Package: ElicitationWizard 0.1.0

Jeffery A. Kportufe

ElicitationWizard: LLM-Based Bayesian Prior Elicitation Wizard

'Shiny' application for eliciting Bayesian prior distributions using large language models (LLMs). Supports multiple LLM experts, linear opinion pooling, and the Delphi method for iterative consensus. For more details see J. R. Falconer et al. (2022) <doi:10.1287/deca.2022.0451> and D. Selby et al. (2025) <doi:10.1002/sta4.70054>.

Authors:Jeffery A. Kportufe [aut, cre]

ElicitationWizard_0.1.0.tar.gz
ElicitationWizard_0.1.0.zip(r-4.7-any)ElicitationWizard_0.1.0.zip(r-4.6-any)
ElicitationWizard_0.1.0.tar.gz(r-4.7-any)ElicitationWizard_0.1.0.tar.gz(r-4.6-any)
ElicitationWizard_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ElicitationWizard/json (API)

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

Bug tracker:https://github.com/jefferyayiti/automated_elicitation/issues

On CRAN:

Conda:

2.30 score 357 downloads 1 exports 49 dependencies

Last updated from:cd70268830. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK140
source / vignettesOK239
linux-releaseOK141
windows-develOK73
windows-releaseOK72
wasm-releaseOK118

Exports:run_elicitation

Dependencies:askpassbase64encbslibcachemclicodetoolscommonmarkcorocurldigestellmerfastmapfontawesomefsfutureglobalsgluehtmltoolshttpuvhttr2jquerylibjsonlitelaterlifecyclelistenvmagrittrmemoisemimeopensslotelparallellypillarpkgconfigpromisesR6rappdirsRcpprlangS7sassshinyshinyjssourcetoolssystibbleutf8vctrswithrxtable

Getting Started with ElicitationWizard
Overview | Installation | Dependencies | API Requirements | Launching the App | Interface Overview | Sidebar | Main Panel Tabs | Available Models | Elicitation Models | Stan Code Generation Models | Supported Distribution Families | Next Steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-05-29
Started: 2026-05-29

Tutorial: Eliciting Bayesian Priors with ElicitationWizard
Setup | Step 1 — Define the Clinical Context | Step 2 — Specify the Model and Parameters | Step 3 — Configure Experts | Step 4 — Elicit Priors | Step 5 — Review Expert Responses | Generating Stan Code from an Expert Response | Step 6 — Inspect the Prior Summary | Step 7 — Pool Expert Opinions | Step 8 (Optional) — Delphi Iterative Elicitation | How it works | Running a Delphi session | Exporting Results | Stan Model | Downstream Validation | Troubleshooting
Rendered fromtutorial.Rmdusingknitr::rmarkdown

Last update: 2026-05-29
Started: 2026-05-29