library(tidyverse) data <- tibble( x = 1:100 ) data |> print(n = 100)
# A tibble: 100 × 1 x <int> 1 1 2 2 3 …
reticulate
library(tidyverse) library(cfdecomp) library(gapclosing) library(causal.decomp) d <- sMIDUS |> transmute(Y = health |> as.numeric(), # outcome T = edu …
glm(family = binomial('logit'))