Sample size calculations for normal means
Lawrence Joseph <lawrence.joseph@mcgill.ca> and Patrick Belisle
<pbelisle@epimgh.mcgill.ca>
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. Functions for
calculation of required sample sizes for the Average Length
Criterion, the Average Coverage Criterion and the Worst Outcome
Criterion in the context of normal means are provided.
Functions for both the fully Bayesian and the mixed
Bayesian/likelihood approaches are provided.

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.  Functions for
calculation of required sample sizes for the Average Length
Criterion, the Average Coverage Criterion and the Worst Outcome
Criterion in the context of normal means are provided.
Functions for both the fully Bayesian and the mixed
Bayesian/likelihood approaches are provided.

Calculating sample size requirements when estimating the
difference between two binomial proportions
Lawrence Joseph <lawrence.joseph@mcgill.ca>, Roxane du Berger
and Patrick Belisle <pbelisle@epimgh.mcgill.ca>
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. Functions for calculation of
required sample sizes for the Average Length Criterion, the
Average Coverage Criterion and the Worst Outcome Criterion in
the context of binomial observations are provided. In all
cases, estimation of the difference between two binomial
proportions is considered. Functions for both the fully
Bayesian and the mixed Bayesian/likelihood approaches are
provided.

A framework for comparing the performance of MCMC samplers
This package consists of two components: a framework for
running sets of MCMC samplers on sets of distributions with a
variety of tuning parameters and plotting functions to
visualize the results of those simulations.  See sc-intro.pdf
for an introduction.
(c) 2010 Madeleine Thompson, except dch?d.f and jss*