I am a Ph.D. candidate in Political Science and Social Data Analytics at Penn State and an NSF Big Data Social Science IGERT fellow. I am advised by Bruce Desmarais. My research and teaching interests primarily center on statistical methods for analyzing text and network data, with applications to the study of American political institutions and a particular focus on Congress and the bureaucracy. My dissertation project, The Role of Bureaucratic Discretion in Congressional Politics, examines the provision of bureaucratic discretion as a policy tool in Congress. I begin by deriving theoretical expectations regarding how partisanship shapes the use of statutory constraint and delegation of authority to the bureaucracy. I then test these theories by developing new methods for analyzing legislative texts. From 2013-2015, I was a statistical methods consultant for the Institute for Social Science Research at UMass Amherst, while completing Master's degrees in Applied Econometrics and Political Science.
Contact: mdenny 'at' psu.edu -or- matthewjdenny 'at' gmail.com -or- @MatthewJDenny
Github Page --- Department Page --- Google Scholar --- Dataverse



Click here for more information about my research and links to papers, select presentations, and conference posters.


Teaching and Workshops

Links to most of my workshop materials and tutorials.



Current as of September, 2017.



Links to some templates and a bunch of other useful resources that I use but have trouble finding.


Short Tutorials

Short tutorials related to programming.


Data Science Tools

This tutorial presents an introduction to Data Science and gently introduces RStudio, Github, and remote access tools for working on Data Science project with lots of pictures.


Using C++ with R

This tutorial presents an introduction to using C++ with R through the use of the Rcpp, RcppArmadillo, and BH packages. It includes a lot of examples and links to other tutorials.


R Tutorial

This tutorial introduces a number of concepts related to data management and basic programming in R.


ICPSR Data Science Tutorial

This three night tutorial introduces a number of core concepts and tools in Data Science for Social Science applications. It covers a number of topics including basic programming, software tools, High Performance Computing, Big Data, web scraping, and code distributability.


Text Processing in R

This short tutorial covers some basic functions for manipulating string variables in R, a set of functions and example code for cleaning raw text data, and example code for turning the resulting cleaned text data into a document-term matrix.


Preparing Network Data In R

This tutorial covers preparing network data in edgelist (both single and multiple receiver) and sociomatrix formats (weighted and unweighted) for social network analysis using the network package in R. Basic network plotting is also covered.


R Package Development Pictorial

This pictorial brings together a bunch of resources I used to learn how to create an R package and illustrates the process of creating your first R package in RStudio with pictures. It also covers including C++ and Python code with your R package.


Getting Started With GERGM

This vignette provides a basic example using the GERGM R package to assess the structure of the international financial network, and is intended to introduce the package to new users.


Getting Started With preText

This vignette provides a basic example using the preText R package to assess the consequences of text preprocessing decisions.


Getting Started With phrasemachine

This vignette provides a basic example using the phrasemachine R package to extract noun phrases from a corpus.


Comparative Networks Dataset

A carefully curated and documented collection of over 300 network datasets in multiple formats for easy use with network analysis software in R.

Copyright Matthew J. Denny 2013-2017