# test_that("starting EM iterations and parameter estimation from a given vector of block memberships works", {
#   set.seed(334)
#   # Simulate a network to work with in this unit test.
#   # Number of nodes
#   N <- 1000
#   # Number of blocks
#   K <- 50
#   # Block memberships (same block size)
#   memb <- rep(1:K, each = N / K)
#   # Covariates
#   x <- sample(1:10, size = N, replace = TRUE)
#   y <- sample(1:10, size = N, replace = TRUE)
# 
#   # Within-block parameters: edges, nodematch("x"), nodematch("y"), triangle
#   list_within_params <- c(-1, 1, 1, 0.5)
#   # Between-block parameters: edges, nodematch("x"), nodematch("y")
#   list_between_params <- c(-3.5, 0.5, 0.5)
# 
#   formula <- g ~ edges + nodematch("x") + nodematch("y") + triangle
# 
#   vertex_id <- 1:N
# 
#   df <- tibble::tibble(
#     id = vertex_id,
#     memb = memb,
#     x = x,
#     y = y
#   )
# 
#   g_sim <-
#     simulate_hergm(
#       formula_for_simulation = formula,
#       data_for_simulation = df,
#       colname_vertex_id = "id",
#       colname_block_membership = "memb",
#       coef_within_block = list_within_params,
#       coef_between_block = list_between_params,
#       ergm_control = ergm::control.simulate.formula(MCMC.burnin = 1000000, MCMC.interval = 1000),
#       seed = 1,
#       n_sim = 1,
#       directed = FALSE,
#       output = "network"
#     )
# 
#   # Conduct clustering
#   cluster_with_feature <-
#     bigergm::hergm(g_sim ~ edges + nodematch("x") + nodematch("y") + triangles,
#       n_clusters = K,
#       estimate_parameters = FALSE,
#       verbose = 0,
#       n_em_step_max = 3,
#       initialization_method = 3,
#       infomap_python = FALSE,
#       clustering_with_features = TRUE,
#       check_alpha_update = TRUE,
#       compute_pi = TRUE,
#       check_lower_bound = TRUE,
#       check_block_membership = TRUE,
#       seeds = 334
#     )
# 
#   # Check if starting from the previously estimated block memberships works.
#   expect_error(result <-
#                  bigergm::hergm(g_sim ~ edges + nodematch("x") + nodematch("y") + triangles,
#       initialized_cluster_data = cluster_with_feature$partition,
#       n_em_step_max = 2,
#       estimate_parameters = FALSE
#     ), NA)
# 
#   # Check if starting from block memberships initialized Python's infomap works.
#   expect_error(result2 <-
#     bigergm::hergm(g_sim ~ edges + nodematch("x") + nodematch("y") + triangles,
#       initialized_cluster_data = system.file("extdata", "initialized_cluster_data_by_infomap.clu", package = "bigergm"),
#       n_em_step_max = 1,
#       estimate_parameters = FALSE,
#       verbose = 1
#     ), NA)
# 
#   # Check if starting paramter estimation from a given vector of block memberships works.
#   expect_error(result3 <-
#     bigergm::hergm(g_sim ~ edges + nodematch("x") + nodematch("y") + triangles,
#       block_membership = result$partition,
#       verbose = 1
#     ), NA)
# 
#   # Check if not specifying n_clusters when initialized_cluster_data and block_membership are null yields an error.
#   expect_error(result4 <-
#     bigergm::hergm(g_sim ~ edges + nodematch("x") + nodematch("y") + triangles,
#       verbose = 1
#     ))
# })