Other Helpful Links

• Software at Stanford
• Licensed software available on the Stanford shared servers
• Software offered directly through Software Licensing group
• Stanford's software licensing web store

Stanford University Libraries

• Funding opportunities in interdisciplinary learning
• Stanford graduate summer institute
• Graduate student funding
• VPGE funding opportunities
• VPGE fellowships

• R Resources at Stanford Libraries
• R project  An R journal and many books are listed under Documentation. From the book list, use your browser’s search box to search for “social science” for the most relevant books. You can also download manuals by clicking on Documentation – Manuals on the website.
• LinkedIn Learning   Formerly Lynda.com. Online training library that has over 13,000 courses covering business, technology, and creative topics. Login with your SUNet ID through the Stanford ITS page. Tutorials on R and Python programming languages for data cleaning, modeling, and visualization are available. 
• Social Science Data & Software  The Social Science Resource Center in the Velma Denning Room of Green Library hosts statistical software for evaluation and consultation, including R. The social science data services experts in this office are your best resource. Consult with them to get started or when you encounter problems.  They can help you locate information on particular R - related topics. Several computer clusters in the library also include R. SSDS has 3 downloadable worksheets on using R, including a brochure to help you get started (with terminology and tips), and a resource list that includes helpful books available in Stanford libraries.

• Statistics: The Department of Statistics offers a free drop-in consulting service to members of the University community.
• SMACC: Statistical, Mathematical, and Computational Consulting Consulting services from ICME, the Department of Statistics, Research Computing and IRiSS specializing in:
  • Data Science, Machine Learning, Model fitting, Time series, Classification and prediction, Shared computing resources, Network Analysis, Matrix Problems, PDEs and Physical simulation, Discrete Mathematics, Optimization, Distributed Matrix Computations, Distributed Machine Learning and Optimization, High Performance Computing, Linear Algebra, Scientific Computing, FarmShare, Sherlock, GBSC, Proclus