CBSS
ConsensusBased Sample Size
Version 1.0, July 2019
Four sets of R functions for calculating sample size requirements to ensure posterior agreement from different priors using a variety of Bayesian criteria. Each package includes functions for designing an experiment to estimate, respectively:  a single binomial proportion
 the difference between two binomial proportions
 a single normal mean
 the difference between two normal means.
This package is an implementation of the methods presented in

Install instructions 
For each of the four packages, follow the instructions written for SampleSizeBinomial.
For both BinomialProportion packages, follow each step listed in the instructions, as these packages include a .C program.
For the other two packages (NormalMean), skip the first two steps.


SampleSizeRegression
Bayesian Sample Size Criteria for Linear and Logistic Regression in the Presence of Confounding and Measurement Error
A package to calculate Bayesian sample sizes to estimate linear or logistic regression model parameters, with or without the possibility of confounding variables and/or independent variable measurement error; requires that the free software packages R, Winbugs and Perl be installed.
This package is an implementation of the methods presented in

Oneclick download 
Install instructions 
 download and unzip the zipped file (above)
 doubleclick setup.exe
 follow the instructions in file doc\InstallInstructions.html
(or in its online version)


SSCOR
Odds Ratio in presence of exposure misclassification
A package to calculate sample sizes based on highest posterior density intervals in the context of estimating odds ratios when exposure may be misclassified; requires that the free software packages R, Winbugs and Perl be installed.
This package is an implementation of the methods presented in

Oneclick download 
Install instructions 
 download and unzip the zipped file (above)
 doubleclick setup.exe
 follow the instructions in file doc\InstallInstructions.html
(or in its online version)


PropMisclassSampleSize
Prevalence and diagnostic test sensitivity or specificity
A package to calculate sample sizes based on highest posterior density intervals in the context of diagnostic testing in presence of one, two or three imperfect tests; requires that the free software packages R, Winbugs and Perl be installed.
This package is an implementation of the methods presented in

Oneclick download 
Install instructions 
 download and unzip the zipped file (above)
 doubleclick setup.exe
 follow the instructions in file doc\InstallInstructions.html
(or in its online version)


SampleSizeMeans
Normal means
Version 1.0, December 2009
A set of R functions for calculating sample size requirements using three different Bayesian criteria in the context of designing an experiment to estimate a normal mean or the difference between two normal means.
This package is an implementation of the methods presented in


SampleSizeBinomial
Binomial proportion
Version 1.1, March 2018
A set of R functions for calculating sample size requirements using three different Bayesian criteria in the context of a binomial experiment.
This package is an implementation of the methods presented in


bhpd1
Binomial proportion
A FORTRAN program to calculate sample sizes based on highest posterior density intervals in the context of a binomial experiment using three different Bayesian approaches.
This package is an implementation of the methods presented in


Binomial proportion in the presence of misclassification error
An SPlus program to calculate average coverage probabilities when finding sample sizes for estimating a binomial proportion in the presence of misclassification errors.
This package is an implementation of the methods presented in


SampleSizeProportions
Binomial proportions
Version 1.0, December 2009
A set of R functions for calculating sample size requirements using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions.
This package is an implementation of the methods presented in

