This repository contains the code for the paper "Posterior Predictive Null Checks" (Moran et al. 2021).

The R code has been tested on R version 4.0.2 with the following packages:

MASS
tidyverse
ggtext
ggpubr
philentropy
LaplacesDemon
ggplot2
dplyr
reshape2
grid
gridExtra
gtable
xtable
RColorBrewer
kSamples
reticulate

The Python code has been tested on Python 3.7.6 with the packages listed in factor-analysis/environment.yml.

• gaussian-mixture

This folder contains code to replicate the Gaussian mixture model example in Section 1. The main file is gaussian-mixture/analysis.R.

• regression

This folder contains code to replicate the regression example in Section 2.5.

• multinomial-mixture

This folder contains code to replicate the multinomial mixture model experiment in Section 3.1. The main file is multinomial-mixture/analysis.R. The data can be found in multinomial-mixture/dat. The data was sourced from Table 1 of Stern et al. 1995.

• factor-analysis

This folder contains code to replicate the factor analysis examples in Sections 3.2 and 3.3. The code for Section 3.2 is linear_example.py with additional plotting of results in plot_linear_example.R

The code for Section 3.2 is nonlinear_example.py with additional plotting of results in plot_nonlinear_example.R

• Moran, G.E., Cunningham, J.P., and Blei, D. M. (2021) Posterior Predictive Null Checks. arXiv.
• Stern, H. S., Arcus, D., Kagan, J., Rubin, D. B., and Snidman, N. (1995). Using mixture models in temperament research. International Journal of Behavioral Development, 18(3): 407–423. 13, 14.