Add AIR format file and initial debug script for stochtree's propagation of random seed

air.toml

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+[format]
+line-width = 80
+indent-width = 2
+indent-style = "space"
+line-ending = "auto"
+persistent-line-breaks = true
+exclude = []
+default-exclude = true
+skip = []

tools/debug/bart_random_seed.R

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+# Load libraries
+library(stochtree)
+
+# Generate data
+random_seed <- 1234
+set.seed(random_seed)
+n <- 500
+p <- 50
+X <- matrix(runif(n * p), ncol = p)
+# fmt: skip
+f_XW <- (
+  ((0 <= X[, 1]) & (0.25 > X[, 1])) * (-7.5) +
+  ((0.25 <= X[, 1]) & (0.5 > X[, 1])) * (-2.5) +
+  ((0.5 <= X[, 1]) & (0.75 > X[, 1])) * (2.5) +
+  ((0.75 <= X[, 1]) & (1 > X[, 1])) * (7.5)
+)
+noise_sd <- 1
+y <- f_XW + rnorm(n, 0, noise_sd)
+
+# Split into train and test sets
+test_set_pct <- 0.2
+n_test <- round(test_set_pct * n)
+n_train <- n - n_test
+test_inds <- sort(sample(1:n, n_test, replace = FALSE))
+train_inds <- (1:n)[!((1:n) %in% test_inds)]
+X_test <- X[test_inds, ]
+X_train <- X[train_inds, ]
+y_test <- y[test_inds]
+y_train <- y[train_inds]
+
+# Run BART model
+general_params <- list(num_threads = 1, random_seed = random_seed)
+bart_model <- bart(
+  X_train = X_train,
+  y_train = y_train,
+  X_test = X_test,
+  num_gfr = 100,
+  num_mcmc = 100,
+  general_params = general_params
+)
+
+# # Save results
+# write.csv(
+#   bart_model$y_hat_test,
+#   file = "tools/debug/seed_benchmark_y_hat.csv",
+#   row.names = FALSE
+# )
+
+# Read results and compare to our estimates
+y_hat_test_benchmark <- as.matrix(read.csv(
+  "tools/debug/seed_benchmark_y_hat.csv"
+))
+
+# Compare results
+sum(abs(y_hat_test_benchmark - bart_model$y_hat_test) > 1e-6)