Package: bayescount 1.0.0-9

bayescount: Statistical Analyses and Power Calculations for Count Data and Faecal Egg Count Reduction Tests (FECRT)

Power calculations and hypothesis testing for the difference in mean of two negative binomial distributions 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. A working installation of JAGS (<http://mcmc-jags.sourceforge.net>) is required for MCMC-based methods

Authors:Matthew Denwood [aut, cre], Bob Wheeler [cph]

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manual.pdf |manual.html✨
card.svg |card.png
bayescount/json (API)

# Install 'bayescount' in R:
install.packages('bayescount', repos = c('https://mdenwood.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mdenwood/bayescount/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.

On CRAN:

Conda:

cppjags

2.81 score 13 scripts 364 downloads 1 mentions 94 exports 4 dependencies

Last updated from:a9906dcb6d. Checks:11 WARNING, 1 OK, 1 FAIL. Indexed: yes.

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Exports:analyse_fecrassess.varianceasymptotic_cibayescount.singlebinary.searchbnb_pscheckintlimitchecklen2posdoublechecksinglelogicalchecksingleposdoublechecksingleposintchecksingleprobconjbeta_cicount_analysescount_analysiscount_modelcount_powercount_precisioncount.analysiscount.modelcount.powercount.precisiondobson_ciestimate_kestimate_k_experimentestimate_k_mlestimate_k_uselessestimate_ksestimate_ks_oldestimate_ks_old_oldfec.analysisFEC.analysisfec.modelFEC.modelfec.powerFEC.powerfec.power.limitsFEC.power.limitsfec.precisionFEC.precisionfecrtFECRTfecrt_analysesfecrt_bnbfecrt_powerfecrt_power_comparison_wrapfecrt_power_pairedfecrt_power_pooledfecrt_power_unpairedfecrt_power_wrapfecrt_sim_pairedfecrt_sim_unpairedfecrt.analysisFECRT.analysisfecrt.modelFECRT.modelfecrt.powerFECRT.powerfecrt.power.limitsFECRT.power.limitsfecrt.precisionFECRT.precisionfind_thetafindthetaget_type_ciget_type_pvlnormal_paramslnormal.paramsmethodcompnormal_paramsnormal.paramsolderprint.fecrt.resultspbeta_nbinompbnbpbnb1pbnb2pghyperpowersim_pairedpowersim_unpairedprint.fecrt_resultsreduction_analysisreduction_modelreduction_powerreduction_precisionreduction_pvalreduction_pvalsreduction.analysisreduction.modelreduction.powerreduction.precisionrun.modelshiny_launchsummarise_fecrwaavp_ci

Dependencies:codalatticeRcpprunjags

Readme and manuals

Help Manual

Help pageTopics
Analysis and power calculations for faecal egg count (FEC) and faecal egg count reduction test (FECRT) data using computationally intensive statistical methodsbayescount-package bayescount bayescountpackage
Count data analysisbayescount.single count.analysis count_analysis FEC.analysis fec.analysis
Count data modelcount.model count_model FEC.model fec.model run.model
Count data power calculationscount.power count_power FEC.power fec.power
Count data precision calculationscount.precision count_precision FEC.power.limits fec.power.limits FEC.precision fec.precision
Analyse Count data using MCMCbayescount.single count.analysis FEC.analysis fec.analysis
Analyse Count Data Using Jagscount.model FEC.model fec.model run.model
Count Data Power Analysis Calculationscount.power FEC.power fec.power
Count Data Predicted Precision Calculationscount.precision FEC.power.limits fec.power.limits FEC.precision fec.precision
FECRT Power Analysis CalculationsFECRT.power fecrt.power
FECRT Predicted Precision CalculationsFECRT.power.limits fecrt.power.limits FECRT.precision fecrt.precision
Calculate the (Log) Likelihood of Obtaining Data from a Distributionlikelihood
Calculate the Log-Normal Mean and Standard Deviation Using the Normal Mean and Standard Deviation and Vice Versalnormal.params lnormal_params normal.params normal_params
Calculate the Log-Normal Mean and Standard Deviation Using the Normal Mean and Standard Deviationlnormal.params
Calculate the Maximum Likelihood Parameters of a Continuous or Count Distributionmaximise.likelihood
Calculate the Normal Mean and Standard Deviation Using the Log-Normal Mean and Standard Deviationnormal.params
Analyse two sets of count data (such as FECRT) compared to a desired mean reductionFECRT fecrt, fecrt.analysis FECRT.analysis, reduction.analysis reduction_analysis
Generate an un-run MCMC model for FECRT dataFECRT.model fecrt.model reduction.model reduction_model
Count reduction (eg FECRT) data power calculationsFECRT.power fecrt.power reduction.power reduction_power reduction_pval
Count data reduction precision calculationsFECRT.power.limits fecrt.power.limits FECRT.precision fecrt.precision reduction.precision reduction_precision