Proposed Title: The application of software engineering principles to improve quality and performance of statistical software for large data sets
Note: Can't use full proposed title as it causes problems with GitHub integration.
Questions:
• Should this be split into 2 papers?
• Should the paper just focus on one of the 2 high level areas?
• Journal of Statistical Software has no limitations on length nor figures/tables.
Review of standard software engineering precepts – why dive into these?
From Wilson et al. - 2014 - Best Practices for Scientific Computing.pdf
basic software development practices such as writing maintainable code, using version control and issue trackers, code reviews, unit testing, and task automation.
Introduction
In todays environment of Wilson et al in their 2014 paper covered key areas where scientists can benefit from software engineering best practices \citep{Wilson2014} s
The R package "pccc: Pediatric Complex Chronic Conditions" \citep{dewitt_pccc:_2017} (PCCC) is used for code examples in this article. PCCC is an R/C++ implementation of the Pediatric Complex Chronic Conditions software released as part of a series of research papers by Feudtner et al <reference>. The goal of PCCC is to take in a data set containing International Statistical Classification of Diseases and Related Health Problems (ICD) Ninth revision or Tenth revision diagnosis or procedure codes and output which if any complex chronic conditions a patient has.
Knowledge Gap: