Andrew Heiss is a researcher and educator focused on data visualization, causal inference, and applied statistics using R and Bayesian methods. He writes extensively about reproducible research, GIS, and analytical workflows, and teaches data science and social science methods.
55 articles from this blog
A technical guide to performing multilevel multinomial conjoint analysis using R, Bayesian modeling, and statistical packages.
A tutorial on fixing overplotted points on maps by creating filled density gradients using R, ggplot2, and the sf package for geographic data.
A technical guide on using R, brms, and marginaleffects packages to perform conjoint analysis for statistical research.
A technical tutorial on using R to analyze and visualize personal Google Location History data, using a 5,000-mile road trip as a case study.
A technical tutorial on using R and OpenStreetMap to create custom, data-driven maps for planning and visualizing a complex road trip route.
A tutorial on performing Bayesian proportion tests for categorical survey data using R and the {brms} package.
A tutorial on creating maps of Tolkien's Middle Earth using R, the {sf} package, and {ggplot2} for GIS data visualization.
A statistical analysis using R and Bayesian modeling to convert Aragorn's Dúnedan age from Lord of the Rings into equivalent human years, based on Tolkien's writings.
A guide to creating inline bibliography entries in Markdown documents using pandoc and custom Citation Style Language (CSL) files.
A guide for academics on migrating from BibDesk to Zotero for managing citations and PDFs within a pandoc-based Markdown writing workflow.
A guide on using the scales package in R's ggplot2 to format axes with natural log and base 10 log scales for better data visualization.
A guide to calculating marginal and conditional effects in generalized linear mixed models (GLMMs) using the R {marginaleffects} package.
Explains the differences between Bayesian posterior predictions, linear predictions, and expected predictions using R, brms, and Stan.
A tutorial on solving overlapping x-axis label issues in ggplot2 using techniques like plot resizing, axis swapping, and label rotation.
A guide explaining marginal effects in regression analysis, including definitions and differences between types like average marginal effects, using R packages.
A technical guide to implementing Bayesian hurdle lognormal and Gaussian regression models in R for analyzing data with many zero values.
A tutorial on implementing a nearly fully Bayesian causal inference model using inverse probability weights with R, brms, and Stan.
Explores the challenges and a proposed method for combining Bayesian inference with propensity scores and inverse probability weights for causal analysis.
A technical guide on using Bayesian multilevel models with R and brms to analyze country-year panel (time-series cross-sectional) data.
A technical guide on calculating posterior predictions and average marginal effects for multilevel Bayesian models using R and brms.
