test_that("doConcrete() runs with competing risks", 
          code = {
              # competing risk
              PnEIC <- NULL
              data <- as.data.table(survival::pbc)[, c("time", "status", "trt", "id", "age", "sex")]
              set.seed(0)
              data[, trt := sample(0:1, length(trt), replace = TRUE)]
              
              # using superlearner
              expect_error(object = {
                  concrete.args.SL <- formatArguments(Data = data, EventTime = "time", EventType = "status",
                                                      Treatment = "trt", ID = "id", Intervention = 0:1,
                                                      TargetTime = 2500, TargetEvent = NULL, 
                                                      Model = NULL, Verbose = TRUE, ReturnModels = TRUE)
                  concrete.args.SL$Model$trt <- c("SL.glmnet")
                  formatArguments(concrete.args.SL)
                  concrete.est <- doConcrete(ConcreteArgs = concrete.args.SL)
                  print(concrete.est, Verbose = FALSE)
                  plot(concrete.est, convergence = FALSE, propscores = TRUE, ask = FALSE)
                  }, regexp = NA)
              
              # getOutput
              expect_error(object = {
                  concrete.out <- getOutput(concrete.est)
                  plot(concrete.out, "RR", NullLine = TRUE, GComp = TRUE, ask = FALSE)
                  plot(concrete.out, "RR", NullLine = TRUE, GComp = FALSE, ask = FALSE)
                  plot(concrete.out, "RR", NullLine = FALSE, GComp = TRUE, ask = FALSE)
                  plot(concrete.out, "RR", NullLine = FALSE, GComp = FALSE, ask = FALSE)
                  
                  plot(concrete.out, "RD", NullLine = TRUE, GComp = TRUE, ask = FALSE)
                  plot(concrete.out, "RD", NullLine = TRUE, GComp = FALSE, ask = FALSE)
                  plot(concrete.out, "RD", NullLine = FALSE, GComp = TRUE, ask = FALSE)
                  plot(concrete.out, "RD", NullLine = FALSE, GComp = FALSE, ask = FALSE)
              }, regexp = NA)
              
              # not-converged tmle
              expect_warning(object = {
                  concrete.args.SL$MaxUpdateIter <- 1
                  formatArguments(concrete.args.SL)
                  doConcrete(concrete.args.SL)
              })
              
              # getNormPnEIC using sigma - needs stricter tests to actually be used
              expect_error(object = {
                  concrete:::getNormPnEIC(concrete.est$`A=1`$SummEIC[, PnEIC], NaN)
              }, regexp = NA)
              
              # using sl3
              # expect_error(object = {
              #     if (requireNamespace("sl3", quietly = TRUE) & requireNamespace("Rsolnp", quietly = TRUE)) {
              #         a_lrnrs <- make_learner(Stack, Lrnr_glm$new(), Lrnr_glmnet$new())
              #         concrete.args.sl3 <- formatArguments(Data = data, EventTime = "time", EventType = "status",
              #                                              Treatment = "trt", ID = "id", Intervention = 0:1,
              #                                              TargetTime = 2500, TargetEvent = NULL, 
              #                                              Model = NULL, Verbose = TRUE, ReturnModels = TRUE)
              #         concrete.args.sl3$Model$trt <- a_lrnrs
              #         concrete.args.sl3 <- formatArguments(concrete.args.sl3)
              #         concrete.est <- doConcrete(ConcreteArgs = concrete.args.sl3)
              #         print(concrete.est)
              #         plot(concrete.est, convergence = FALSE, propscores = TRUE, ask = FALSE)
              #     }
              # }, regexp = NA) 
          }
)


test_that("doConcrete() runs right-censored survival",
          code = {
              # competing risk
              expect_error(object = {
                  data <- as.data.table(survival::pbc)[, c("time", "status", "trt", "id", "age", "sex")]
                  set.seed(0)
                  data[, trt := sample(0:1, length(trt), replace = TRUE)]

                  data[, status := as.numeric(status >= 1)] # competing risk or not
                  # data[status == 0, status := sample(1:2, sum(status == 0), replace = TRUE)]


                  concrete.args.SL <- formatArguments(Data = data, EventTime = "time", EventType = "status",
                                                      Treatment = "trt", ID = "id", Intervention = 0:1,
                                                      TargetTime = 2500, TargetEvent = NULL, MaxUpdateIter = 2,
                                                      Model = NULL, Verbose = TRUE, ReturnModels = TRUE)
                  concrete.est <- doConcrete(ConcreteArgs = concrete.args.SL)
                  print(concrete.est, Verbose = FALSE)
                  plot(concrete.est, convergence = TRUE, propscores = FALSE, ask = FALSE)

                  # if (requireNamespace("sl3", quietly = TRUE) & requireNamespace("Rsolnp", quietly = TRUE)) {
                  #     a_lrnrs <- make_learner(Stack, Lrnr_glm$new(), Lrnr_glmnet$new())
                  # 
                  #     concrete.args.sl3 <- formatArguments(Data = data, EventTime = "time", EventType = "status",
                  #                                          Treatment = "trt", ID = "id", Intervention = 0:1,
                  #                                          TargetTime = 2500, TargetEvent = NULL, MaxUpdateIter = 2,
                  #                                          Model = NULL, Verbose = TRUE, ReturnModels = TRUE)
                  #     concrete.args.sl3$Model$trt <- a_lrnrs
                  #     concrete.args.sl3 <- formatArguments(concrete.args.sl3)
                  #     concrete.est <- doConcrete(ConcreteArgs = concrete.args.sl3)
                  #     print(concrete.est)
                  #     plot(concrete.est, convergence = TRUE, propscores = FALSE, ask = FALSE)
                  # }
              }, regexp = NA)
          }
)