test_that("Coxph strata_basic_single_CR_null log_bound", {
  fname <- "ll_comp_0.csv"
  colTypes <- c("double", "double", "double", "integer", "integer")
  df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
  set.seed(3742)
  df$rand <- floor(runif(nrow(df), min = 0, max = 5))

  time1 <- "t0"
  time2 <- "t1"
  df$censor <- (df$lung == 0)
  event <- "censor"
  names <- c("dose", "fac")
  term_n <- c(0, 0)
  tform <- c("loglin", "loglin")
  keep_constant <- c(1, 0)
  a_n <- c(0, 0)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
  plot_options <- list("name" = paste(tempfile(), "run", sep = ""), "verbose" = FALSE, "studyid" = "studyid", "age_unit" = "years")
  dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options)
  #
  #
  t_ref <- dft$t
  surv_ref <- dft$surv
  t_c <- df$t1
  cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y
  df$weighting <- cens_weight
  #
  event <- "lung"
  a_n <- c(-0.1, -0.1)
  keep_constant <- c(0, 0)

  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)

  verbose <- FALSE
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  for (i in c(TRUE, FALSE)) {
    for (j in c(TRUE, FALSE)) {
      for (k in c(FALSE, FALSE)) {
        for (l in c(TRUE, FALSE)) {
          for (m in c(TRUE, FALSE)) {
            model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l, "log_bound" = TRUE, "manual" = m)
            a_n <- c(-0.1, -0.1)
            control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 1, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
            expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
            a_n <- c(-0.1, -0.1)
            control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 1, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "efron", "double_step" = 0)
            expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
          }
        }
      }
    }
  }
  for (m in c(TRUE, FALSE)) {
    model_control <- list("null" = TRUE, "log_bound" = TRUE, "manual" = m)
    a_n <- c(-0.1, -0.1)
    control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
    expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
    model_control <- list("single" = TRUE, "log_bound" = TRUE, "manual" = m)
    expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
    model_control <- list("gradient" = TRUE, "log_bound" = TRUE, "manual" = m)
    expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
  }
})
test_that("Poisson strata_single log_bound", {
  fname <- "ll_comp_0.csv"
  colTypes <- c("double", "double", "double", "integer", "integer")
  df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
  set.seed(3742)
  df$rand <- floor(runif(nrow(df), min = 0, max = 5))
  df$pyr <- df$t1 - df$t0
  time1 <- "t0"
  time2 <- "t1"
  pyr <- "pyr"
  df$censor <- (df$lung == 0)
  event <- "censor"
  names <- c("dose", "fac")
  term_n <- c(0, 0)
  tform <- c("loglin", "loglin")
  keep_constant <- c(1, 0)
  a_n <- c(0, 0)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
  #
  event <- "lung"
  a_n <- c(-0.1, -0.1)
  keep_constant <- c(0, 0)

  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)

  verbose <- FALSE
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  for (i in c(TRUE, FALSE)) {
    for (k in c(FALSE, FALSE)) {
      for (m in c(TRUE, FALSE)) {
        model_control <- list("strata" = i, "single" = k, "log_bound" = TRUE, "manual" = m)
        a_n <- c(-0.1, -0.1)
        control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
        expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control))
      }
    }
  }
  for (m in c(TRUE, FALSE)) {
    for (k in c(TRUE)) {
      model_control <- list("strata" = FALSE, "single" = k, "log_bound" = TRUE, "manual" = m)
      a_n <- c(-0.1, -0.1)
      control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
      expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control))
      model_control <- list("strata" = FALSE, "gradient" = k, "log_bound" = TRUE, "manual" = m)
      a_n <- c(-0.1, -0.1)
      control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
      expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control))
    }
  }
})
test_that("Coxph EPICURE validated answers, loglin", {
  fname <- "base_example.csv"
  df <- fread(fname)

  time1 <- "entry"
  time2 <- "exit"
  event <- "event"
  names <- c("dose0", "dose1")
  term_n <- c(0, 0)
  tform <- c("loglin", "loglin")
  keep_constant <- c(0, 0)
  a_n <- c(0, 0)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #

  a_n <- c(-0.6067, 5.019)
  model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117)
  v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399)

  alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    a_n <- c(-0.6067, 5.019)
    model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
  }

  v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041)
  v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945)
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    a_n <- c(-0.6067, 5.019)
    model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
  }
})

test_that("Coxph EPICURE validated answers, loglin manual", {
  fname <- "base_example.csv"
  df <- fread(fname)

  time1 <- "entry"
  time2 <- "exit"
  event <- "event"
  names <- c("dose0", "dose1")
  term_n <- c(0, 0)
  tform <- c("loglin", "loglin")
  keep_constant <- c(0, 0)
  a_n <- c(0, 0)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #

  a_n <- c(-0.6067, 5.019)
  model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)

  v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117)
  v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    a_n <- c(-0.6067, 5.019)
    model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0, "manual" = TRUE)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
  }

  v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041)
  v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945)
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    a_n <- c(-0.6067, 5.019)
    model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1, "manual" = TRUE)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
  }
})

test_that("Coxph, lin both", {
  fname <- "base_example.csv"
  df <- fread(fname)

  time1 <- "entry"
  time2 <- "exit"
  event <- "event"
  names <- c("dose0", "dose1", "dose0")
  term_n <- c(0, 0, 1)
  tform <- c("loglin", "loglin", "lin")
  keep_constant <- c(0, 0, 0)
  # a_n <- c(0.2462, 5.020, -0.5909)
  a_n <- c(-1.493177, 5.020007, 1.438377)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(2, 2), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)

  alpha <- 0.005
  a_n <- c(-1.493177, 5.020007, 1.438377)
  model_control <- list("basic" = FALSE, "maxstep" = 2, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
  expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control))
  a_n <- c(-1.493177, 5.020007, 1.438377)
  model_control <- list("basic" = FALSE, "maxstep" = 2, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
  expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control))
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
  v_lower <- c(4.98213, 4.939947, 4.901443, 4.883815, 4.825766, 4.788947, 4.756168, 4.717337, 4.690578)
  # c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871)
  v_upper <- c(5.058015, 5.100656, 5.139868, 5.157913, 5.217754, 5.256045, 5.290357, 5.331277, 5.359649)
  # c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899)
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    alpha <- alpha_list[alpha_i]
    a_n <- c(-1.493177, 5.020007, 1.438377)
    model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
  }

  v_lower <- c(1.174142, 0.8706736, 0.565792, 0.4011414, -0.2129912, 0.2830731, -0.07262359, -0.1306881, 0.3065214)
  # c(-0.643365949558998, -0.677336655540846, -0.706075250211414, -0.718165409196492, -0.753647332793819, -0.773208334303991, -0.789018704115451, -0.806061085000755, -0.816875114954096)
  v_upper <- c(1.702503, 2.0018595, 2.283068, 2.4149146, 2.8653084, 3.1659745, 3.44475771, 3.7898521, 4.0379529)
  # c(-0.521472203247917, -0.444964438813732, -0.327862977142017, -0.235044092073815, 2.91573713669059, 3.21014641617297, 3.48490803194128, 3.82648584413642, 4.07272009904963)
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    alpha <- alpha_list[alpha_i]
    a_n <- c(-1.493177, 5.020007, 1.438377)
    model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    # expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
  }
})

test_that("Poisson, lin both", {
  fname <- "base_example.csv"
  df <- fread(fname)

  pyr <- "exit"
  event <- "event"
  names <- c("dose0", "dose1")
  term_n <- c(0, 1)
  tform <- c("loglin", "lin")
  keep_constant <- c(0, 0)
  a_n <- c(-2.917, 0.06526)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #
  model_control <- list("basic" = FALSE, "maxstep" = 10, "log_bound" = FALSE, "alpha" = 0.1)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)

  alpha <- 0.005
  a_n <- c(-2.917, 0.06526)
  model_control <- list("basic" = FALSE, "maxstep" = 3, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
  expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control))
  a_n <- c(-2.917, 0.06526)
  model_control <- list("basic" = FALSE, "maxstep" = 3, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
  expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control))
})

test_that("Coxph, lin both, check ordering", {
  fname <- "base_example.csv"
  df <- fread(fname)

  time1 <- "entry"
  time2 <- "exit"
  event <- "event"
  names <- c("dose0", "dose1", "dose0")
  term_n <- c(0, 0, 1)
  tform <- c("loglin", "loglin", "lin")
  keep_constant <- c(0, 0, 0)
  # a_n <- c(0.2462, 5.020, -0.5909)
  a_n <- c(0.2462, 5.020, -0.7)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)

  alpha <- 0.005
  a_n <- c(0.2462, 5.020, -0.599)
  model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
  expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control))
  a_n <- c(0.2462, 5.020, -0.599)
  model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
  expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control))
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
  v_lower <- c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871)
  v_upper <- c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899)
  alpha <- alpha_list[1]
  options(warn = -1)
  ### ----------------------------------------- ###
  names <- c("dose0", "dose1", "dose0")
  term_n <- c(0, 0, 1)
  tform <- c("loglin", "loglin", "lin")
  a_n <- c(0.2462, 5.020, -0.599)
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
  e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
  a <- e$Parameter_Limits
  expect_equal(a[1], v_lower[1], tolerance = 1e-4)
  expect_equal(a[2], v_upper[1], tolerance = 1e-4)
  ### ----------------------------------------- ###
  names <- c("dose1", "dose0", "dose0")
  term_n <- c(0, 0, 1)
  tform <- c("loglin", "loglin", "lin")
  a_n <- c(5.020, 0.2462, -0.599)
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 0, "manual" = TRUE)
  e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
  a <- e$Parameter_Limits
  expect_equal(a[1], v_lower[1], tolerance = 1e-4)
  expect_equal(a[2], v_upper[1], tolerance = 1e-4)
  ### ----------------------------------------- ###
  names <- c("dose0", "dose0", "dose1")
  term_n <- c(0, 1, 0)
  tform <- c("loglin", "lin", "loglin")
  a_n <- c(0.2462, -0.599, 5.020)
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE)
  e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
  a <- e$Parameter_Limits
  expect_equal(a[1], v_lower[1], tolerance = 1e-4)
  expect_equal(a[2], v_upper[1], tolerance = 1e-4)
})

test_that("Coxph, lin both, curve search", {
  fname <- "base_example.csv"
  df <- fread(fname)
  time1 <- "entry"
  time2 <- "exit"
  event <- "event"
  names <- c("dose0", "dose1", "dose0")
  term_n <- c(0, 0, 1)
  tform <- c("loglin", "loglin", "lin")
  keep_constant <- c(0, 0, 0)
  # a_n <- c(0.2462, 5.020, -0.5909)
  a_n <- c(-1.493177, 5.020007, 1.438377)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.005)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)

  alpha <- 0.005
  a_n <- c(-1.493177, 5.020007, 1.438377)

  alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 2, "epsilon" = 1e-4, "deriv_epsilon" = 1e-4, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)

  model_control <- list("basic" = FALSE, "maxstep" = 10, "log_bound" = FALSE, "alpha" = 0.005, "para_number" = 2, "step_size" = 0.5)
  v_lower <- c(1.174125, 0.8706585, 0.5657879, 0.401115, -0.749947, -0.7704548, -0.7868122, -0.8043293, -0.8154377)
  v_upper <- c(1.702507, 2.0018841, 2.283073, 2.414909, 2.865287, 3.1660076, 3.4447552, 3.7898479, 4.0379559)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    alpha <- alpha_list[alpha_i]
    a_n <- c(-1.493177, 5.020007, 1.438377)
    model_control <- list("basic" = FALSE, "maxstep" = 20, "log_bound" = FALSE, "alpha" = alpha, "para_number" = 2, "step_size" = 0.5)
    e <- CoxCurveSolver(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-3)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-3)
  }
})

test_that("Poisson, curve search", {
  fname <- "base_example.csv"
  df <- fread(fname)
  pyr <- "exit"
  event <- "event"
  names <- c("dose0", "dose1")
  term_n <- c(0, 1)
  tform <- c("loglin", "lin")
  keep_constant <- c(0, 0)
  a_n <- c(-2.917, 0.06526)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  #
  model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-4, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)

  alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
  v_lower <- c(0.05418212, 0.04221923, 0.03135497, 0.02635009, 0.0100537, -0.0002002051, -0.009355479, -0.02009767, -0.02748292)
  v_upper <- c(0.07591063, 0.0881787, 0.09953124, 0.10471923, 0.1221753, 0.1333447168, 0.143415518, 0.15550048, 0.16386229)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
    skip("Cran Skip")
  }
  for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
    alpha <- alpha_list[alpha_i]
    a_n <- c(-2.917, 0.06526)
    model_control <- list("basic" = FALSE, "maxstep" = 20, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
    e <- PoissonCurveSolver(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control)
    a <- e$Parameter_Limits
    expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-3)
    expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-3)
  }
})