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imPois (version 0.0.7.5)

Imprecise Inferential Framework for Poisson Sampling Model

Description

A collection of tools for conducting an imprecise inference is provided. Imprecise prior is used for this inference, and imprecise probability theory introduced by Peter Walley (1991) is its underlying theoretical foundation. The package is developed based on the PhD thesis work of Lee (2014). Poisson and zero-truncated Poisson sampling models are mainly studied with two types of prior distributions.

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Install

install.packages('imPois')

Monthly Downloads

28

Version

0.0.7.5

License

GPL (>= 2)

Maintainer

Chel Hee Lee

Last Published

November 28th, 2015

Functions in imPois (0.0.7.5)

imPois

Imprecise Inferential Framework for Poisson Sampling Model
print.summary.impinf

Print Imprecise Objects
fn.evfn

Objective And Gradient Vector Needed For Optimization
plot.impinf

Plotting Imprecise Objects
cgf

Comupting Normalizing Constant of Bickis and Lee's Probability Distribution
summary.impinf

Summary of impinf object
dcpm

Imprecise Probability Distribution
iprior

Characterize Imprecise Prior
update.impinf

Applying Bayes Rule
evfn

Expected Value of Canonical Variable
kcpm

Kernel of Imprecise Probability Measure Formulated By Bickis and Lee