Our compute grid GUI login environment can be tricky to navigate, especially if you're unfamiliar with Linux. We've compiled some tips to help guide you through the GNOME interface, which underwent a major facelift when we upgraded the compute grid operating system. To learn how to change your screen size, manage your preferences, and more, check out this document.
RCS would like to congratulate class of 2019 graduates and those who are departing for new opportunities! In anticipation of your departure from HBS, we'd like to bring to your attention the following RCS-related departure checklist that may require action on your part before losing access to HBS resources on June 30th....
This month's tip is by our Senior Statistician, Xiang Ao:
Stata’s margins command has been a powerful tool for many economists. It can calculate predicted means as well as predicted marginal effects. Sometimes we’d like to compare those marginal effects. People use margins and marginsplot to generate marginal effects; then draw conclusions on whether there is a difference between marginal effects, based on whether the confidence intervals overlap or not. However, that can actually be wrong. In this post, I’d like to introduce a way to...
Over the past few weeks, you may have noticed that the RCS intranet has received a facelift! In addition to restructuring content, the updated page includes several new features that we hope will streamline users’ experiences. In particular, we now offer forms that allow users to:
Based on both research computing trends and recommendations from the HBS research computing environment assessment, RCS in partnership with HBS launched the Grid 2.5 last December. This new compute grid provides a number of updates and enhancements that improve compute capacity with fewer restrictions, increased work safeguards, and both GUI and command-line programs to increase research capabilities for programming, statistics, and research data management.
How could you benefit from using the compute grid?
We are often asked about how to calculate marginal effects in R, especially from Stata users who use Stata's margins and marginsplot commands after regression models. These two packages in R have similar functions to Stata's margins and marginsplot commands, which are used to calculate marginal effects after a regression model and graph them:
Natural Language Processing (NLP) assists computers with processing and understanding natural human language, such as speeches, tweets, and newspaper articles. NLP can range from counting the number of times a word appears in text to analyses that assess attitudes (e.g., positive, negative). NLP can be conducted on a variety of platforms, including the robust NLTK package in Python and several libraries in R.
For an introduction and hands-on experience using the NLTK in Python, DataCamp provides a free module as part of their NLP fundamentals course:...