I am Economics Lead at Zalando SE building and evaluating large scale decision-making systems. I am also a visiting research fellow at the Institute for Quantitative Social Science, Harvard University developing statistical software and applications for the social and physical sciences. I previously held posts at City, University of London, the Hertie School of Governance, Yonsei University, and the London School of Economics where in 2012 I completed a PhD in quantitative political science. My academic research focuses on the international political economy of public financial and monetary institutions, as well as applied social science statistics. My work has been published in peer reviewed journals including the British Journal of Political Science, Journal of Common Market Studies, Journal of Peace Research, International Studies Quarterly, Journal of European Public Policy, Review of International Political Economy, Political Science Research and Methods, and Journal of Statistical Software. I have co-authored a number of pieces on European banking union for the Bruegel Policy Contribution series. I published a book on reproducible computational research methods for Chapman and Hall.
For more details, please see my CV.
Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package. I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object. To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis . The full function is below. To