This workshop will cover techniques for conducting large-scale inference, using Python and its libraries.  We will cover the principals of how large scale inference is different from classical inference, and why multiple comparisons should usually be accounted for in an analysis.  We will then discuss the Bonferroni method, Benjamini and Hochberg's False Discovery Rate (FDR), […]

This brief workshop will demonstrate the capabilities of two recent R packages, gganimate and patchwork. One package allows the data explorer to provide some lively enhancement to an otherwise static plot, without doing much more than standard plots with ggplot2. Likewise, the other package can seamlessly combine multiple ggplots of varying kinds into one cohesive […]

Topics: By the end of the workshop, participants will be able to: Work with Stata, including using Do-files and using the help system. Get data into Stata and manage your data files Establish familiarity with your data Clean the data to prepare it for analysis Check for basic errors in the data Generate new variables […]

This workshop will cover introductory concepts, tools, and techniques to model spatially referenced data observed over a regular or irregular grid. We will cover models such as spatial autoregressive that emphasizes the idea of spatial correlation via neighborhood. The workshop will focus on conceptual aspects, diagnostics tests, implementation in R, and interpretation of results.

In the R world, lme4 is a great package for mixed model estimation, and the most widely used for such models.  For standard settings, few tools will do the trick as easily or as quickly, and because of that, its approach has been emulated in other packages and statistical programs.  However, that ease and efficiency […]