Quantified Risk Assessment Data Collection

An open source process for collecting quantified data inputs from subject matter experts. Intended for feeding into an OpenFAIR analysis < https://www2.opengroup.org/ogsys/catalog/C13K> using a tool such as 'evaluator' < https://evaluator.tidyrisk.org>.

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

Installation

Collector is now available on CRAN.

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.

# install.packages("remotes")
remotes::install_github("davidski/collector")

Basic Flow

See the package website for reference. The basic flow for preparing for interviews with your SMEs, processing the results, and generating parameters for simulation via evaluator is:

1. Build questions and define SME expertise

2. Read in the question set. See read_questions() for more information.

   library(collector)
   
   questions <- read_questions()
   

3. Generate materials for interviewing a SME.

   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.

   responses <- read_responses()
   

5. Fit the SME answers to distributions.

   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.

   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.

   scenarios <- prepare_data(scenario_parameters, capability_parameters, 
                             threat_parameters, questions)
   

Contributing

This project is governed by a Code of Conduct. By participating in this project you agree to abide by these terms.

License

The MIT License applies.