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BayesianPower (version 0.2.3)

Sample Size and Power for Comparing Inequality Constrained Hypotheses

Description

A collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) .

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Version

Install

install.packages('BayesianPower')

Monthly Downloads

266

Version

0.2.3

License

LGPL-3

Maintainer

Fayette Klaassen

Last Published

June 22nd, 2020

Functions in BayesianPower (0.2.3)

bayes_sampsize

Determine the required sample size for a Bayesian hypothesis test
samp_bf

Sample multiple datasets and compute the Bayes factor in each
calc_fc

Compute the complexity or fit for two hypotheses.
bayes_error

Determine the unconditional error probabilities for a set of simulated Bayes factors.
calc_bf

Compute a Bayes factor
samp_dist

Sample from prior or posterior distribution
bayes_power

Determine the 'power' for a Bayesian hypothesis test
eval_const

Evaluate a constraint matrix for a set of prior/posterior samples