Skip to content
This repository has been archived by the owner on Feb 13, 2024. It is now read-only.

sandialabs/LMSMIPNFA

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 

Repository files navigation

Data and Analysis Code for 'Comparing multiple statistical methods for inverse prediction in nuclear forensics applications'

https://data.mendeley.com/datasets/mf95d3swv5/1

SCR# 2236

Lewis, John; Zhang, Adah; Anderson-Cook, Christine (2018), “Data and Analysis Code for 'Comparing multiple statistical methods for inverse prediction in nuclear forensics applications'”, Mendeley Data, V1, doi: 10.17632/mf95d3swv5.1

Data and Analysis Code for 'Comparing multiple statistical methods for inverse prediction in nuclear forensics applications' Published: 21 March 2018| Version 1 | DOI: 10.17632/mf95d3swv5.1 Contributors: John Lewis, Adah Zhang, Christine Anderson-Cook

Description

  1. R script and data to reproduce some of the results in 'Comparing multiple statistical methods for inverse prediction in nuclear forensics applications'

Files

  1. analysis_code_all.R
  2. PuO2.csv

Steps to reproduce

  1. Ensure 'PuO2.csv' is in the current working directory
  2. Ensure libraries called via library() are installed.
  3. Run the script

The script will take several minutes to run depending on the system.

Related Identifiers*

  1. This dataset is supplement to 10.1016/j.chemolab.2017.10.010 http://doi.org/10.1016/j.chemolab.2017.10.010 *provided by DataCite https://datacite.org/

License

  1. CC BY 4.0 licence description The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean? You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

About

No description or website provided.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages