Added unit tests for BayesSurvive() (#11)

Since this case call all remaining functions to be translated, it should suffice for testing the rest of #11. Tests will still need to be readapted once R code is removed.

tests/testthat/test-cpp_translation.R

@@ -0,0 +1,45 @@
+# Load the example dataset
+dataset <- list(
+  "X" = simData[[1]]$X,
+  "t" = simData[[1]]$time,
+  "di" = simData[[1]]$status
+)
+
+# Run a Bayesian Cox model
+
+## Initial value: null model without covariates
+initial <- list("gamma.ini" = rep(0, ncol(dataset$X)))
+
+# Prior parameters
+hyperparPooled = list(
+  "c0"     = 2,                      # prior of baseline hazard
+  "tau"    = 0.0375,                 # sd (spike) for coefficient prior
+  "cb"     = 20,                     # sd (slab) for coefficient prior
+  "pi.ga"  = 0.02,                   # prior variable selection probability for standard Cox models
+  "a"      = -4,                     # hyperparameter in MRF prior
+  "b"      = 0.1,                    # hyperparameter in MRF prior
+  "G"      = simData$G               # hyperparameter in MRF prior
+)
+
+# Run a 'Pooled' Bayesian Cox model with graphical learning
+
+set.seed(715074)
+BayesSurvive_wrap <- function(use_cpp = FALSE) {
+  # suppressWarnings(
+    BayesSurvive(
+      survObj = dataset, model.type = "Pooled", MRF.G = TRUE, verbose = TRUE,
+      hyperpar = hyperparPooled, initial = initial, nIter = 100, burnin = 100,
+      cpp = use_cpp
+    )
+  # )
+}
+fit_R <- BayesSurvive_wrap(use_cpp = FALSE)
+fit_C <- BayesSurvive_wrap(use_cpp = TRUE)
+
+test_that("R and C++ objects are similar", {
+  expect_equal(fit_R$call, fit_C$call)
+  expect_equal(fit_R$input, fit_C$input)
+  for (obj in names(fit_R$output)[2]) {
+    expect_equal(fit_R$output[[obj]], fit_C$output[[obj]], tolerance = 1)
+  }
+})