A lightly opinionated R Package for making LaTeX DAGs


An R Package for visualizing and analyzing causal directed acyclic graphs

Tidy causal DAGs with ggdag 0.2.0

I’m pleased to announce that ggdag 0.2.0 is now on CRAN! ggdag links the dagitty package, which contains powerful algorithms for analyzing causal DAGs, with the unlimited flexibility of ggplot2. ggdag coverts dagitty objects to a tidy DAG data structure, which allows you to both analyze your DAG and plot it easily in ggplot2. Let’s look at an example for a causal diagram of the effect of smoking on cardiac arrest.

An introduction to precisely and ggdag: tools for modern methods in R

Modern epidemiology gives us insight into study planning and causal inference, but the success of these approaches require friendly and accessible software. I will discuss two R packages for modern methods in study design and causal inference: …

ggdag 0.1.0

I’m pleased to announce the release of ggdag 0.1.0 on CRAN! ggdag uses the powerful dagitty package to create and analyze structural causal models and plot them using ggplot2 and ggraph in a tidy, consistent, and easy manner. You can use dagitty objects directly in ggdag, but ggdag also includes wrappers to make DAGs using a more R-like syntax: # install.packages("ggdag") library(ggdag) dag <- dagify(y ~ x + z, x ~ z) %>% tidy_dagitty() dag ## # A DAG with 3 nodes and 3 edges ## # ## # A tibble: 4 x 8 ## name x y direction to xend yend circular ## <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl> <lgl> ## 1 x 6.