R version 4.5.0 RC (2025-04-03 r88103 ucrt) -- "How About a Twenty-Six"
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> # testthat.R - Test suite for GICHighDimension package
> 
> # Load necessary packages
> library(testthat)
> library(GICHighDimension)
> 
> # Setup Julia environment for tests
> setup_julia_for_tests <- function() {
+   # Initialize Julia connection
+   tryCatch({
+     # Check if Julia is available by attempting setup
+     julia_ok <- JuliaCall::julia_setup(installJulia = FALSE, silent = TRUE)
+     if (!julia_ok) {
+       testthat::skip("Julia not available - skipping tests")
+     }
+ 
+     # Verify Julia is working
+     JuliaCall::julia_eval("1+1")
+ 
+     # Load required Julia packages
+     JuliaCall::julia_library("Distributions")
+     JuliaCall::julia_library("LinearAlgebra")
+ 
+     return(TRUE)
+   }, error = function(e) {
+     testthat::skip(paste("Julia initialization failed:", e$message))
+   })
+ }
> 
> # Test Suite ---------------------------------------------------------------
> 
> test_that("Univariate Normal Model Selection works correctly", {
+   # Skip if Julia not available
+   if (!setup_julia_for_tests()) return()
+ 
+   # Parameters
+   N <- 100L  # Reduced from 3000 for faster testing
+   P <- 20L   # Reduced from 100 for faster testing
+   k <- 3L    # Reduced from 5 for faster testing
+   rho <- 0.0
+ 
+   # Generate test data directly in R (faster than Julia for small cases)
+   true_columns <- sort(sample(1:P, k, replace = FALSE))
+   cov_matrix <- diag(P)
+   X <- matrix(rnorm(N*P), N, P) %*% chol(cov_matrix)
+   true_beta <- rep(0, P)
+   true_beta[true_columns] <- 2
+   Y <- X %*% true_beta + rnorm(N)
+ 
+   # Run model selection
+   result <- GICSelection(
+     X = X,
+     Y = Y,
+     Initial_Column = 1:P,
+     Calculate_GIC = "Calculate_SIC",
+     Calculate_GIC_short = "Calculate_SIC_short",
+     Nsim = 2L  # Reduced from 5 for faster testing
+   )
+ 
+   # Tests
+   selected_cols <- result$GIC_coeff[[length(result$GIC_coeff)]]
+   false_positives <- setdiff(selected_cols, true_columns)
+   false_negatives <- setdiff(true_columns, selected_cols)
+ 
+   expect_lte(length(false_positives), 5,
+              info = "Too many false positives in selection")
+   expect_lte(length(false_negatives), 2,  # More lenient for smaller test
+              info = "Too many false negatives in selection")
+ })
── Skip: Univariate Normal Model Selection works correctly ─────────────────────
Reason: Julia initialization failed: unused argument (silent = TRUE)

> 
> test_that("Univariate Poisson Model Selection works correctly", {
+   # Skip if Julia not available
+   if (!setup_julia_for_tests()) return()
+ 
+   # Reduced parameters for faster testing
+   N <- 100L
+   P <- 20L
+   k <- 3L
+ 
+   # Generate test data in R
+   true_columns <- sort(sample(1:P, k, replace = FALSE))
+   X <- matrix(rnorm(N*P), N, P)
+   true_beta <- rep(0, P)
+   true_beta[true_columns] <- 0.3  # Smaller coefficients for Poisson
+   Y <- rpois(N, exp(X %*% true_beta))
+ 
+   # Run model selection
+   result <- GICSelection(
+     X = X,
+     Y = Y_to_LP(Y, "Poisson"),
+     Initial_Column = 1:P,
+     Calculate_GIC = "Calculate_SIC",
+     Calculate_GIC_short = "Calculate_SIC_short",
+     Nsim = 2L
+   )
+ 
+   # Tests
+   selected_cols <- result$GIC_coeff[[length(result$GIC_coeff)]]
+   expect_lte(length(setdiff(selected_cols, true_columns)), 5,
+              info = "Too many false positives in Poisson model")
+   expect_lte(length(setdiff(true_columns, selected_cols)), 2,
+              info = "Too many false negatives in Poisson model")
+ })
── Skip: Univariate Poisson Model Selection works correctly ────────────────────
Reason: Julia initialization failed: unused argument (silent = TRUE)

> 
> test_that("Multivariate Normal Model Selection works correctly", {
+   # Skip if Julia not available
+   if (!setup_julia_for_tests()) return()
+ 
+   # Reduced parameters
+   N <- 100L
+   P <- 20L
+   k <- 3L
+   m <- 3L
+ 
+   # Generate test data in R
+   X <- matrix(rnorm(N*P), N, P)
+   multi_beta <- matrix(0, P, m)
+   true_columns <- integer(0)
+ 
+   for (i in 1:m) {
+     cols <- sort(sample(1:P, k, replace = FALSE))
+     true_columns <- union(true_columns, cols)
+     multi_beta[cols, i] <- seq(1, 0.1, length.out = k)
+   }
+ 
+   # Generate response
+   Y <- X %*% multi_beta + matrix(rnorm(N*m), N, m)
+ 
+   # Run model selection
+   result <- GICSelection(
+     X = X,
+     Y = Y,
+     Initial_Column = 1:P,
+     Calculate_GIC = "Calculate_SIC",
+     Calculate_GIC_short = "Calculate_SIC_short",
+     Nsim = 2L
+   )
+ 
+   # Tests
+   selected_cols <- result$GIC_coeff[[length(result$GIC_coeff)]]
+   expect_lte(length(setdiff(selected_cols, true_columns)), 5,
+              info = "Too many false positives in multivariate model")
+   expect_lte(length(setdiff(true_columns, selected_cols)), 3,
+              info = "Too many false negatives in multivariate model")
+ })
── Skip: Multivariate Normal Model Selection works correctly ───────────────────
Reason: Julia initialization failed: unused argument (silent = TRUE)

> 
> proc.time()
   user  system elapsed 
   1.23    0.21    1.42