Creating Libraries and Packages - Revision history

Ajshen at 20:45, 28 June 2026

2026-06-28T20:45:34Z

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	[[Category:Beginner'~~s_Guide_Software_Engineering~~\|02]]		[[Category:Beginner's_Guide_Software\|01]]

	{{~~PackageDevelopmentSidebar~~}}		{{SoftwareSidebar}}

	'''Package development''' is the process of organizing your research code, functions, and data into a standardized, structured format that can be easily shared, installed, and reused. While a collection of isolated scripts might suffice for exploring a local dataset, bundling your code into a formal package is essential when you want to distribute your particular algorithm or datasets for use by the general academic community		'''Package development''' is the process of organizing your research code, functions, and data into a standardized, structured format that can be easily shared, installed, and reused. While a collection of isolated scripts might suffice for exploring a local dataset, bundling your code into a formal package is essential when you want to distribute your particular algorithm or datasets for use by the general academic community

Ajshen: /* 1. Language-Specific Guides */

2026-06-28T20:42:43Z

1. Language-Specific Guides

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	<div style="background-color: #f8f9fa; border-left: 4px solid #005A9C; padding: 15px; margin-bottom: 10px;">		<div style="background-color: #f8f9fa; border-left: 4px solid #005A9C; padding: 15px; margin-bottom: 10px;">
	'''[[Package Development in R]]'''<br>		[[Developing R packages\|'''Package Development in R''']]<br>
	R has a deeply ingrained culture of package development. This guide covers the essential anatomy of an R package, using tools like <code>devtools</code> and <code>roxygen2</code> to automate documentation, and best practices for organizing your statistical modeling code.		R has a deeply ingrained culture of package development. This guide covers the essential anatomy of an R package, using tools like <code>devtools</code> and <code>roxygen2</code> to automate documentation, and best practices for organizing your statistical modeling code.
	</div>		</div>

	<div style="background-color: #f8f9fa; border-left: 4px solid #ffc107; padding: 15px; margin-bottom: 10px;">		<div style="background-color: #f8f9fa; border-left: 4px solid #ffc107; padding: 15px; margin-bottom: 10px;">
	'''[[Package Development in Python]]'''<br>		'''[[Developing python packages\|Package Development in Python]]'''<br>
	Modern Python packaging emphasizes clean project structures and declarative configuration. Learn how to structure your directories, configure a <code>pyproject.toml</code> file, and manage dependencies to make your machine learning models or data pipelines easily <code>pip</code>-installable.		Modern Python packaging emphasizes clean project structures and declarative configuration. Learn how to structure your directories, configure a <code>pyproject.toml</code> file, and manage dependencies to make your machine learning models or data pipelines easily <code>pip</code>-installable.
	</div>		</div>

Ajshen: Created page with "02 {{PackageDevelopmentSidebar}} '''Package development''' is the process of organizing your research code, functions, and..."

2026-06-28T20:40:37Z

Created page with "02 {{PackageDevelopmentSidebar}} '''Package development''' is the process of organizing your research code, functions, and..."

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[[Category:Beginner's_Guide_Software_Engineering|02]]

{{PackageDevelopmentSidebar}}

'''Package development''' is the process of organizing your research code, functions, and data into a standardized, structured format that can be easily shared, installed, and reused. While a collection of isolated scripts might suffice for exploring a local dataset, bundling your code into a formal package is essential when you want to distribute your particular algorithm or datasets for use by the general academic community

This page connects you to our dedicated guides for the department's most commonly used languages and outlines the broader principles of distributing software.

<div style="background-color: #fff3cd; border-left: 5px solid #ffc107; padding: 15px; margin-bottom: 20px; margin-top: 15px; overflow: hidden;">
'''Important: Version Control and Testing'''<br>
Before diving into package development, ensure your project is tracked using Git. Creating a package also implies a commitment to reliability. You should adopt the habit of writing unit tests alongside your code to guarantee your functions perform exactly as expected—a necessity when sharing your work with the broader academic community.
</div>

== 1. Language-Specific Guides ==
The mechanics of building a package differ significantly depending on your programming language of choice. We maintain detailed, step-by-step tutorials for both R and Python.

<div style="background-color: #f8f9fa; border-left: 4px solid #005A9C; padding: 15px; margin-bottom: 10px;">
'''[[Package Development in R]]'''<br>
R has a deeply ingrained culture of package development. This guide covers the essential anatomy of an R package, using tools like <code>devtools</code> and <code>roxygen2</code> to automate documentation, and best practices for organizing your statistical modeling code.
</div>

<div style="background-color: #f8f9fa; border-left: 4px solid #ffc107; padding: 15px; margin-bottom: 10px;">
'''[[Package Development in Python]]'''<br>
Modern Python packaging emphasizes clean project structures and declarative configuration. Learn how to structure your directories, configure a <code>pyproject.toml</code> file, and manage dependencies to make your machine learning models or data pipelines easily <code>pip</code>-installable.
</div>

== 2. Beyond the Code ==
Writing the functions is only half the battle. A successful, widely adopted package requires clear instructions and automated maintenance.

<div style="background-color: #f8f9fa; border-left: 4px solid #28a745; padding: 15px; margin-bottom: 10px;">
'''[[Writing Good Documentation]] (TBW)'''<br>
Guidelines for writing clear READMEs, vignettes, and function-level docstrings. Good documentation lowers the barrier to entry and helps other researchers understand how to apply your computational tools to their own data.
</div>