Inference on Single Binomial Proportion and Bayesian Computations

Abundant statistical literature has revealed the importance of constructing and evaluating various methods for constructing confidence intervals (CI) for single binomial proportion (p). We comprehensively provide procedures in frequentist (approximate with or without adding pseudo counts or continuity correction or exact) and in Bayesian cultures. Evaluation procedures for CI warrant active computational attention and required summaries pertaining to four criterion (coverage probability, expected length, p-confidence, p-bias, and error) are implemented.


R package for proportion

Multiple procedures to obtain an interval estimate for an unknown proportion (p) based on binomial sampling. It is known that approximations are poor when the true p is close to zero or to one. So we provide alternative procedures with better properties. Non-iterative methods widely discussed in litrature for computing a (central) two-sided interval estimate for p are implemented in terms of coverage probability and expected length.

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("proportion")

2.0.0 by Rajeswaran Viswanathan, 5 months ago


https://github.com/RajeswaranV/proportion


Report a bug at https://github.com/RajeswaranV/proportion/issues


Browse source code at https://github.com/cran/proportion


Authors: M.Subbiah, V.Rajeswaran


Documentation:   PDF Manual  


GPL-2 license


Imports TeachingDemos, ggplot2

Suggests knitr, rmarkdown


See at CRAN