# Bayesian

# New statistics for the design researcher

## by Martin Schmettow

A statistics book for designers, human factors specialists, UX researchers, applied psychologists and everyone else who works hard to make this world a better place. […] This book makes the following assumptions: Chapter @ref(design_research) introduces a framework for quantitative design research. It carves out the basic elements of empirical design research, such as users, designs and performance and links them to typical research problems. Then the idea of design as decision making under uncertainty is developed at the example of two case studies. Chapter @ref(bayesian_statistics) … Read more →

# Graphical & Latent Variable Modeling

## by Michael Clark m-clark.github.io

This document focuses on structural equation modeling. It is conceptually based, and tries to generalize beyond the standard SEM treatment. It includes special emphasis on the lavaan package. Topics include: graphical models, including path analysis, bayesian networks, and network analysis, mediation, moderation, latent variable models, including principal components analysis and ‘factor analysis’, measurement models, structural equation models, mixture models, growth curves, item response theory, Bayesian nonparametric techniques, latent dirichlet allocation, and more. Read more →

# recoding Introduction to Mediation, Moderation, and Conditional Process Analysis

## by A Solomon Kurz

This project is an effort to connect his Hayes’s conditional process analysis work with the Bayesian paradigm. Herein I refit his models with my favorite R package for Bayesian regression, Bürkner’s brms. I use syntax based on sensibilities from the tidyverse and plot with Wickham’s ggplot2. […] Andrew Hayes’s Introduction to Mediation, Moderation, and Conditional Process Analysis text, the second edition of which just came out, has become a staple in social science graduate education. Both editions of his text have been from a frequentist OLS perspective. This project is an effort to … Read more →

# Bayesian Basics

## by Michael Clark

This document provides an introduction to Bayesian data analysis. It is conceptual in nature, but uses the probabilistic programming language Stan for demonstration (and its implementation in R via rstan). From elementary examples, guidance is provided for data preparation, efficient modeling, diagnostics, and more. […] … Read more →

# Efficient R programming

## by Colin Gillespie, Robin Lovelace

Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. […] This is the online version of the O’Reilly book: Efficient R programming. Pull requests and general comments are welcome. Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. His research interests are high performance statistical computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005 at a variety of levels, ranging … Read more →