Package: ordinalRR 1.1
Ken Ryan
ordinalRR: Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements
Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.
Authors:
ordinalRR_1.1.tar.gz
ordinalRR_1.1.tar.gz(r-4.7-any)ordinalRR_1.1.tar.gz(r-4.6-any)
ordinalRR_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ordinalRR/json (API)
| # Install 'ordinalRR' in R: |
| install.packages('ordinalRR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- 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.
- c++– GNU Standard C++ Library v3
- followup - Followup data from experiment on soldered joints.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:b09380b4f9. Checks:4 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 92 | ||
| source / vignettes | OK | 149 | ||
| linux-release-x86_64 | OK | 101 | ||
| wasm-release | OK | 88 |
Exports:compute.qmake.raterordinalRRordinalRR.controlordinalRR.simpreprocess
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute the probabilities for a single rater at a fixed part quality. | compute.q |
| Plot densities of the latent part distributions. | density.ordinalRR |
| Followup data from experiment on soldered joints. | followup |
| Histogram for the latent part distributions from a Bayesian ordinal R&R analysis. | hist.ordinalRR |
| Format the parameters for a single rater. | make.rater |
| Fit a Bayesian ordinal R&R model using JAGS. | ordinalRR |
| Set control parameters for a Bayesian ordinal R&R model. | ordinalRR.control |
| Simulate an ordinal R&R data set. | ordinalRR.sim |
| Format an ordinal R&R data frame into object required by function ordinalRR. | preprocess |
| Print function for an object of class ordinalRR. | print.ordinalRR |
| Summarize an object of class ordinalRR. | summary.ordinalRR |
