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Software And Applications

Welcome to the Software and Applications section of the SCRC documentation! This is your central hub for understanding how to access, manage, and effectively utilize the diverse range of software available on our computing resources.

Whether you need to load a specific version of a statistical package, set up an isolated environment for your Python project, install custom R packages, or simply see what tools are available, you'll find the necessary guidance here.

Key Concepts

  • Available Software: We provide access to a wide variety of research software, from compilers and programming languages (Python, R, Java) to specialized applications (MATLAB, Stata, SAS) and data analysis tools.
  • Modules System (module command): This is the primary way software environments are managed on the SCRC clusters. It allows multiple versions of software to coexist without conflicts and lets you easily load the specific version you need for your work.
  • Virtual Environments & Local Libraries: For languages like Python and R, managing project-specific dependencies is crucial. Learn how to create isolated environments (like Python virtual environments) and install packages locally without interfering with system-wide installations.
  • Running Applications: Find instructions for running specific applications, including those with graphical user interfaces (GUIs) like RStudio or XStata, often facilitated through services like FastX or accessed via other platforms like Apps@Stern.

Topics in this Category

Explore the following pages to learn more about specific software and management techniques:

Available Software Overview

A list of commonly used research software available at SCRC, including statistical packages, programming languages, and data analysis tools.

Using the Software Modules System

How to use the module command to find, load, and unload available software packages on the SCRC clusters. Essential for managing your software environment.

Python Virtual Environments

Creating, activating, and managing isolated Python environments to install and use specific package versions, ensuring project reproducibility.

Installing Local R Packages

Instructions for installing R packages into your personal user library from CRAN, Bioconductor, and GitHub, giving you access to the latest R tools.

Running GUI Applications (RStudio, XStata, Jupyter)

Guides for running graphical applications like RStudio, XStata, and Anaconda/Jupyter notebooks within interactive sessions on SLURM, typically using FastX.

Using Apps@Stern

Information on accessing certain academic software titles virtually through the NYU Stern Apps@Stern service.