test_that("Coxph basic_single_null match", {
  fname <- "ll_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)
  time1 <- "t0"
  time2 <- "t1"
  event <- "lung"
  names <- c("dose")
  term_n <- c(0)
  tform <- c("loglin")
  keep_constant <- c(0)
  a_n <- c(0.0)
  modelform <- "M"
  fir <- 0
  der_iden <- 0
  verbose <- FALSE

  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)
  model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "null" = FALSE)
  e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", fir = 0, der_iden = der_iden, control = control, strat_col = "fac", model_control = model_control)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  for (j in c(TRUE, FALSE)) {
    for (k in c(TRUE, FALSE)) {
      for (l in c(TRUE, FALSE)) {
        model_control <- list("strata" = FALSE, "basic" = j, "single" = k, "null" = l)
        if (verbose) {
          print(model_control)
        }
        a_n <- c(0.0)
        e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", fir = 0, der_iden = der_iden, control = control, strat_col = "fac", model_control = model_control)
        expect_equal(e0$LogLik, e1$LogLik, tolerance = 1e-2)
        if (verbose) {
          print("---------------")
        }
      }
    }
  }
  model_control <- list("strata" = TRUE, "basic" = FALSE, "single" = FALSE, "null" = FALSE)
  e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", fir = 0, der_iden = der_iden, control = control, strat_col = "fac", model_control = model_control)
  for (j in c(TRUE, FALSE)) {
    for (k in c(TRUE, FALSE)) {
      for (l in c(TRUE, FALSE)) {
        model_control <- list("strata" = TRUE, "basic" = j, "single" = k, "null" = l)
        if (verbose) {
          print(model_control)
        }
        a_n <- c(0.0)
        e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", fir = 0, der_iden = der_iden, control = control, strat_col = "fac", model_control = model_control)
        expect_equal(e0$LogLik, e1$LogLik, tolerance = 1e-2)
        if (verbose) {
          print("---------------")
        }
      }
    }
  }
})
test_that("Coxph strata_basic_single_CR", {
  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)
  #
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  #
  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
  j_iterate <- 1
  LL_comp <- c(-81.12079, -81.12079, -77.97632, -77.97632, -68.60263, -68.67108, -75.34028, -75.3691, -81.12079, -81.12079, -77.97632, -77.97632, -68.60263, -68.67108, -75.34028, -75.3691, -122.8909, -122.8909, -119.9814, -119.9814, -109.6211, -109.6742, -117.0147, -117.0539, -122.8909, -122.8909, -119.9814, -119.9814, -109.6211, -109.6742, -117.0147, -117.0539)
  for (i in c(TRUE, FALSE)) {
    for (j in c(TRUE, FALSE)) {
      for (k in c(TRUE, FALSE)) {
        for (l in c(TRUE, FALSE)) {
          model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l)
          if (verbose) {
            print(model_control)
          }
          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)
          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 = "rand", model_control = model_control, cens_weight = "weighting")
          if (abs(e$LogLik - LL_comp[j_iterate]) > 10) {
            message(j_iterate)
          }
          expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
          j_iterate <- j_iterate + 1
          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" = "efron", "double_step" = 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 = "rand", model_control = model_control, cens_weight = "weighting")
          if (abs(e$LogLik - LL_comp[j_iterate]) > 10) {
            message(j_iterate)
          }
          expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
          j_iterate <- j_iterate + 1
          if (verbose) {
            print("---------------")
          }
        }
      }
    }
  }
})

test_that("Pois strata_single", {
  fname <- "ll_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)
  time1 <- "t0"
  df$pyr <- df$t1 - df$t0
  pyr <- "pyr"
  event <- "lung"
  set.seed(3742)
  df$rand <- floor(runif(nrow(df), min = 0, max = 5))
  names <- c("dose", "rand", "rand")
  term_n <- c(2, 1, 0)
  tform <- c("loglin", "lin", "plin")
  keep_constant <- c(0, 0, 0)
  a_n <- c(0.01, 0.1, 0.1)
  modelform <- "PAE"
  fir <- 0
  der_iden <- 0
  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" = 0)
  strat_col <- "fac"
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  verbose <- FALSE
  j_iterate <- 1
  LL_comp <- c(-468.7465, -464.9279, -461.2769, -462.1182, -3033.332, -2734.64, -992.622, -1334.36)
  for (i in c(TRUE, FALSE)) {
    for (j in c(TRUE, FALSE)) {
      model_control <- list("strata" = i, "single" = j)
      if (verbose) {
        print(model_control)
      }
      a_n <- c(0.01, 0.1, 0.1)
      modelform <- "PAE"
      e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col, model_control)
      expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
      j_iterate <- j_iterate + 1
      a_n <- c(0.01, 0.1, 0.1)
      modelform <- "A"
      e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col, model_control)
      expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
      j_iterate <- j_iterate + 1
      if (verbose) {
        print("---------------")
      }
    }
  }
})

test_that("Pois comb_forms", {
  fname <- "ll_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)
  time1 <- "t0"
  df$pyr <- df$t1 - df$t0
  pyr <- "pyr"
  event <- "lung"
  set.seed(3742)
  df$rand <- floor(runif(nrow(df), min = 1, max = 5))
  names <- c("dose", "rand", "rand", "dose", "dose")
  term_n <- c(1, 0, 0, 0, 0)
  tform <- c("loglin", "lin", "plin", "loglin_slope", "loglin_top")
  keep_constant <- c(0, 0, 0, 0, 0)
  a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
  modelform <- "PAE"
  fir <- 0
  der_iden <- 0
  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" = 0)
  strat_col <- "fac"

  verbose <- FALSE
  modelforms <- c("A", "PAE", "M", "PA")
  j_iterate <- 1
  LL_comp <- c(-790.3591, -471.0312, -471.0312, -463.1375, -707.56, -678.2228, -678.2228, -471.4805)
  for (modelform in modelforms) {
    model_control <- list("strata" = FALSE, "single" = FALSE)
    if (verbose) {
      print(model_control)
    }
    a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
    e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col, model_control)
    expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
    j_iterate <- j_iterate + 1
    if (verbose) {
      print("---------------")
    }
  }
  term_n <- c(1, 1, 1, 0, 0)
  for (modelform in modelforms) {
    model_control <- list("strata" = FALSE, "single" = FALSE)
    if (verbose) {
      print(model_control)
    }
    a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
    e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col, model_control)
    expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
    j_iterate <- j_iterate + 1
    if (verbose) {
      print("---------------")
    }
  }
})

test_that("Pois strata_single expanded", {
  fname <- "ll_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)
  time1 <- "t0"
  df$pyr <- df$t1 - df$t0
  pyr <- "pyr"
  event <- "lung"
  set.seed(3742)
  df$rand <- floor(runif(nrow(df), min = 0, max = 5))
  names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand")
  term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
  tform <- c("loglin_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
  keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
  a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)

  modelform <- "PAE"
  fir <- 0
  der_iden <- 0
  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" = 0)
  strat_col <- "fac"
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  verbose <- FALSE
  j_iterate <- 1
  LL_comp <- c(-496.736611833105, -475.421339181129, -466.96134309201, -463.218078300204, -4497.1782941901, -3577.95315136081, -2557.93228186619, -2350.19459732101)
  for (i in c(TRUE, FALSE)) {
    for (j in c(TRUE, FALSE)) {
      model_control <- list("strata" = i, "single" = j)
      if (verbose) {
        print(model_control)
      }
      a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
      modelform <- "PAE"
      e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col, model_control)
      expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
      j_iterate <- j_iterate + 1
      a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
      modelform <- "A"
      e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col, model_control)
      expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
      j_iterate <- j_iterate + 1
      if (verbose) {
        print("---------------")
      }
    }
  }
})

test_that("risk check omnibus plain", {
  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 <- "lung"
  names <- c("dose", "fac", "dose", "fac", "rand")
  term_n <- c(0, 0, 1, 1, 1)
  tform <- c("loglin", "lin", "lin", "plin", "loglin")
  keep_constant <- c(0, 0, 0, 0, 0)
  a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
  modelform <- "M"
  fir <- 0
  der_iden <- 0

  cens_weight <- c(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

  df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)

  model_list <- c("M", "A", "PA", "PAE")
  means <- c(0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.223545565747452, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0079328174864236, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.0532705372693687, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931, 0.186140663450931)
  medians <- c(0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0.155704743824735, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.0822977918796923, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932, 0.112941187026932)
  sums <- c(53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 223.545565747452, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 7.9328174864236, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 53.2705372693687, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931, 186.140663450931)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  for (model_i in 1:4) {
    modelform <- model_list[model_i]
    for (fir in c(0, 1)) {
      for (i in 1:5) {
        model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
        #
        df_order$order <- sample(df_order$order)
        setorderv(df_order, c("order"))
        term_n <- df_order$term_n
        tform <- df_order$tform
        keep_constant <- df_order$keep_constant
        a_n <- df_order$a_n
        names <- df_order$names
        #
        control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
        e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, control = control, model_control = model_control)$Risk

        a_i <- (model_i - 1) * 10 + fir * 5 + i
        expect_equal(mean(e), means[a_i], tolerance = 1e-2)
        expect_equal(median(e), medians[a_i], tolerance = 1e-2)
        expect_equal(sum(e), sums[a_i], tolerance = 1e-2)
      }
    }
  }
})

test_that("risk check omnibus gmix", {
  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 <- "lung"
  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

  model_list <- c("GMIX-R", "GMIX-E", "GMIX")
  names <- c("dose", "fac", "dose", "fac", "rand")
  term_n <- c(0, 0, 1, 1, 2)
  tform <- c("loglin", "loglin", "plin", "plin", "loglin")
  keep_constant <- c(0, 0, 0, 0, 0)
  a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
  df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)

  means <- c(0.6918334, 0.6918334, 0.6918334, 2.9080098, 3.8839872, 2.7077608, 0.6918334, 1.2728663, 1.8214142, 2.9080098, 0.6918334, 1.4807626, 1.8214142, 3.8839872, 0.6918334, 1.4807626, 1.2728663, 2.7077608)
  medians <- c(0.5871897, 0.5871897, 0.5871897, 2.8144283, 3.7521540, 2.4285966, 0.5871897, 1.0226682, 1.7472830, 2.8144283, 0.5871897, 1.2200643, 1.7472830, 3.7521540, 0.5871897, 1.2200643, 1.0226682, 2.4285966)
  sums <- c(691.8334, 691.8334, 691.8334, 2908.0098, 3883.9872, 2707.7608, 691.8334, 1272.8663, 1821.4142, 2908.0098, 691.8334, 1480.7626, 1821.4142, 3883.9872, 691.8334, 1480.7626, 1272.8663, 2707.7608)

  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  count <- 0
  for (model_i in 1:3) {
    modelform <- model_list[model_i]
    if (modelform == "GMIX") {
      for (fir in c(0, 1, 2)) {
        for (term_i in 0:3) {
          model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
          if (fir == 0) {
            model_control$gmix_term <- c(0, term_i %% 2, floor(term_i / 2))
          } else if (fir == 1) {
            model_control$gmix_term <- c(term_i %% 2, 0, floor(term_i / 2))
          } else if (fir == 2) {
            model_control$gmix_term <- c(term_i %% 2, floor(term_i / 2), 1)
          }
          #
          df_order$order <- sample(df_order$order)
          setorderv(df_order, c("order"))
          term_n <- df_order$term_n
          tform <- df_order$tform
          keep_constant <- df_order$keep_constant
          a_n <- df_order$a_n
          names <- df_order$names
          #
          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)
          e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, control = control, model_control = model_control)$Risk
          count <- count + 1
          expect_equal(mean(e), means[count], tolerance = 1e-2)
          expect_equal(median(e), medians[count], tolerance = 1e-2)
          expect_equal(sum(e), sums[count], tolerance = 1e-2)
        }
      }
    } else {
      for (fir in c(0, 1, 2)) {
        model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
        #
        df_order$order <- sample(df_order$order)
        setorderv(df_order, c("order"))
        term_n <- df_order$term_n
        tform <- df_order$tform
        keep_constant <- df_order$keep_constant
        a_n <- df_order$a_n
        names <- df_order$names
        #
        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)
        e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, control = control, model_control = model_control)$Risk
        count <- count + 1
        expect_equal(mean(e), means[count], tolerance = 1e-2)
        expect_equal(median(e), medians[count], tolerance = 1e-2)
        expect_equal(sum(e), sums[count], tolerance = 1e-2)
      }
    }
  }
})

test_that("risk check omnibus dose", {
  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 <- "lung"
  names <- c("dose", "fac", "dose", "fac", "rand")
  term_n <- c(0, 0, 1, 1, 1)
  tform <- c("loglin", "lin", "lin", "plin", "loglin")
  keep_constant <- c(0, 0, 0, 0, 0)
  a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
  modelform <- "M"
  fir <- 0
  der_iden <- 0

  cens_weight <- c(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

  df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)

  model_list <- c("M", "A", "PA", "PAE")
  names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac")
  term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
  tform <- c("loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
  keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
  a_n <- c(-0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)

  df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:22)
  means <- c(3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 2.67297456164882, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 1.75338940554459, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 3.26883019325272, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529, 2.91092317948529)
  medians <- c(2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 2.55867858245872, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 1.6364085377938, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.89841334635291, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858, 2.90408823467858)
  sums <- c(3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 2672.97456164882, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 1753.38940554459, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 3268.83019325272, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529, 2910.92317948529)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  for (model_i in 1:4) {
    modelform <- model_list[model_i]
    for (fir in c(0, 1)) {
      for (i in 1:22) {
        model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
        #
        df_order$order <- sample(df_order$order)
        setorderv(df_order, c("order"))
        term_n <- df_order$term_n
        tform <- df_order$tform
        keep_constant <- df_order$keep_constant
        a_n <- df_order$a_n
        names <- df_order$names
        #
        control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
        e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, control = control, model_control = model_control)$Risk
        a_i <- (model_i - 1) * 44 + fir * 22 + i
        expect_equal(mean(e), means[a_i], tolerance = 1e-2)
        expect_equal(median(e), medians[a_i], tolerance = 1e-2)
        expect_equal(sum(e), sums[a_i], tolerance = 1e-2)
      }
    }
  }
})

test_that("check deviation calc", {
  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"


  #
  event <- "lung"
  names <- c("dose", "fac", "rand")
  term_n <- c(0, 0, 1)
  tform <- c("loglin", "loglin", "loglin")
  keep_constant <- c(0, 0, 0)
  a_n <- c(-0.1, 0.1, 0.2)
  modelform <- "M"
  fir <- 0
  der_iden <- 0

  cens_weight <- c(0)

  verbose <- FALSE

  devs <- c()

  modelform <- "M"
  model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
  for (i in 1:3) {
    a_n <- c(0.6465390, 0.4260961, 0.1572781)
    keep_constant <- c(0, 0, 0)
    keep_constant[i] <- 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)
    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 = "fac", model_control = model_control)
    devs <- c(devs, sum(e$Standard_Deviation))
  }
  a_n <- c(0.6465390, 0.4260961, 0.1572781)
  keep_constant <- c(0, 0, 0)
  #
  control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
  e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "fac", model_control = model_control)
  devs <- c(devs, sum(e$Standard_Deviation))
  expect_equal(devs, c(0.6091269, 0.5356671, 0.7385757, 0.9448081), tolerance = 1e-4)
})

test_that("check Linear Constraints", {
  fname <- "l_pl_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)
  df$pyr <- df$t1 - df$t0
  time1 <- "t0"
  time2 <- "t1"
  pyr <- "pyr"
  event <- "lung"
  names <- c("dose", "fac")
  term_n <- c(0, 0)
  tform <- c("loglin", "plin")
  keep_constant <- c(0, 0)
  model_control <- list("strata" = F, "basic" = F, "single" = F, "null" = F, "constraint" = T)
  Constraint_Matrix <- matrix(c(1, -1), nrow = 1)
  Constraint_const <- c(0.0)
  set.seed(3742)
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  for (i in 1:20) {
    a_n <- 2 * runif(2) - 1
    del <- abs(a_n[1] - a_n[2])
    a_n0 <- rep(sum(a_n) / 2, 2)
    a_n <- a_n0 - c(-del / 2, del / 2)
    modelform <- "M"
    fir <- 0
    der_iden <- 0
    control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
    e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col = "fac", model_control = model_control, cons_mat = Constraint_Matrix, cons_vec = Constraint_const)
    expect_equal(e$beta_0, c(0.357333, 0.357333), tolerance = 1e-2)
  }
  for (i in 1:20) {
    a_n <- 2 * runif(2) - 1
    del <- abs(a_n[1] - a_n[2])
    a_n0 <- rep(sum(a_n) / 2, 2)
    a_n <- a_n0 + c(-del / 2, del / 2)
    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)
    e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col = "fac", model_control = model_control, cons_mat = Constraint_Matrix, cons_vec = Constraint_const)
    expect_equal(e$beta_0, c(-0.472812, -0.472812), tolerance = 1e-2)
  }
})

test_that("Pois double_step change_all calcs", {
  fname <- "ll_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)
  time1 <- "t0"
  df$pyr <- df$t1 - df$t0
  pyr <- "pyr"
  event <- "lung"
  set.seed(3742)
  df$rand <- floor(runif(nrow(df), min = 0, max = 5))
  names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand")
  term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
  tform <- c("loglin_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
  keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
  a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)

  modelform <- "PAE"
  fir <- 0
  der_iden <- 0
  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" = 0)
  strat_col <- "fac"
  if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")}
  verbose <- FALSE
  j_iterate <- 1
  LL_comp <- c(-496.7366, -475.4213, -461.9726, -461.1227, -4497.178, -3577.953, -2561.685, -2339.961)
  for (i in c(0, 1)) {
    for (j in seq_len(length(term_n))) {
      model_control <- list("strata" = F, "single" = F)
      if (verbose) {
        print(model_control)
      }
      a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
      modelform <- "PAE"
      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" = F, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = i)
      expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, j - 1, control, strat_col, model_control))
      if (verbose) {
        print("---------------")
      }
    }
  }
})