Prior Distributions Using a Bayesian Hierarchical Framework
c(
person("Falk", "Hesse", email = "falk.hesse@ufz.de", role = "cre"),
person("Nura", "Kawa", email = "nkawa@berkeley.edu", role = "aut"),
person("Karina", "Cucchi", email = "karina.cucchi@gmail.com", role = "aut"))
The aim of this package is to provide practitioners of statistics in
geology, hydrology, etc. a tool to derive prior distributions for Bayesian
inference. Prior distributions summarize knowledge from studies at similar sites.
The main features of the package are to (i) generate prior distributions based
on external data only; (ii) to account for possible autocorrelation
in the data, and (iii) to account for available soft data, say, in
the form of expert information on bounds and moments.
plyr, stats, dplyr, ggplot2, reshape2, gtable, grid, coda
Falk Hesse [cre],
Nura Kawa [aut],
Karina Cucchi [aut]

plyr, stats, ggplot2, reshape2, gtable, grid, coda

Exact Samples Sizes and Inference for Clinical Trials with
Binary Endpoint
Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see “A comprehensive open-source library for exact required sample size in binary clinical trials” by Lloyd, C.J. & Ripamonti E. (2021) Contemporary Clinical Trials 107, <doi:10.1016/j.cct.2021.106491>.

Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see “Exact samples sizes for clinical trials subject to size and power constraints” by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.

Exact Conditional Tests and Confidence Intervals for 2x2 tables
Calculates Fisher's exact and Blaker's exact tests with
appropriate matching confidence intervals.

Calculates Fisher's exact test, Blaker's exact test, or
the exact McNemar's test with appropriate matching confidence
intervals.

Calculates Fisher's exact test, Blaker's exact test, or the exact McNemar's test with appropriate matching confidence intervals.