calculates an uncensored bivariate table from decoupled and summarized arguments.

An issue occurs when authors do not reveal clear information.  Decoupled variables (e.g. separate averages) and numeric censoring (e.g. between ages 10-15) are reoccurring instances found in areas ranging from demographic and epidemiological data to ecological inference problems.  Decoupled variables provide no availability for cross tabulations while censoring obscures the true underlying values.  The revengc R package was developed to reverse engineer this unclear information that is 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).  There are two main functions in revengc and both fit data to a Poisson or Quasi-Poisson distribution.  The estimated_lambda function takes a univariate censored frequency table and approximates its lambda (average) value.  The rec function calculates an uncensored bivariate table from decoupled and summarized arguments.