https://github.com/davidski/collector

⚖Open Source Toolkit for Conducting Quantitative Risk Assessment Interviews
https://github.com/davidski/collector

openfair r risk risk-assessment risk-management tidyrisk

Last synced: 10 days ago
JSON representation

⚖Open Source Toolkit for Conducting Quantitative Risk Assessment Interviews

• Host: GitHub
• URL: https://github.com/davidski/collector
• Owner: davidski
• License: other
• Created: 2018-05-16T13:36:08.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2024-03-10T01:02:08.000Z (8 months ago)
• Last Synced: 2024-05-21T16:14:45.945Z (6 months ago)
• Topics: openfair, r, risk, risk-assessment, risk-management, tidyrisk
• Language: R
• Homepage: https://collector.tidyrisk.org
• Size: 844 KB
• Stars: 31
• Watchers: 6
• Forks: 11
• Open Issues: 5
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• jimsghstars - davidski/collector - ⚖Open Source Toolkit for Conducting Quantitative Risk Assessment Interviews (R)

README

        
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# collector 

 

[![R build status](https://github.com/davidski/collector/workflows/R-CMD-check/badge.svg)](https://github.com/davidski/collector/actions)

[![Coverage Status](https://codecov.io/gh/davidski/collector/branch/master/graph/badge.svg)](https://codecov.io/github/davidski/collector?branch=master)

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/collector)](https://cran.r-project.org/package=collector)

![downloads](https://cranlogs.r-pkg.org/badges/grand-total/collector)

 

**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.