diff --git a/tools/debug/bcf_quick.R b/tools/debug/bcf_quick.R
deleted file mode 100644
index 60a40bda..00000000
--- a/tools/debug/bcf_quick.R
+++ /dev/null
@@ -1,53 +0,0 @@
-library(stochtree)
-g <- function(x) {ifelse(x[,5]==1,2,ifelse(x[,5]==2,-1,-4))}
-mu1 <- function(x) {1+g(x)+x[,1]*x[,3]}
-mu2 <- function(x) {1+g(x)+6*abs(x[,3]-1)}
-tau1 <- function(x) {rep(3,nrow(x))}
-tau2 <- function(x) {1+2*x[,2]*x[,4]}
-n <- 500
-snr <- 3
-x1 <- rnorm(n)
-x2 <- rnorm(n)
-x3 <- rnorm(n)
-x4 <- as.numeric(rbinom(n,1,0.5))
-x5 <- as.numeric(sample(1:3,n,replace=TRUE))
-X <- cbind(x1,x2,x3,x4,x5)
-p <- ncol(X)
-mu_x <- mu1(X)
-tau_x <- tau2(X)
-pi_x <- 0.8*pnorm((3*mu_x/sd(mu_x)) - 0.5*X[,1]) + 0.05 + runif(n)/10
-Z <- rbinom(n,1,pi_x)
-E_XZ <- mu_x + Z*tau_x
-y <- E_XZ + rnorm(n, 0, 1)*(sd(E_XZ)/snr)
-X <- as.data.frame(X)
-X$x4 <- factor(X$x4, ordered = TRUE)
-X$x5 <- factor(X$x5, ordered = TRUE)
-
-# Split data into test and train 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,]
-pi_test <- pi_x[test_inds]
-pi_train <- pi_x[train_inds]
-Z_test <- Z[test_inds]
-Z_train <- Z[train_inds]
-y_test <- y[test_inds]
-y_train <- y[train_inds]
-mu_test <- mu_x[test_inds]
-mu_train <- mu_x[train_inds]
-tau_test <- tau_x[test_inds]
-tau_train <- tau_x[train_inds]
-num_gfr <- 10
-num_burnin <- 0
-num_mcmc <- 1000
-num_samples <- num_gfr + num_burnin + num_mcmc
-bcf_model_warmstart <- bcf(
-    X_train = X_train, Z_train = Z_train, y_train = y_train, pi_train = pi_train, 
-    X_test = X_test, Z_test = Z_test, pi_test = pi_test, num_trees_variance = 50, 
-    num_gfr = num_gfr, num_burnin = num_burnin, num_mcmc = num_mcmc, 
-    sample_sigma_leaf_mu = F, sample_sigma_leaf_tau = F
-)