This package is considered a duplicate. The official version of this package is found at:https://mdenwood.r-universe.dev/bayescount

Package: bayescount 0.9.99-9

Matthew Denwood

bayescount: Power Calculations and Bayesian Analysis of Count Distributions and FECRT Data using MCMC

A set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. Functions to perform power analyses for faecal egg counts (including FECRT) are also provided.

Authors:Matthew Denwood [aut, cre]

bayescount_0.9.99-9.tar.gz
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bayescount_0.9.99-9.tgz(r-4.4-emscripten)bayescount_0.9.99-9.tgz(r-4.3-emscripten)
bayescount.pdf |bayescount.html✨
bayescount/json (API)

# Install 'bayescount' in R:

install.packages('bayescount', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jags cpp

1.00 score 9 scripts 391 downloads 1 mentions 33 exports 4 dependencies

Last updated 1 years agofrom:0a301ff999. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 03 2024
R-4.5-linux-x86_64	OK	Dec 03 2024

Exports:bayescount bayescount.single count.analysis count.model count.power count.precision fec.analysis FEC.analysis fec.model FEC.model fec.power FEC.power fec.power.limits FEC.power.limits fec.precision FEC.precision fecrt FECRT fecrt.analysis FECRT.analysis fecrt.model FECRT.model fecrt.power FECRT.power fecrt.power.limits FECRT.power.limits fecrt.precision FECRT.precision likelihood lnormal.params maximise.likelihood normal.params run.model

Dependencies:coda lattice rjags runjags

Help page	Topics
Analyse Count data using MCMC	bayescount
Analyse Count Data Using Jags	count.model FEC.model fec.model run.model
Count Data Power Analysis Calculations	count.power FEC.power fec.power
Count Data Predicted Precision Calculations	count.precision FEC.power.limits fec.power.limits FEC.precision fec.precision
Analyse Count data using MCMC	bayescount.single count.analysis FEC.analysis fec.analysis
Analyse FECRT Data Using MCMC to Give a Probability Distribution of Values for the Mean Egg Count Reduction	FECRT fecrt FECRT.analysis fecrt.analysis
Create an MCMC model to analyse FECRT Data	FECRT.model fecrt.model
FECRT Power Analysis Calculations	FECRT.power fecrt.power
FECRT Predicted Precision Calculations	FECRT.power.limits fecrt.power.limits FECRT.precision fecrt.precision
Calculate the (Log) Likelihood of Obtaining Data from a Distribution	likelihood
Calculate the Log-Normal Mean and Standard Deviation Using the Normal Mean and Standard Deviation	lnormal.params
Calculate the Maximum Likelihood Parameters of a Continuous or Count Distribution	maximise.likelihood
Calculate the Normal Mean and Standard Deviation Using the Log-Normal Mean and Standard Deviation	normal.params

Package: bayescount 0.9.99-9

bayescount: Power Calculations and Bayesian Analysis of Count Distributions and FECRT Data using MCMC

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)