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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# collector <a href="https://collector.tidyrisk.org"><img alt="collector Logo" title="collector" align="right" src="man/figures/logo.png" height="139"></a>
<!-- badges: start -->
[](https://github.com/davidski/collector/actions)
[](https://codecov.io/github/davidski/collector?branch=master)
[](https://cran.r-project.org/package=collector)
<!-- badges: end -->
**collector** is an R package for conducting interviews with subject matter
experts (SMEs) on the risk scenarios facing an organization. It offers
functions for the following stages of input collection:
- generate scenario and capability questions
- building interview artifacts, including progress card, slide decks, and handouts
- calibration testing, similar to that promoted by Doug Hubbard and the FAIR Institute
- distribution fitting
- opinion pooling of multiple SMEs into a single representative distribution
- generating quantitative risk scenarios for simulation and reporting by [Evaluator](https://evaluator.tidyrisk.org)
## Installation
Collector is now available on CRAN.
```{r install_cran, eval=FALSE}
install.packages("collector")
```
If you wish to run the development (and potentially bleeding edge) version,
you can install directly from GitHub via the following `remotes` command.
```{r github_install, eval=FALSE}
# install.packages("remotes")
remotes::install_github("davidski/collector")
```
## Basic Flow
See the [package website](https://collector.tidyrisk.org) for
reference. The basic flow for preparing for interviews with your SMEs,
processing the results, and generating parameters for simulation via
[evaluator](https://evaluator.tidyrisk.org) is:
1. Build questions and define SME expertise
2. Read in the question set. See `read_questions()` for more information.
```{r, eval=FALSE}
library(collector)
questions <- read_questions()
```
3. Generate materials for interviewing a SME.
```{r, eval=FALSE}
output_dir <- tempdir()
make_handouts("Leader Name", questions, output_dir)
make_scorecard("Leader Name", questions, output_dir)
make_slides("Leader Name", questions, output_dir)
```
4. Read in the responses from your SMEs. See `read_responses()` documentation
for more information.
```{r, eval=FALSE}
responses <- read_responses()
```
5. Fit the SME answers to distributions.
```{r, eval=FALSE}
scenario_answers_fitted <- fit_scenarios(responses)
capability_answers_fitted <- fit_capabilities(responses)
```
6. Combine distributions into final parameters, applying weighting based on
each SMEs level of calibration.
```{r eval=FALSE}
sme_weightings <- generate_weights(questions, responses)
scenario_parameters <- left_join(scenario_answers_fitted, sme_weightings, by = "sme") %>%
combine_scenario_parameters()
capability_parameters <- left_join(capability_answers_fitted, sme_weightings, by = "sme") %>%
combine_capability_parameters()
```
7. Build quantitative scenarios for [evaluator](https://evaluator.tidyrisk.org).
```{r eval=FALSE}
scenarios <- prepare_data(scenario_parameters, capability_parameters,
threat_parameters, questions)
```
## Contributing
This project is governed by a [Code of Conduct](https://collector.tidyrisk.org/CODE_OF_CONDUCT.html). By
participating in this project you agree to abide by these terms.
## License
The [MIT License](LICENSE) applies.