test_that("errors and warnings q = 1", {


  #' @srrstats {G5.2} *Appropriate error and warning behaviour of all functions
  #' should be explicitly demonstrated through tests. In particular,* Tested in
  #' test-warning-error test file.
  #' @srrstats {G5.2b} *Explicit tests should demonstrate conditions which
  #' trigger every one of those messages, and should compare the result with
  #' expected values.* Done. See test-warning-error test file.



  skip_on_cran()
  skip_if_not(
    find_proglang("julia"),
    message = "skip test as julia installation not found."
  )

  set.seed(2351)
  dqrng::dqset.seed(2351)


  # ------------------------------------------------------------------ #
  # test for warnings and errors
  # ------------------------------------------------------------------ #


  for (engine in c("R", "WildBootTests.jl", "R-lean")) {
    lm_fit <-
      lm(
        proposition_vote ~ treatment + ideology1 + log_income +
          Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )
    feols_fit <-
      fixest::feols(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )
    felm_fit <-
      lfe::felm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )
    feols_fit_c <-
      fixest::feols(
        proposition_vote ~ treatment + ideology1 + log_income |
          Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )
    felm_fit_c <-
      lfe::felm(
        proposition_vote ~ treatment + ideology1 + log_income |
          Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )




    # sign_level
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sign_level = 1.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = c("group_id1"),
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sign_level = 1.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sign_level = 1.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sign_level = -1.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = c("group_id1"),
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sign_level = -1.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sign_level = -1.1,
        engine = engine
      )
    )

    # B < 100
    if (engine %in% c("R", "R-lean")) {
      expect_error(
        boottest(
          object = lm_fit,
          clustid = "group_id1",
          B = 99,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      )
      expect_error(
        boottest(
          object = feols_fit,
          clustid = c("group_id1"),
          B = 99,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      )
      expect_error(
        boottest(
          object = felm_fit,
          clustid = "group_id1",
          B = 99,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      )
    }

    # param not in data.frame
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment1",
        conf_int = TRUE,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = c("group_id1"),
        B = 999,
        param = "treatment1",
        conf_int = TRUE,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment1",
        conf_int = TRUE,
        engine = engine
      )
    )

    # rademacher enumeration case
    if (engine != "R-lean") {
      suppressWarnings(expect_warning(
        boottest(
          object = lm_fit,
          clustid = "group_id1",
          B = 9999,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      ))
      suppressWarnings(expect_warning(
        boottest(
          object = feols_fit,
          clustid = "group_id1",
          B = 9999,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      ))
      suppressWarnings(expect_warning(
        boottest(
          object = felm_fit,
          clustid = "group_id1",
          B = 9999,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      ))
    }

    suppressWarnings(expect_warning(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 2^10,
        param = "treatment",
        conf_int = TRUE,
        engine = engine
      )
    ))



    # test for banned function arguments and syntax for fixest
    feols_fit <-
      fixest::feols(
        proposition_vote ~ treatment + ideology1 +
          i(log_income, Q1_immigration),
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = c("group_id1"),
        B = 999,
        param = "treatment1",
        conf_int = TRUE,
        engine = engine
      )
    )

    feols_fit <-
      fixest::feols(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        weights = fwildclusterboot:::create_data(
          N = 10000,
          N_G1 = 20,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )$weights,
        data = fwildclusterboot:::create_data(
          N = 10000,
          N_G1 = 20,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )
    felm_fit <-
      lfe::felm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        weights = fwildclusterboot:::create_data(
          N = 10000,
          N_G1 = 20,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )$weights,
        data = fwildclusterboot:::create_data(
          N = 10000,
          N_G1 = 20,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )

    # joint fe != NULL and weights = on
    if (engine %in% c("R", "R-lean")) {
      expect_error(
        boottest(
          object = felm_fit,
          clustid = "group_id1",
          B = 999,
          param = "treatment",
          conf_int = TRUE,
          fe = "Q1_immigration",
          engine = engine
        )
      )
      expect_error(
        boottest(
          object = feols_fit,
          clustid = c("group_id1"),
          B = 999,
          param = "treatment",
          conf_int = TRUE,
          fe = "Q1_immigration",
          engine = engine
        )
      )
    }

    # nthreads < 1

    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        nthreads = -1,
        engine = engine
      )
    )



    # Warning: In boottest.lm(object = lm_fit, clustid = "group_id1...:
    # Asked for 20 threads while the maximum is 8. Set to 8 threads instead.
    # will probably not run on cran, as max 2 cores

    expect_error(
      boottest(
        object = feols_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        nthreads = -1,
        engine = engine
      )
    )


    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        nthreads = -1,
        engine = engine
      )
    )



    # maxiter
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        maxiter = -1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        maxiter = 0.1,
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = feols_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        maxiter = -1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        maxiter = 0.1,
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        maxiter = -1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        maxiter = 0.1,
        engine = engine
      )
    )


    # tol
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        tol = -1,
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = feols_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        tol = -1,
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        tol = -1,
        engine = engine
      )
    )

    # p-val type
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        p_val_type = "equaltail",
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = feols_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        p_val_type = "equaltail",
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        p_val_type = "equaltail",
        engine = engine
      )
    )

    # B = 1000
    suppressWarnings(expect_message(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 1000,
        param = "treatment",
        conf_int = TRUE,
        engine = engine
      )
    ))

    if (engine != "R-lean") {
      suppressWarnings(expect_message(
        boottest(
          object = feols_fit,
          clustid = "group_id1",
          B = 1000,
          param = "treatment",
          conf_int = TRUE,
          engine = engine
        )
      ))

      suppressWarnings(
        expect_message(
          boottest(
            object = felm_fit,
            clustid = "group_id1",
            B = 1000,
            param = "treatment",
            conf_int = TRUE,
            engine = engine
          )
        )
      )
    }

    # banned function arguments


    # evalute dots ... in methods
    # write sig_level instead of sign_level
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sig_level = 0.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sig_level = 0.1,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = "treatment",
        conf_int = TRUE,
        sig_level = 0.1,
        engine = engine
      )
    )

    lm_fit <-
      lm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      )

    res <- boottest(
      lm_fit,
      clustid = "group_id1",
      B = 999,
      param = "treatment",
      conf_int = TRUE,
      sign_level = 0.1,
      engine = engine
    )

    expect_error(summary(res, a = 1))
    # expect_error(tidy(res, a = 1))
    expect_error(plot(res, a = 1))




    # if 2^(number of clusters) < B and rademacher or mammen weights are used,
    # boottest() switches
    # to full enumeration. In consequence, only 2^(number of clusters - 1)
    # unique t statistics can be computed (see Webb, "Reworking wild bootstrap
    # based inference for clustered errors", 2013)
    # This will cause trouble for the inversion of p-values, for two reasons:
    # a) the p-value function will not
    # be sufficiently smooth b) no appropriate starting value for the root
    # finding procedure will be found
    # this set of tests checks if boottest() throws an error in the part
    # of the code that is responsible for
    # calculating p-values

    lm_fit <-
      lm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        data = fwildclusterboot:::create_data(
          N = 100,
          N_G1 = 4,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1
        )
      )

    # no confidence intervals calculated: expect warning
    expect_warning(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        # guarantees that full enumeration is employed
        B = 2^4 + 1,
        param = "treatment",
        type = "rademacher",
        conf_int = FALSE,
        engine = engine
      )
    )



    # with confidence intervals: expect_error because B < 100
    if (engine == "R") {
      expect_error(
        boottest(
          object = lm_fit,
          clustid = "group_id1",
          # guarantees that full enumeration is employed
          B = 2^4 + 1,
          param = "treatment",
          type = "rademacher",
          conf_int = TRUE,
          engine = engine
        )
      )
    }


    # with confidence intervals: expect_error because B < 100
    if (engine == "R") {
      expect_error(
        boottest(
          object = lm_fit,
          clustid = "group_id1",
          # guarantees that full enumeration is employed
          B = 2^4 + 1,
          param = "treatment",
          type = "mammen",
          conf_int = TRUE,
          engine = engine
        )
      )
    }





    # --------------------------------------------------------------------- #
    # NA values in the cluster variables
    # --------------------------------------------------------------------- #

    data <-
      fwildclusterboot:::create_data(
        N = 100,
        N_G1 = 20,
        icc1 = 0.01,
        N_G2 = 10,
        icc2 = 0.01,
        numb_fe1 = 10,
        numb_fe2 = 10,
        seed = 1
      )
    data[1, "group_id1"] <- NA
    data2 <<- data

    lm_fit <-
      lm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        data = data2
      )

    # expect error as na_omit = FALSE & missing variable in group_id1
    # (the cluster variable)
    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        # guarantees that full enumeration is employed
        B = 999,
        param = "treatment",
        type = "rademacher",
        conf_int = TRUE,
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = lm_fit,
        clustid = ~ group_id1 + group_id2,
        # guarantees that full enumeration is employed
        B = 999,
        param = "treatment",
        type = "rademacher",
        conf_int = TRUE,
        engine = engine
      )
    )

    # NAs in a bootcluster variable

    expect_error(
      boottest(
        object = lm_fit,
        clustid = ~group_id2,
        bootcluster = ~ group_id1 + group_id2,
        # guarantees that full enumeration is employed
        B = 999,
        param = "treatment",
        type = "rademacher",
        conf_int = TRUE,
        engine = engine
      )
    )
    # suppressWarnings(expect_warning(
    #   res <-
    #     boottest(
    #       object = lm_fit,
    #       clustid = "group_id1",
    #       # guarantees that full enumeration is employed
    #       B = 999,
    #       param = "treatment",
    #       type = "rademacher",
    #       conf_int = TRUE,
    #       na_omit = TRUE, engine = engine
    #     )
    # ))
    # expect_equal(res$N, 99)

    # data[2, "group_id1"] <- NA
    # data3 <<- data
    # lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income
    # + Q1_immigration,
    #   data = data3
    # )
    # suppressWarnings(expect_warning(
    #   res <-
    #     boottest(
    #       object = lm_fit,
    #       clustid = "group_id1",
    #       # guarantees that full enumeration is employed
    #       B = 999,
    #       param = "treatment",
    #       type = "rademacher",
    #       conf_int = TRUE,
    #       na_omit = TRUE, engine = engine
    #     )
    # ))
    # expect_equal(res$N, 98)


    # expect error when length(R) != length(param)

    expect_error(
      boottest(
        object = lm_fit,
        clustid = "group_id1",
        B = 999,
        param = c("treatment", "ideology1"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = feols_fit,
        clustid = c("group_id1"),
        B = 999,
        param = c("treatment", "ideology1"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit,
        clustid = "group_id1",
        B = 999,
        param = c("treatment", "ideology1"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )

    # specify a fixed effect that is also clustering variable OR test variable
    # -> error

    expect_error(
      boottest(
        object = feols_fit_c,
        fe = "Q1_immigration",
        clustid = c("Q1_immigration"),
        B = 999,
        param = c("treatment", "ideology1"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit_c,
        fe = "Q1_immigration",
        clustid = "Q1_immigration",
        B = 999,
        param = c("treatment", "ideology1"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )

    expect_error(
      boottest(
        object = feols_fit_c,
        clustid = c("Q1_immigration"),
        B = 999,
        param = c("Q1_immigration"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )
    expect_error(
      boottest(
        object = felm_fit_c,
        clustid = "Q1_immigration",
        B = 999,
        param = c("Q1_immigration"),
        R = 1,
        conf_int = TRUE,
        engine = engine
      )
    )


    # test for p-val type & conf_int == TRUE (tba)

    # check fixest for deleted singletons. boottest() should throw an error

    base <- iris
    names(base) <- c("y", "x1", "x_endo_1", "x_inst_1", "fe")
    set.seed(2)
    base$x_inst_2 <-
      0.2 * base$y + 0.2 * base$x_endo_1 + rnorm(150, sd = 0.5)
    base$x_endo_2 <-
      0.2 * base$y - 0.2 * base$x_inst_1 + rnorm(150, sd = 0.5)
    base$clustid <- sample(1:10, nrow(base), TRUE)
    base$clustid[1] <- 11
    base$clustid[2] <- 12
    # unique singletons -> 11, 12

    feols_fit <- fixest::feols(y ~ x1 | clustid, base, fixef.rm = "both")
    # lfe::felm - no such behavior
    # felm_fit =lfe::felm(y ~ x1 | clustid ,
    # base, keepX = TRUE, keepCX = TRUE)
    # dim(felm_fit$X)
    # dim(felm_fit$cX)
    # summary(felm_fit)

    expect_error(boottest(
      feols_fit,
      param = "x1",
      B = 999,
      clustid = "clustid",
      engine = engine
    ))


    data <-
      fwildclusterboot:::create_data(
        N = 1000,
        N_G1 = 10,
        icc1 = 0.01,
        N_G2 = 10,
        icc2 = 0.01,
        numb_fe1 = 10,
        numb_fe2 = 10,
        seed = 1234
      )
    weights_vec <- data$weights

    lm_fit <-
      lm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        weights = weights_vec,
        data = data
      )
    feols_fit <-
      fixest::feols(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        weights = weights_vec,
        data = data
      )
    lfe_fit <-
      lfe::felm(
        proposition_vote ~ treatment + ideology1 + log_income
          + Q1_immigration,
        weights = weights_vec,
        data = data
      )

    expect_error(
      boottest(
        lm_fit,
        param = "treatment",
        B = 999,
        clustid = "clustid",
        engine = engine
      )
    )
    expect_error(
      boottest(
        feols_fit,
        param = "treatment",
        B = 999,
        clustid = "clustid",
        engine = engine
      )
    )
    expect_error(
      boottest(
        lfe_fit,
        param = "treatment",
        B = 999,
        clustid = "clustid",
        engine = engine
      )
    )


    # param not in clustid, fe
    feols_fit <-
      fixest::feols(
        proposition_vote ~ treatment + ideology1 + log_income |
          Q1_immigration,
        data = data
      )
    lfe_fit <-
      lfe::felm(
        proposition_vote ~ treatment + ideology1 + log_income |
          Q1_immigration,
        data = data
      )

    expect_error(
      boottest(
        feols_fit,
        param = "treatment",
        fe = "treatment",
        B = 999,
        clustid = "group_id1",
        engine = engine
      )
    )
    expect_error(
      boottest(
        lfe_fit,
        param = "treatment",
        fe = "treatment",
        B = 999,
        clustid = "group_id1",
        engine = engine
      )
    )



    if (engine %in% c("R", "R-lean")) {
      # R is matrix
      expect_error(
        boottest(
          lm_fit,
          param = "treatment",
          R = matrix(c(0, 0), 2, 1),
          fe = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = engine
        )
      )
      expect_error(
        boottest(
          feols_fit,
          param = "treatment",
          R = matrix(c(0, 0), 2, 1),
          fe = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = engine
        )
      )
      expect_error(
        boottest(
          felm_fit,
          param = "treatment",
          R = matrix(c(0, 0), 2, 1),
          fe = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = engine
        )
      )

      expect_error(
        boottest(
          lm_fit,
          param = "treatment",
          fe = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = engine,
          p_val_type = ">",
          conf_int = TRUE
        )
      )
      expect_error(
        boottest(
          feols_fit,
          param = "treatment",
          fe = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = engine,
          p_val_type = ">",
          conf_int = TRUE
        )
      )
      expect_error(
        boottest(
          felm_fit,
          param = "treatment",
          fe = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = engine,
          p_val_type = ">",
          conf_int = TRUE
        )
      )
    }


    # no support for R-lean with fe = on
    if (engine == "R-lean") {
      feols_fit_c <-
        fixest::feols(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration,
          data = fwildclusterboot:::create_data(
            N = 1000,
            N_G1 = 10,
            icc1 = 0.01,
            N_G2 = 10,
            icc2 = 0.01,
            numb_fe1 = 10,
            numb_fe2 = 10,
            seed = 1234
          )
        )
      expect_error(
        boottest(
          feols_fit_c,
          param = "treatment",
          fe = "Q1_immigration",
          B = 999,
          clustid = "group_id1",
          engine = "R-lean"
        )
      )

      felm_fit_c <-
        lfe::felm(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration,
          data = fwildclusterboot:::create_data(
            N = 1000,
            N_G1 = 10,
            icc1 = 0.01,
            N_G2 = 10,
            icc2 = 0.01,
            numb_fe1 = 10,
            numb_fe2 = 10,
            seed = 1234
          )
        )
      expect_error(
        boottest(
          felm_fit_c,
          param = "treatment",
          fe = "Q1_immigration",
          B = 999,
          clustid = "group_id1",
          engine = "R-lean"
        )
      )

      # no support for R-lean with weights
      data1 <<-
        fwildclusterboot:::create_data(
          N = 1000,
          N_G1 = 10,
          icc1 = 0.01,
          N_G2 = 10,
          icc2 = 0.01,
          numb_fe1 = 10,
          numb_fe2 = 10,
          seed = 1234
        )
      lm_fit <-
        lm(
          proposition_vote ~ treatment + ideology1 + log_income,
          weights = data1$weights,
          data = data1
        )
      feols_fit <-
        fixest::feols(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration,
          weights = data1$weights,
          data = data1
        )
      felm_fit <-
        lfe::felm(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration,
          weights = data1$weights,
          data = data1
        )

      expect_error(
        boottest(
          lm_fit,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = "R-lean"
        )
      )
      expect_error(
        boottest(
          felm_fit,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = "R-lean"
        )
      )
      expect_error(
        boottest(
          feols_fit,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          engine = "R-lean"
        )
      )


      feols_fit_c <-
        fixest::feols(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration,
          data = fwildclusterboot:::create_data(
            N = 1000,
            N_G1 = 10,
            icc1 = 0.01,
            N_G2 = 10,
            icc2 = 0.01,
            numb_fe1 = 10,
            numb_fe2 = 10,
            seed = 1234
          )
        )
      felm_fit_c <-
        lfe::felm(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration,
          data = fwildclusterboot:::create_data(
            N = 1000,
            N_G1 = 10,
            icc1 = 0.01,
            N_G2 = 10,
            icc2 = 0.01,
            numb_fe1 = 10,
            numb_fe2 = 10,
            seed = 1234
          )
        )

      expect_error(
        boottest(
          feols_fit_c,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          fe = "Q2_defense"
        )
      )

      expect_error(
        boottest(
          felm_fit_c,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          fe = "Q2_defense"
        )
      )

      expect_error(
        boottest(
          feols_fit_c,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          fe = "treatment"
        )
      )

      expect_error(
        boottest(
          felm_fit_c,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          fe = "treatment"
        )
      )

      feols_fit_c <-
        fixest::feols(
          proposition_vote ~ treatment + ideology1 + log_income |
            Q1_immigration^Q2_defense,
          data = fwildclusterboot:::create_data(
            N = 1000,
            N_G1 = 10,
            icc1 = 0.01,
            N_G2 = 10,
            icc2 = 0.01,
            numb_fe1 = 10,
            numb_fe2 = 10,
            seed = 1234
          )
        )

      expect_error(
        boottest(
          feols_fit_c,
          param = "treatment",
          B = 999,
          clustid = "group_id1",
          fe = "treatment"
        )
      )
      
      # varying slopes
      
      feols_fit_c <-
        fixest::feols(
          proposition_vote ~ treatment + ideology1  |
            Q1_immigration[log_income],
          data = fwildclusterboot:::create_data(
            N = 1000,
            N_G1 = 10,
            icc1 = 0.01,
            N_G2 = 10,
            icc2 = 0.01,
            numb_fe1 = 10,
            numb_fe2 = 10,
            seed = 1234
          )
        )
      
      expect_error(
        boottest(
          feols_fit_c,
          param = "treatment",
          B = 999,
          clustid = "group_id1"
        )
      )
    }
  }
})



test_that("error warning IV/WRE and q > 1", {
  skip_on_cran()
  skip_if_not(
    find_proglang("julia"),
    message = "skip test as julia installation not found."
  )


  # drop all NA values from SchoolingReturns
  # SchoolingReturns <-
  #  SchoolingReturns[rowMeans(sapply(SchoolingReturns, is.na)) == 0, ]
  SchoolingReturns <- na.omit(ivreg::SchoolingReturns)
  ivreg_fit <- ivreg::ivreg(
    log(wage) ~ education + age +
      ethnicity + smsa + south + parents14 |
      nearcollege + age + ethnicity + smsa
        + south + parents14,
    data = SchoolingReturns
  )


  # error because invalid param name
  expect_error(suppressMessages(
    boottest(
      object = ivreg_fit,
      clustid = "kww",
      B = 999,
      param = "res",
      type = "rademacher",
      conf_int = FALSE
    )
  ))

  # error due to length(R) != length(param)
  expect_error(suppressMessages(
    boottest(
      object = ivreg_fit,
      clustid = "kww",
      B = 999,
      param = "education",
      R = c(0, 1),
      type = "rademacher",
      conf_int = FALSE
    )
  ))

  # enumeration warning
  expect_warning(suppressMessages(
    boottest(
      object = ivreg_fit,
      clustid = "ethnicity",
      B = 999,
      param = "education",
      type = "rademacher",
      conf_int = FALSE
    )
  ))

  # drop all NA values from SchoolingReturns
  # SchoolingReturns <-
  #   SchoolingReturns[rowMeans(sapply(SchoolingReturns, is.na)) == 0, ]
  SchoolingReturns <- na.omit(SchoolingReturns)
  ivreg_fit <- ivreg::ivreg(
    log(wage) ~ education + age +
      ethnicity + smsa + south + parents14 |
      nearcollege + age + ethnicity + smsa
        + south + parents14,
    data = SchoolingReturns,
    method = "M"
  )

  expect_error(
    boottest(
      object = ivreg_fit,
      B = 999,
      param = "education",
      clustid = "kww",
      type = "mammen",
      impose_null = TRUE
    )
  )



  # bannd args ivreg
  # test for banned function arguments and syntax for fixest with mboottest

  feols_fit <- fixest::feols(
    proposition_vote ~ treatment + ideology1,
    data = fwildclusterboot:::create_data(
      N = 1000,
      N_G1 = 10,
      icc1 = 0.01,
      N_G2 = 10,
      icc2 = 0.01,
      numb_fe1 = 10,
      numb_fe2 = 10,
      seed = 1234
    )
  )
  # R <- clubSandwich::constrain_zero(1:2,coef(feols_fit))
  R <- matrix(c(1, 0, 0, 0, 1, 0), 2, 3)
  expect_error(mboottest(
    object = feols_fit,
    clustid = c("group_id1"),
    B = 999,
    R = R,
    r = 1:3
  ))
})

test_that("error message when character vars in felm and fixest", {
  N <- 1000

  real_1000 <- runif(N, 1, 100)
  Year_cont <- sample(1:10, N, replace = TRUE)
  post1 <- sample(0:1, N, replace = TRUE)
  post2 <- sample(0:1, N, replace = TRUE)
  PWSID <- sample(LETTERS, N, TRUE)
  Utility_state <- as.character(sample(1:10, N, TRUE))
  SEclusters <- as.character(sample(1:10, N, TRUE))

  event_reg_data <-
    data.frame(
      real_1000 = real_1000,
      Year_cont = Year_cont,
      post1 = post1,
      post2 = post2,
      PWSID = PWSID,
      Utility_state = Utility_state,
      SEclusters = SEclusters
    )

  res1 <- fixest::feols(
    real_1000 ~ post1 | SEclusters,
    data = event_reg_data
  )

  res2 <- lfe::felm(
    real_1000 ~ post1 | 0 | 0 | SEclusters,
    data = event_reg_data
  )

  res3 <- lfe::felm(
    real_1000 ~ post1 | Utility_state | 0 | 0,
    data = event_reg_data
  )

  expect_error(
    boottest(
      res1,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )

  expect_error(
    boottest(
      res2,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )

  expect_error(
    boottest(
      res3,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )
})

test_that("error message when character vars in felm and fixest", {
  N <- 1000

  real_1000 <- runif(N, 1, 100)
  Year_cont <- sample(1:10, N, replace = TRUE)
  post1 <- sample(0:1, N, replace = TRUE)
  post2 <- sample(0:1, N, replace = TRUE)
  PWSID <- sample(LETTERS, N, TRUE)
  Utility_state <- as.character(sample(1:10, N, TRUE))
  SEclusters <- as.character(sample(1:10, N, TRUE))

  event_reg_data <-
    data.frame(
      real_1000 = real_1000,
      Year_cont = Year_cont,
      post1 = post1,
      post2 = post2,
      PWSID = PWSID,
      Utility_state = Utility_state,
      SEclusters = SEclusters
    )

  res1 <- fixest::feols(
    real_1000 ~ post1 | SEclusters,
    data = event_reg_data
  )

  res2 <- lfe::felm(
    real_1000 ~ post1 | 0 | 0 | SEclusters,
    data = event_reg_data
  )

  res3 <- lfe::felm(
    real_1000 ~ post1 | Utility_state | 0 | 0,
    data = event_reg_data
  )

  expect_error(
    boottest(
      res1,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )

  expect_error(
    boottest(
      res2,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )

  expect_error(
    boottest(
      res3,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )
})



test_that("error message when character vars in felm and fixest", {
  N <- 1000

  real_1000 <- runif(N, 1, 100)
  Year_cont <- sample(1:10, N, replace = TRUE)
  post1 <- sample(0:1, N, replace = TRUE)
  post2 <- sample(0:1, N, replace = TRUE)
  PWSID <- sample(LETTERS, N, TRUE)
  Utility_state <- as.character(sample(1:10, N, TRUE))
  SEclusters <- as.character(sample(1:10, N, TRUE))

  event_reg_data <-
    data.frame(
      real_1000 = real_1000,
      Year_cont = Year_cont,
      post1 = post1,
      post2 = post2,
      PWSID = PWSID,
      Utility_state = Utility_state,
      SEclusters = SEclusters
    )

  res1 <- fixest::feols(
    real_1000 ~ post1 | SEclusters,
    data = event_reg_data
  )

  res2 <- lfe::felm(
    real_1000 ~ post1 | 0 | 0 | SEclusters,
    data = event_reg_data
  )

  res3 <- lfe::felm(
    real_1000 ~ post1 | Utility_state | 0 | 0,
    data = event_reg_data
  )

  expect_error(
    boottest(
      res1,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )

  expect_error(
    boottest(
      res2,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )

  expect_error(
    boottest(
      res3,
      param = "post1",
      clustid = "SEclusters",
      B = 999
    )
  )
})


test_that("other misc errors", {
  lm_fit <-
    lm(
      proposition_vote ~ treatment + ideology1 + log_income +
        Q1_immigration,
      data = fwildclusterboot:::create_data(
        N = 1000,
        N_G1 = 10,
        icc1 = 0.01,
        N_G2 = 10,
        icc2 = 0.01,
        numb_fe1 = 10,
        numb_fe2 = 10,
        seed = 1234
      )
    )

  felm_fit <-
    lfe::felm(
      proposition_vote ~ treatment + ideology1 + log_income +
        Q1_immigration,
      data = fwildclusterboot:::create_data(
        N = 1000,
        N_G1 = 10,
        icc1 = 0.01,
        N_G2 = 10,
        icc2 = 0.01,
        numb_fe1 = 10,
        numb_fe2 = 10,
        seed = 1234
      )
    )

  expect_error(
    boottest(
      object = lm_fit,
      param = ~treatment,
      clustid = ~group_id1,
      B = 999,
      bootstrap_type = "13",
      engine = "WildBootTests.jl"
    )
  )

  expect_error(
    boottest(
      object = lm_fit,
      param = ~treatment,
      clustid = ~group_id1,
      B = 999,
      bootstrap_type = "13",
      engine = "R-lean"
    )
  )

  expect_error(
    boottest(
      object = lm_fit,
      param = ~treatment,
      clustid = ~group_id1,
      B = 999,
      bootstrap_type = "33",
      engine = "R-lean"
    )
  )

  # felm and 13 or 33
  expect_error(
    boottest(
      object = felm_fit,
      param = ~treatment,
      clustid = ~group_id1,
      B = 999,
      bootstrap_type = "33",
      engine = "R"
    )
  )
  
  expect_error(
    boottest(
      object = feols_fit,
      param = ~treatment,
      clustid = ~group_id1,
      B = 999,
      bootstrap_type = "13",
      bootcluster = "group_id2",
      engine = "R"
    )
  )
  
  expect_error(
    boottest(
      object = feols_fit,
      param = ~treatment,
      clustid = ~group_id1,
      B = 999,
      bootstrap_type = "13",
      bootcluster = ~group_id1 + group_id2,
      engine = "R"
    )
  )
  
  
  
  
  
})