# A helper function for testing the structure of an expected extended `family`
# object.
#
# @param extfam An object of class `family` (at least expected so) which has
#   been extended by projpred.
# @param fam_orig The original object of class `family` which has been used as
#   input to extend_family().
# @param from_brms A single logical value indicating whether the extended family
#   was created for a reference model based on a `brmsfit` (`TRUE`) or not
#   (`FALSE`).
# @param augdat_expected A single logical value indicating whether the extended
#   family is expected to be for augmented-data projection (`TRUE`) or not
#   (`FALSE`).
# @param latent_expected A single logical value indicating whether the reference
#   model is expected to be for latent projection (`TRUE`) or not (`FALSE`).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
extfam_tester <- function(extfam,
                          fam_orig,
                          from_brms = FALSE,
                          augdat_expected = FALSE,
                          latent_expected = FALSE,
                          info_str) {
  # General structure tests -------------------------------------------------

  ## For `fam_orig` ---------------------------------------------------------

  expect_s3_class(fam_orig, "family")
  expect_type(fam_orig, "list")
  # Basic names of `fam_orig` (will be adapted later if necessary):
  fam_orig_nms <- c("family", "link")
  # Check for element `family`:
  expect_true("family" %in% names(fam_orig), info = info_str)
  expect_true(is.vector(fam_orig$family, "character"), info = info_str)
  expect_length(fam_orig$family, 1)
  # Family "cumulative_rstanarm" is an artificial family, so it lacks some
  # elements present in actual families:
  if (fam_orig$family != "cumulative_rstanarm") {
    fam_orig_nms <- c(fam_orig_nms, "linkfun", "linkinv")
  }
  # Families from brms which require the augmented-data projection:
  bfam_nms <- c("categorical", "cumulative", "cratio", "sratio", "acat")
  # Check the names of `fam_orig`:
  if (fam_orig$family %in% bfam_nms) {
    # Just check that the basic names exist (to avoid depending too strongly on
    # brms internals):
    expect_true(all(fam_orig_nms %in% names(fam_orig)), info = info_str)
  } else {
    if (fam_orig$family != "cumulative_rstanarm") {
      fam_orig_nms <- c(fam_orig_nms, "variance", "dev.resids", "aic", "mu.eta",
                        "initialize", "validmu", "valideta")
      if (fam_orig$family %in% c("binomial", "poisson")) {
        fam_orig_nms <- c(fam_orig_nms, "simulate")
      }
    }
    expect_true(all(fam_orig_nms %in% names(fam_orig)), info = info_str)
  }

  ## For `extfam` -----------------------------------------------------------

  expect_s3_class(extfam, "family")
  expect_type(extfam, "list")
  expect_true("for_augdat" %in% names(extfam), info = info_str)
  expect_true(isTRUE(extfam$for_augdat) || isFALSE(extfam$for_augdat),
              info = info_str)
  expect_identical(extfam$for_augdat, augdat_expected, info = info_str)
  expect_true("for_latent" %in% names(extfam), info = info_str)
  expect_true(isTRUE(extfam$for_latent) || isFALSE(extfam$for_latent),
              info = info_str)
  expect_identical(extfam$for_latent, latent_expected, info = info_str)
  extfam_nms_add <- c("ce", "dis_fun", "predvar", "ll_fun", "deviance", "ppd",
                      "for_latent", "for_augdat", "is_extended")
  if (extfam$for_augdat) {
    extfam_nms_add <- setdiff(extfam_nms_add, "deviance")
    extfam_nms_add <- c(extfam_nms_add, "cats", "ce_ptwise")
    if (extfam$family == "categorical") {
      extfam_nms_add <- c(extfam_nms_add, "refcat")
    }
    if (extfam$family == "cumulative_rstanarm") {
      extfam_nms_add <- c(extfam_nms_add, "linkfun", "linkinv")
    }
  } else if (extfam$for_latent) {
    extfam_nms_add <- c(extfam_nms_add, "family_oscale", "link_oscale",
                        "latent_ilink", "latent_ll_oscale", "latent_ppd_oscale")
    if (extfam$family_oscale != "binomial") {
      extfam_nms_add <- c(extfam_nms_add, "cats")
    }
  }
  extfam_nms <- c(fam_orig_nms, extfam_nms_add)
  expect_true(all(extfam_nms %in% names(extfam)), info = info_str)

  # Detailed tests ----------------------------------------------------------

  ## For `fam_orig` ---------------------------------------------------------

  fam_orig_ch <- structure(
    extfam[setdiff(names(fam_orig), c("dpars", "multi_dpars"))],
    class = if (fam_orig$family %in% bfam_nms) {
      c("brmsfamily", "family")
    } else {
      "family"
    }
  )
  if (extfam$family == "binomial") {
    fam_orig_ch$initialize <- fam_orig$initialize
    if (extfam$for_augdat) {
      expect_identical(
        get("augdat_link", envir = environment(fam_orig_ch$linkfun)),
        augdat_link_binom,
        info = info_str
      )
      expect_identical(
        get("augdat_ilink", envir = environment(fam_orig_ch$linkinv)),
        augdat_ilink_binom,
        info = info_str
      )
      fam_orig_ch$linkfun <- fam_orig$linkfun
      fam_orig_ch$linkinv <- fam_orig$linkinv
    }
  } else if (extfam$for_augdat) {
    fam_orig_ch$linkfun <- fam_orig$linkfun
    fam_orig_ch$linkinv <- fam_orig$linkinv
  }
  if (!from_brms) {
    expect_identical(fam_orig_ch, fam_orig,
                     ignore.environment = extfam$for_latent, info = info_str)
  } else if (extfam$family %in% bfam_nms) {
    expect_identical(
      fam_orig_ch,
      structure(fam_orig[setdiff(names(fam_orig), c("dpars", "multi_dpars"))],
                class = class(fam_orig)),
      ignore.environment = TRUE,
      info = info_str
    )
  } else {
    expect_identical(fam_orig_ch, fam_orig, ignore.environment = TRUE,
                     info = info_str)
  }

  ## For `extfam` -----------------------------------------------------------

  if ("family_oscale" %in% names(extfam)) {
    expect_true(extfam$family_oscale %in% fam_nms_long, info = info_str)
  }
  if ("link_oscale" %in% names(extfam)) {
    expect_true(extfam$link_oscale %in% link_str, info = info_str)
  }
  if ("latent_ll_oscale" %in% names(extfam)) {
    if (extfam$family_oscale == "binomial") {
      expect_identical(extfam$latent_ll_oscale, latent_ll_oscale_binom_nocats,
                       info = info_str)
    } else {
      expect_identical(extfam$latent_ll_oscale, latent_ll_oscale_cats,
                       info = info_str)
    }
  }
  if ("latent_ppd_oscale" %in% names(extfam)) {
    if (extfam$family_oscale == "binomial") {
      expect_identical(extfam$latent_ppd_oscale, latent_ppd_oscale_binom_nocats,
                       info = info_str)
    } else {
      expect_identical(extfam$latent_ppd_oscale, latent_ppd_oscale_cats,
                       info = info_str)
    }
  }
  if ("refcat" %in% names(extfam)) {
    expect_true(is.vector(extfam$refcat, "character"), info = info_str)
    expect_length(extfam$refcat, 1)
  }
  if ("cats" %in% names(extfam)) {
    expect_true(is.vector(extfam$cats, "character"), info = info_str)
  }
  expect_true(extfam$is_extended, info = info_str)
  el_nms_clos <- setdiff(
    extfam_nms_add,
    c("family_oscale", "link_oscale", "refcat", "cats", "for_latent",
      "for_augdat", "is_extended")
  )
  for (el_nm in el_nms_clos) {
    expect_type(extfam[[el_nm]], "closure")
  }

  if (extfam$for_augdat) {
    arr_pr <- abind::abind(array(c(0.7, 0.6, 0.3, 0.4), dim = c(2, 1, 2)),
                           array(c(0.1, 0.2, 0.9, 0.8), dim = c(2, 1, 2)),
                           along = 2)
    augm_pr <- arr2augmat(arr_pr, margin_draws = 1)
    expect_equal(extfam$linkinv(extfam$linkfun(augm_pr)), augm_pr,
                 info = info_str)
    # We expect an N x S matrix:
    ce_pt <- extfam$ce_ptwise(mu_ref = augm_pr, mu_sub = augm_pr)
    expect_identical(dim(ce_pt), c(2L, 2L), info = info_str)
    expect_equal(ce_pt,
                 unname(t(-apply(arr_pr * log(arr_pr), c(1, 2), sum))),
                 info = info_str)
    # We expect a vector of length S:
    ce_summed <- extfam$ce(pref = list(mu = augm_pr),
                           data = list(weights = rep(1, 2)),
                           psub = list(mu = augm_pr))
    expect_length(ce_summed, 2)
    expect_equal(ce_summed, colMeans(ce_pt), info = info_str)
    # We expect a vector of length S:
    expect_equal(extfam$dis_fun(pref = list(mu = augm_pr),
                                psub = list(mu = augm_pr)),
                 rep(NA, 2),
                 info = info_str)
    # We expect a vector of length N_augcat = nrow(augm_pr):
    expect_equal(extfam$predvar(mu = augm_pr, dis = NA),
                 rep(NA, 4),
                 info = info_str)
    # We expect an N x S matrix:
    expect_equal(extfam$ll_fun(mu = augm_pr,
                               y = factor(head(letters, 2)[c(2, 1)])),
                 matrix(log(c(0.3, 0.1, 0.4, 0.2)), ncol = 2),
                 info = info_str)
    # We expect a vector of length N:
    if (exists(".Random.seed", envir = .GlobalEnv)) {
      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
    }
    set.seed(seed2_tst)
    ppd_draws <- extfam$ppd(mu = augm_pr[, 1])
    expect_true(is.vector(ppd_draws, "integer"), info = info_str)
    expect_length(ppd_draws, 2)
  }
  # TODO: For the traditional (and latent) projection, add some mathematical
  # checks (i.e., check that the calculations for the objects listed in
  # `extfam_nms_add` are mathematically correct).

  # Output ------------------------------------------------------------------

  return(invisible(TRUE))
}

# A helper function for testing the structure of an expected `refmodel` object.
#
# @param refmod An object of class `refmodel` (at least expected so).
# @param is_datafit A single logical value indicating whether the reference
#   model is expected to be a `datafit` (`TRUE`) or not (`FALSE`).
# @param pkg_nm A single character string specifying the name of the package
#   upon whose fit the reference model (or `datafit`) is based.
# @param fit_expected The expected `refmod$fit` object.
# @param formul_expected The expected `refmod$formula` object. A cbind()
#   expression on the left-hand side of the formula is handled automatically.
# @param data_expected The original dataset used for the reference model fit or
#   as input to get_refmodel() or init_refmodel(). Internal changes (i.e.,
#   inside of projpred) of this dataset are performed automatically.
# @param with_spclformul A single logical value indicating whether the reference
#   model has a special formula (`TRUE`) or not (`FALSE`).
# @param nobsv_expected A single integer value giving the expected number of
#   observations.
# @param wobs_expected The expected numeric vector of observation weights.
# @param offs_expected The expected numeric vector of offsets.
# @param nrefdraws_expected A single integer value giving the expected number of
#   posterior draws in the reference model.
# @param fam_orig The original object of class `family` which has been used as
#   input to extend_family().
# @param mod_nm A single character string specifying the type of model (see
#   object `mod_nms`.
# @param fam_nm A single character string specifying the family (see object
#   `fam_nms`.
# @param augdat_expected A single logical value indicating whether the reference
#   model is expected to be for augmented-data projection (`TRUE`) or not
#   (`FALSE`).
# @param latent_expected A single logical value indicating whether the reference
#   model is expected to be for latent projection (`TRUE`) or not (`FALSE`).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
refmodel_tester <- function(
    refmod,
    is_datafit = FALSE,
    pkg_nm,
    fit_expected,
    formul_expected = get_formul_from_fit(fit_expected),
    data_expected = dat,
    with_spclformul = FALSE,
    nobsv_expected = nobsv,
    wobs_expected = wobs_tst,
    offs_expected = offs_tst,
    nrefdraws_expected = chains_tst * (iter_tst %/% 2L),
    fam_orig,
    mod_nm,
    fam_nm,
    augdat_expected = FALSE,
    latent_expected = FALSE,
    info_str
) {
  # Preparations:
  needs_wobs_added <- !is_datafit && pkg_nm == "rstanarm" &&
    ((length(refmod$fit$weights) > 0 && !all(refmod$fit$weights == 1)) ||
       fam_nm == "binom")
  if (needs_wobs_added) {
    data_expected$projpred_internal_wobs_stanreg <- wobs_expected
  }
  needs_offs_added <- !is_datafit && pkg_nm == "rstanarm" &&
    length(refmod$fit$offset) > 0 && !all(refmod$fit$offset == 0)
  if (needs_offs_added) {
    data_expected$projpred_internal_offs_stanreg <- refmod$fit$offset
  }
  if (refmod$family$for_latent) {
    formul_expected[[2]] <- str2lang(
      paste0(".", as.character(formul_expected[[2]]))
    )
  }
  if (!is.null(attr(terms(formul_expected), "offset"))) {
    # In the reference model, the offset() term is placed last:
    formul_expected <- update(formul_expected,
                              . ~ . - offset(offs_col) + offset(offs_col))
  }
  stdized_lhs <- stdize_lhs(formul_expected)
  formul_expected <- stdized_lhs$fml
  if (with_spclformul) {
    # Reference models take arithmetic expressions on the left-hand side of
    # the formula into account:
    y_spclformul <- stdized_lhs$y_nm_orig
    y_spclformul_new <- stdized_lhs$y_nm
    formul_expected <- update(
      formul_expected,
      as.formula(paste(y_spclformul_new, "~ ."))
    )
    data_expected <- within(data_expected, {
      assign(y_spclformul_new, eval(str2lang(y_spclformul)))
    })
  }
  if (!is_datafit && pkg_nm == "rstanarm" &&
      refmod$fit$stan_function == "stan_gamm4" &&
      refmod$family$family_oscale %||% refmod$family$family == "binomial") {
    # A column added internally by rstanarm which is not relevant for projpred:
    data_expected$temp_y <- 1
  }
  has_grp <- mod_nm %in% c("glmm", "gamm")
  has_add <- mod_nm %in% c("gam", "gamm")
  is_gamm <- mod_nm == "gamm"

  # Test the general structure of the object:
  refmod_nms <- c(
    "fit", "formula", "div_minimizer", "family", "eta", "mu", "mu_offs", "dis",
    "y", "fetch_data", "wobs", "wdraws_ref", "offset", "cvfun", "cvfits",
    "extract_model_data", "ref_predfun", "mu_fun", "cvrefbuilder", "y_oscale",
    "nobs"
  )
  refmod_class_expected <- "refmodel"
  if (is_datafit) {
    refmod_class_expected <- c("datafit", refmod_class_expected)
  }
  expect_s3_class(refmod, refmod_class_expected, exact = TRUE)
  expect_type(refmod, "list")
  expect_named(refmod, refmod_nms, info = info_str)

  # fit
  if (!is_datafit && pkg_nm == "rstanarm" && mod_nm == "gamm" &&
      !all(wobs_expected == 1)) {
    fit_expected$weights <- wobs_expected
  }
  expect_identical(refmod$fit, fit_expected, info = info_str)

  # formula
  if (!is_gamm) {
    # TODO (GAMMs): Adapt the expected formula to GAMMs.
    if (is_datafit && pkg_nm == "brms") {
      expect_equal(refmod$formula, formul_expected, info = info_str)
    } else {
      expect_identical(refmod$formula, formul_expected, info = info_str)
    }
  }

  # div_minimizer
  expect_type(refmod$div_minimizer, "closure")

  # family
  extfam_tester(refmod$family,
                fam_orig = fam_orig,
                from_brms = (pkg_nm == "brms"),
                augdat_expected = augdat_expected,
                latent_expected = latent_expected,
                info_str = info_str)

  # eta
  # In principle, it would be desirable to compare `refmod$eta` to
  # `refmod$family$linkfun(refmod$mu)`, but numerical underflow and overflow can
  # make this problematic. (Here in the unit tests, we generate rather extreme
  # linear predictors, which should be avoided in the first place, but doesn't
  # seem to be that simple in the context of these specific unit tests.)
  if (refmod$family$family %in% c("binomial")) {
    eta_cut <- refmod$eta
    mu_cut <- refmod$mu
    tol_ex <- 1e-12
    eta_cut[eta_cut < f_binom$linkfun(tol_ex)] <- f_binom$linkfun(tol_ex)
    eta_cut[eta_cut > f_binom$linkfun(1 - tol_ex)] <-
      f_binom$linkfun(1 - tol_ex)
    mu_cut[mu_cut < tol_ex] <- tol_ex
    mu_cut[mu_cut > 1 - tol_ex] <- 1 - tol_ex
    expect_equal(eta_cut, refmod$family$linkfun(mu_cut), info = info_str)
  } else if (refmod$family$family %in% fam_nms_aug_long &&
             (any(abs(refmod$mu - 0) <= .Machine$double.eps) ||
              any(abs(refmod$mu - 1) <= .Machine$double.eps))) {
    # The degenerate probabilities in `refmod$mu` are probably due to numerical
    # underflow and overflow (for zeros and ones, respectively), so applying the
    # link function would lead to infinite values. Thus, the only sensible (and
    # quickly feasible) check is:
    expect_equal(refmod$mu, refmod$family$linkinv(refmod$eta), info = info_str)
  } else {
    expect_equal(refmod$eta, refmod$family$linkfun(refmod$mu), info = info_str)
  }

  # mu
  ### Not needed because of the more precise test below:
  # expect_true(is.matrix(refmod$mu), info = info_str)
  # expect_type(refmod$mu, "double")
  # expect_identical(dim(refmod$mu), c(nobsv_expected, nrefdraws_expected),
  #                  info = info_str)
  ###
  if (!is_datafit) {
    ### Helpful for debugging:
    # mu_expected_ch <- unname(t(posterior_linpred(refmod$fit)))
    ###
    if (pkg_nm == "rstanarm") {
      # In this case, the linear predictors are calculated manually because of
      # the offset issues in rstanarm.
      drws <- as.matrix(refmod$fit)
      if ("(Intercept)" %in% colnames(drws)) {
        drws_icpt <- drws[, "(Intercept)"]
      } else {
        drws_icpt <- numeric(nrow(drws))
        drws_thres <- drws[, grep("\\|", colnames(drws))]
      }
      drws_beta_cont <- drws[
        ,
        setdiff(grep("xco\\.", colnames(drws), value = TRUE),
                grep("z\\.", colnames(drws), value = TRUE)),
        drop = FALSE
      ]
      predictors_cont <- colnames(drws_beta_cont)
      predictors_cont <- sub("(I\\(.*as\\.logical\\(.*\\)\\))TRUE", "\\1",
                             predictors_cont)
      predictors_cont <- unique(sub("(poly[m]*\\(.*\\))[[:digit:]]+", "\\1",
                                    predictors_cont))
      mm_cont <- model.matrix(
        as.formula(paste("~", paste(predictors_cont, collapse = " + "))),
        data = data_expected
      )
      stopifnot(identical(c("(Intercept)", colnames(drws_beta_cont)),
                          colnames(mm_cont)))
      mu_expected <- cbind(drws_icpt, drws_beta_cont) %*% t(mm_cont)
      cate_post <- lapply(names(x_cate_list), function(x_cate_idx) {
        sapply(x_cate_list[[x_cate_idx]]$x_cate, function(lvl_obs_i) {
          if (lvl_obs_i != "lvl1") {
            return(drws[, paste0("xca.", x_cate_idx, lvl_obs_i)])
          } else {
            return(matrix(0, nrow = nrow(drws)))
          }
        })
      })
      mu_expected <- mu_expected + do.call("+", cate_post)
      if (has_grp) {
        r_post <- lapply(names(z_list), function(z_nm) {
          unname(
            drws[, paste0("b[(Intercept) ", z_nm, ":", z_list[[z_nm]]$z, "]")] +
              drws[, paste0("b[xco.1 ", z_nm, ":", z_list[[z_nm]]$z, "]")] %*%
              diag(x_cont[, 1])
          )
        })
        mu_expected <- mu_expected + do.call("+", r_post)
      }
      if (has_add) {
        drws_beta_s <- drws[
          ,
          grep("^s\\(", colnames(drws), value = TRUE),
          drop = FALSE
        ]
        ### TODO (GAMs and GAMMs): Do this manually:
        mm_s <- rstanarm:::pp_data(refmod$fit)$x
        mm_s <- mm_s[, grep("^s\\(", colnames(mm_s), value = TRUE),
                     drop = FALSE]
        ###
        mu_expected <- mu_expected + drws_beta_s %*% t(mm_s)
      }
      mu_expected <- unname(mu_expected)
    } else if (pkg_nm == "brms") {
      mu_expected <- sweep(posterior_linpred(refmod$fit), 2L, offs_expected)
      if (fam_nm %in% fam_nms_ordin) {
        drws <- as.matrix(refmod$fit)
        drws_thres <- drws[, grep("b_Intercept\\[", colnames(drws))]
      }
    }
    if (refmod$family$family != "gaussian") {
      if (refmod$family$family %in% fam_nms_aug_long) {
        if (refmod$family$family %in% fam_nms_ordin_long) {
          if (refmod$family$family %in% c("cumulative", "cumulative_rstanarm",
                                          "sratio")) {
            mu_expected <- apply(drws_thres, 2, function(thres_vec) {
              thres_vec - mu_expected
            }, simplify = FALSE)
          } else if (refmod$family$family %in% c("cratio", "acat")) {
            mu_expected <- apply(drws_thres, 2, function(thres_vec) {
              mu_expected - thres_vec
            }, simplify = FALSE)
          }
          mu_expected <- do.call(abind::abind, c(mu_expected, rev.along = 0))
        }
        mu_expected <- arr2augmat(mu_expected, margin_draws = 1)
        mu_expected <- refmod$family$linkinv(mu_expected)
      } else {
        mu_expected <- fam_orig$linkinv(mu_expected)
      }
    }
    if (refmod$family$for_augdat && refmod$family$family == "binomial") {
      mu_expected <- cbind(1 - mu_expected,
                           mu_expected)
    }
    if (!refmod$family$family %in% fam_nms_aug_long) {
      mu_expected <- t(mu_expected)
    }
    if (refmod$family$for_augdat && refmod$family$family == "binomial") {
      mu_expected <- structure(mu_expected, ndiscrete = 2L, class = "augmat")
    }
    expect_equal(refmod$mu, mu_expected, info = info_str)
  } else {
    if (refmod$family$family != "binomial") {
      expect_identical(refmod$mu, as.matrix(refmod$y), info = info_str)
    } else {
      expect_identical(refmod$mu, as.matrix(refmod$y / refmod$wobs),
                       info = info_str)
    }
  }

  # mu_offs
  expect_equal(
    refmod$mu_offs,
    refmod$family$linkinv(
      refmod$eta + ifelse(refmod$family$family %in% fams_neg_linpred(), -1, 1) *
        refmod$offset
    ),
    info = info_str
  )
  if (!is_datafit) {
    expect_equal(
      refmod$mu_offs,
      refmod$family$linkinv(refmod$ref_predfun(
        refmod$fit, excl_offs = FALSE,
        mlvl_allrandom = getOption("projpred.mlvl_proj_ref_new", FALSE)
      )),
      info = info_str
    )
  }

  # dis
  if (refmod$family$family == "gaussian") {
    if (is_datafit) {
      expect_identical(refmod$dis, 0, info = info_str)
    } else if (latent_expected) {
      expect_identical(refmod$dis, rep(1.6, nrefdraws_expected),
                       info = info_str)
    } else {
      expect_true(is.vector(refmod$dis, "double"), info = info_str)
      expect_length(refmod$dis, nrefdraws_expected)
      expect_true(all(refmod$dis > 0), info = info_str)
    }
  } else {
    expect_identical(refmod$dis, rep(NA, nrefdraws_expected), info = info_str)
  }

  # y
  ### Not needed because of the more precise test below:
  # expect_true(is.vector(refmod$y, "numeric"), info = info_str)
  # expect_length(refmod$y, nobsv_expected)
  ###
  if (!with_spclformul) {
    y_expected <- dat[[paste("y", mod_nm, fam_nm, sep = "_")]]
    if (!is_datafit && pkg_nm == "brms" && packageVersion("brms") < "2.16.11" &&
        fam_nm == "brnll") {
      # Fixed (as a side effect) by brms PR #1314:
      y_expected <- as.numeric(y_expected)
    }
    if (latent_expected) {
      y_expected <- unname(colMeans(posterior_linpred(fit_expected)))
    }
  } else {
    y_expected <- data_expected[[y_spclformul_new]]
  }
  if (refmod$family$for_augdat) {
    y_expected <- as.factor(y_expected)
    if (fam_nm %in% fam_nms_aug && pkg_nm == "brms") {
      # brms seems to set argument `contrasts`, but this is not important for
      # projpred, so ignore it in the comparison:
      attr(y_expected, "contrasts") <- attr(refmod$y, "contrasts")
    }
  }
  expect_identical(refmod$y, y_expected, info = info_str)

  # fetch_data
  expect_type(refmod$fetch_data, "closure")
  if (!is_gamm) {
    # TODO (GAMMs): Adapt this to GAMMs.
    if (latent_expected) {
      data_expected[[stdized_lhs$y_nm]] <- y_expected
    }
    if ((!is_datafit && pkg_nm != "brms") ||
        (is_datafit && (pkg_nm == "brms" || fam_nm != "binom"))) {
      if (pkg_nm != "brms" && fam_nm == "binom" && needs_wobs_added) {
        data_expected <- data_expected[, c(
          setdiff(names(data_expected), "projpred_internal_wobs_stanreg"),
          "projpred_internal_wobs_stanreg"
        )]
      }
      expect_identical(refmod$fetch_data(), data_expected, info = info_str)
    } else if (!is_datafit && pkg_nm == "brms") {
      if (!with_spclformul) {
        refdat_colnms <- as.character(
          attr(terms(formul_expected), "variables")
        )[-1]
        refdat_colnms <- sub(".*\\|[[:blank:]]*", "", refdat_colnms)
        refdat_colnms <- sub("s\\((.*)\\)", "\\1", refdat_colnms)
        if (!all(wobs_expected == 1)) {
          refdat_colnms <- c(head(refdat_colnms, 1),
                             "wobs_col",
                             tail(refdat_colnms, -1))
        }
        refdat_colnms <- sub("^offset\\((.*)\\)$", "\\1", refdat_colnms)
        if (latent_expected) {
          # Re-order:
          refdat_colnms <- c(sub("^\\.", "", stdized_lhs$y_nm),
                             setdiff(refdat_colnms, stdized_lhs$y_nm),
                             stdized_lhs$y_nm)
        }
        refdat_ch <- data_expected[, refdat_colnms, drop = FALSE]
        expect_equal(refmod$fetch_data(), refdat_ch, check.attributes = FALSE,
                     info = info_str)
      } else {
        # TODO: The check in this case is not optimal yet because it subsets
        # `refmod$fetch_data()` to only those columns which also exist in the
        # rebuilt dataset. It would be more desirable to check *all* columns of
        # `refmod$fetch_data()`.
        refdat_ch <- model.frame(formul_expected, data = data_expected)
        if (!all(wobs_expected == 1)) {
          refdat_ch <- cbind(refdat_ch, wobs_col = data_expected$wobs_col)
        }
        names(refdat_ch) <- sub("^offset\\((.*)\\)$", "\\1", names(refdat_ch))
        expect_equal(refmod$fetch_data()[, names(refdat_ch), drop = FALSE],
                     refdat_ch, check.attributes = FALSE, info = info_str)
      }
    } else if (is_datafit && pkg_nm != "brms" && fam_nm == "binom") {
      refdat_ch <- data_expected
      y_nm <- paste("y", mod_nm, fam_nm, sep = "_")
      refdat_ch$dummy_nm <- refdat_ch$wobs_col - refdat_ch[, y_nm]
      names(refdat_ch)[names(refdat_ch) == "dummy_nm"] <- paste("wobs_col -",
                                                                y_nm)
      expect_identical(refmod$fetch_data(), refdat_ch, info = info_str)
    } else {
      stop("This case should not occur.")
    }
  }

  # wobs
  ### Not needed because of the more precise test below:
  # expect_true(is.vector(refmod$wobs, "numeric"), info = info_str)
  # expect_length(refmod$wobs, nobsv_expected)
  # expect_true(all(refmod$wobs > 0), info = info_str)
  ###
  if (!is_gamm) {
    # TODO (GAMMs): Adapt the expected observation weights to GAMMs.
    expect_identical(refmod$wobs, wobs_expected, info = info_str)
  }

  # wdraws_ref
  expect_true(is.vector(refmod$wdraws_ref, "double"), info = info_str)
  expect_length(refmod$wdraws_ref, nrefdraws_expected)
  expect_true(all(refmod$wdraws_ref > 0), info = info_str)

  # offset
  ### Not needed because of the more precise test below:
  # expect_true(is.vector(refmod$offset, "double"), info = info_str)
  # expect_length(refmod$offset, nobsv_expected)
  ###
  expect_identical(refmod$offset, offs_expected, info = info_str)

  # cvfun
  expect_type(refmod$cvfun, "closure")

  # cvfits
  expect_null(refmod$cvfits, info = info_str)
  expect_false(is.null(refmod$cvfun) && is.null(refmod$cvfits), info = info_str)

  # extract_model_data
  expect_type(refmod$extract_model_data, "closure")

  # ref_predfun
  expect_type(refmod$ref_predfun, "closure")

  # mu_fun
  expect_type(refmod$mu_fun, "closure")

  # cvrefbuilder
  expect_type(refmod$cvrefbuilder, "closure")

  # y_oscale
  if (latent_expected) {
    y_oscale_expected <- data_expected[[sub("^\\.", "", stdized_lhs$y_nm)]]
    if (!is.null(refmod$family$cats) && !is.factor(y_oscale_expected)) {
      y_oscale_expected <- as.factor(y_oscale_expected)
    }
    if (pkg_nm == "brms") {
      # brms seems to set argument `contrasts`, but this is not important for
      # projpred, so ignore it in the comparison:
      attr(y_oscale_expected, "contrasts") <- attr(refmod$y_oscale, "contrasts")
    }
  } else {
    y_oscale_expected <- y_expected
  }
  expect_identical(refmod$y_oscale, y_oscale_expected, info = info_str)

  # nobs
  expect_identical(refmod$nobs, nrow(refmod$fetch_data()), info = info_str)

  return(invisible(TRUE))
}

# A helper function for testing the structure of a list of fits for the same
# single submodel, with that submodel coming from a traditional projection.
#
# @inheritParams outdmin_tester
# @param seq_extensive_tests A numeric vector of indexes from
#   `seq_along(outdmin_totest)` indicating those elements of `outdmin_totest`
#   for which more extensive tests should be conducted.
# @param nobsv The number of (possibly augmented) observations.
#
# @return `TRUE` (invisible).
outdmin_tester_trad <- function(
    outdmin_totest,
    nprjdraws_expected,
    sub_formul,
    sub_data,
    sub_fam,
    has_grp = formula_contains_group_terms(sub_formul[[1]]),
    has_add = formula_contains_additive_terms(sub_formul[[1]]),
    wobs_expected = wobs_tst,
    prd_trms_vsel_L1_search = NULL,
    with_offs = FALSE,
    augdat_cats = NULL,
    allow_w_zero = FALSE,
    check_y_from_resp = TRUE,
    seq_extensive_tests = seq_extensive_tests,
    nobsv = nobsv,
    info_str
) {
  from_vsel_L1_search <- !is.null(prd_trms_vsel_L1_search)

  if (!has_grp && !has_add) {
    sub_x_expected <- model.matrix(sub_formul[[1]], data = sub_data)
    sub_x_expected <- sub_x_expected[
      , colnames(sub_x_expected) != "(Intercept)", drop = FALSE
    ]
    I_logic_excl <- grep("I\\(.*as\\.logical\\(.*\\)\\)FALSE",
                         colnames(sub_x_expected), value = TRUE)
    if (length(I_logic_excl) && from_vsel_L1_search) {
      sub_x_expected <- sub_x_expected[
        , setdiff(colnames(sub_x_expected), I_logic_excl), drop = FALSE
      ]
    }
    ncoefs <- ncol(sub_x_expected)
    if (from_vsel_L1_search) {
      # Unfortunately, model.matrix() uses terms() and there seems to be no way
      # to set `keep.order = TRUE` in that internal terms() call. Thus, we have
      # to reorder the columns manually:
      if (length(prd_trms_vsel_L1_search)) {
        terms_contr_expd <- lapply(prd_trms_vsel_L1_search, function(term_crr) {
          term_crr <- strsplit(term_crr, ":")[[1]]
          main_terms_expand <- lapply(term_crr, function(main_term_crr) {
            if (!is.factor(sub_data[[main_term_crr]])) {
              main_term_crr <- sub("(I\\(.*as\\.logical\\(.*\\)\\))", "\\1TRUE",
                                   main_term_crr)
              main_term_crr <- expand_poly(
                main_term_crr, info_str = paste0(info_str, "__", main_term_crr)
              )
              return(main_term_crr)
            } else {
              return(paste0(main_term_crr,
                            levels(sub_data[[main_term_crr]])[-1]))
            }
          })
          return(apply(expand.grid(main_terms_expand), 1, paste,
                       collapse = ":"))
        })
        terms_contr_expd <- unlist(terms_contr_expd)
        colnames(sub_x_expected) <- reorder_ias(colnames(sub_x_expected),
                                                terms_contr_expd)
        sub_x_expected <- sub_x_expected[
          ,
          colnames(sub_x_expected)[order(match(colnames(sub_x_expected),
                                               terms_contr_expd))],
          drop = FALSE
        ]
      }
    }
    subfit_nms <- c("alpha", "beta", "w", "formula", "x", "y", "xlvls")
    if (from_vsel_L1_search) {
      subfit_nms <- setdiff(subfit_nms, "y")
    }
    for (j in seq_along(outdmin_totest)) {
      expect_s3_class(outdmin_totest[[!!j]], "subfit")
      expect_type(outdmin_totest[[!!j]], "list")
      expect_named(outdmin_totest[[!!j]], subfit_nms, info = info_str)

      if (j %in% seq_extensive_tests) {
        expect_true(is.vector(outdmin_totest[[!!j]]$alpha, "double"),
                    info = info_str)
        expect_length(outdmin_totest[[!!j]]$alpha, 1)

        if (ncoefs > 0 || !from_vsel_L1_search) {
          expect_true(is.matrix(outdmin_totest[[!!j]]$beta), info = info_str)
          expect_true(is.numeric(outdmin_totest[[!!j]]$beta), info = info_str)
          expect_identical(dim(outdmin_totest[[!!j]]$beta), c(ncoefs, 1L),
                           info = info_str)
          expect_identical(rownames(outdmin_totest[[!!j]]$beta),
                           colnames(outdmin_totest[[!!j]]$x),
                           info = info_str)
        } else {
          expect_null(outdmin_totest[[!!j]]$beta, info = info_str)
        }

        if (!from_vsel_L1_search) {
          expect_true(is.matrix(outdmin_totest[[!!j]]$w), info = info_str)
          expect_type(outdmin_totest[[!!j]]$w, "double")
          expect_identical(dim(outdmin_totest[[!!j]]$w), c(nobsv, 1L),
                           info = info_str)
        } else {
          expect_true(is.vector(outdmin_totest[[!!j]]$w, "double"),
                      info = info_str)
          expect_length(outdmin_totest[[!!j]]$w, nobsv)
        }
        if (allow_w_zero) {
          expect_true(all(outdmin_totest[[!!j]]$w >= 0), info = info_str)
        } else {
          expect_true(all(outdmin_totest[[!!j]]$w > 0), info = info_str)
        }
        if (sub_fam == "gaussian") {
          # Note: For non-Gaussian families, a comparison of
          # `outdmin_totest[[j]]$w` with `wobs_expected` doesn't make sense
          # since `glm_ridge(<...>)$w` contains the weights of the
          # pseudo-Gaussian observations as calculated in pseudo_data().
          expect_equal(as.vector(outdmin_totest[[!!j]]$w),
                       wobs_expected %||% rep(1, nobsv),
                       info = info_str)
        }

        expect_s3_class(outdmin_totest[[!!j]]$formula, "formula")
        if (!grepl(":", as.character(outdmin_totest[[j]]$formula)[3])) {
          expect_equal(outdmin_totest[[!!j]]$formula, sub_formul[[!!j]],
                       info = info_str)
        } else {
          # The order of terms as well as the order of individual terms within
          # ":" interaction terms might be changed in the reference model:
          expect_equal(outdmin_totest[[!!j]]$formula[[2]],
                       sub_formul[[!!j]][[2]],
                       info = info_str)
          trms_to_test <- labels(terms(outdmin_totest[[j]]$formula))
          trms_ch <- labels(terms(sub_formul[[j]]))
          trms_ch <- reorder_ias(trms_ch, trms_to_test)
          expect_identical(trms_to_test, trms_ch, info = info_str)
        }

        x_to_test <- outdmin_totest[[j]]$x
        x_ch <- sub_x_expected
        dimnames(x_ch)[[2]] <- reorder_ias(dimnames(x_ch)[[2]],
                                           dimnames(x_to_test)[[2]])
        expect_identical(x_to_test, x_ch, info = info_str)

        if (!from_vsel_L1_search) {
          y_ch <- setNames(eval(str2lang(as.character(sub_formul[[j]])[2]),
                                sub_data),
                           seq_len(nobsv))
          if (!is.null(augdat_cats)) {
            stopifnot(is.factor(y_ch), identical(levels(y_ch), c("0", "1")))
            y_ch <- setNames(as.integer(y_ch) - 1L, names(y_ch))
          }
          expect_identical(outdmin_totest[[!!j]]$y, y_ch, info = info_str)
        }

        # TODO: For now, we don't need to take `character` predictors into
        # account because in the tests, we are currently always using `factor`s
        # in such cases. In the future, however, we should extend the tests to
        # `character` predictors as well and then the following needs to be
        # adapted.
        nms_fac <- grep("xca", labels(terms(outdmin_totest[[j]]$formula)),
                        value = TRUE)
        nms_fac <- grep(":", nms_fac, value = TRUE, invert = TRUE)
        if (length(nms_fac)) {
          xlvls_ch <- lapply(setNames(nm = nms_fac), function(nm_fac) {
            levels(droplevels(sub_data[[nm_fac]]))
          })
          expect_setequal(names(outdmin_totest[[!!j]]$xlvls), names(xlvls_ch))
          xlvls_ch <- xlvls_ch[names(outdmin_totest[[j]]$xlvls)]
          expect_identical(outdmin_totest[[!!j]]$xlvls, xlvls_ch,
                           info = info_str)
        } else {
          expect_null(outdmin_totest[[!!j]]$xlvls, info = info_str)
        }
      }
    }
  } else if (has_grp && !has_add) {
    if (sub_fam == "gaussian") {
      for (j in seq_along(outdmin_totest)) {
        expect_s4_class(outdmin_totest[[!!j]], "lmerMod")
      }
    } else {
      for (j in seq_along(outdmin_totest)) {
        expect_s4_class(outdmin_totest[[!!j]], "glmerMod")
      }
    }

    sub_trms_for_mf <- labels(terms(sub_formul[[1]]))
    sub_trms_for_mf <- sub(
      "^[[:blank:]]*(.*)[[:blank:]]*\\|[[:blank:]]*(.*)[[:blank:]]*$",
      "\\1 + \\2",
      sub_trms_for_mf
    )
    sub_trms_for_mf <- unique(sub_trms_for_mf)
    sub_formul_for_mf <- as.formula(paste(
      "~", paste(sub_trms_for_mf, collapse = " + ")
    ))
    sub_mf_expected <- model.frame(sub_formul_for_mf, data = sub_data)

    sub_formul_for_mm <- grep("\\|", labels(terms(sub_formul[[1]])),
                              value = TRUE, invert = TRUE)
    if (length(sub_formul_for_mm) == 0) {
      sub_formul_for_mm <- "1"
    }
    sub_formul_for_mm <- as.formula(paste(
      "~", paste(sub_formul_for_mm, collapse = " + ")
    ))
    mm_expected <- model.matrix(sub_formul_for_mm, data = sub_mf_expected)
    attr(mm_expected, "msgScaleX") <- character()

    for (j in seq_extensive_tests) {
      # formula
      if (!with_offs) {
        sub_formul_expected <- flatten_formula(sub_formul[[j]])
      } else {
        sub_formul_expected <- sub_formul[[j]]
      }
      expect_equal(outdmin_totest[[!!j]]@call[["formula"]],
                   sub_formul_expected,
                   info = info_str)

      # resp
      if (!with_offs) {
        offs_expected <- numeric(nobsv)
      } else {
        offs_expected <- offs_tst
      }
      expect_identical(outdmin_totest[[!!j]]@resp$offset,
                       offs_expected,
                       info = info_str)
      if (!is.null(wobs_expected)) {
        if (is.matrix(wobs_expected)) {
          wobs_expected_j <- wobs_expected[, j]
        } else {
          wobs_expected_j <- wobs_expected
        }
        expect_equal(outdmin_totest[[!!j]]@resp$weights,
                     wobs_expected_j,
                     info = info_str)
      } else {
        expect_equal(outdmin_totest[[!!j]]@resp$weights,
                     rep(1, nobsv),
                     info = info_str)
      }
      y_from_resp <- outdmin_totest[[j]]@resp$y
      if (!is.null(augdat_cats)) {
        y_from_resp <- as.factor(y_from_resp)
      }
      if (check_y_from_resp) {
        expect_equal(y_from_resp,
                     eval(str2lang(as.character(sub_formul[[!!j]])[2]),
                          sub_data),
                     info = info_str)
      }

      # frame
      expect_identical(outdmin_totest[[!!j]]@frame,
                       model.frame(outdmin_totest[[!!j]]),
                       info = info_str)
      if (check_y_from_resp) {
        expect_equal(
          outdmin_totest[[!!j]]@frame[[
            grep("y_|ybinprop", names(outdmin_totest[[!!j]]@frame),
                 value = TRUE)
          ]],
          y_from_resp,
          info = info_str
        )
      }
      if (!is.null(wobs_expected)) {
        expect_equal(structure(outdmin_totest[[!!j]]@frame$`(weights)`,
                               ndiscrete = NULL,
                               class = NULL),
                     outdmin_totest[[!!j]]@resp$weights,
                     info = info_str)
      } else {
        expect_null(outdmin_totest[[!!j]]@frame$`(weights)`,
                    info = info_str)
      }
      if (with_offs) {
        expect_equal(outdmin_totest[[!!j]]@frame$`offset(offs_col)`,
                     offs_expected,
                     info = info_str)
      }
      frame_nms <- grep("y_|ybinprop|^\\(weights\\)$|^offset\\(.*\\)$",
                        names(outdmin_totest[[j]]@frame),
                        value = TRUE,
                        invert = TRUE)
      expect_setequal(frame_nms, names(sub_mf_expected))
      expect_equal(
        outdmin_totest[[!!j]]@frame[frame_nms],
        sub_mf_expected[frame_nms],
        info = info_str
      )

      # model.matrix()
      expect_identical(model.matrix(outdmin_totest[[!!j]]), mm_expected,
                       info = info_str)

      # flist
      expect_type(outdmin_totest[[!!j]]@flist, "list")
      expect_length(outdmin_totest[[!!j]]@flist, length(nlvl_ran))
      z_nms <- intersect(names(outdmin_totest[[j]]@flist),
                         names(outdmin_totest[[j]]@frame))
      expect_identical(outdmin_totest[[!!j]]@flist[z_nms],
                       as.list(outdmin_totest[[!!j]]@frame[z_nms]),
                       info = info_str)

      # coef()
      coefs_crr <- coef(outdmin_totest[[j]])
      expect_type(coefs_crr, "list")
      expect_length(coefs_crr, length(nlvl_ran))
      for (zz in seq_len(length(nlvl_ran))) {
        expect_true(is.data.frame(coefs_crr[[zz]]),
                    info = paste(info_str, j, zz, sep = "__"))
        expect_identical(nrow(coefs_crr[[zz]]),
                         unname(nlvl_ran[zz]),
                         info = paste(info_str, j, zz, sep = "__"))
        expect_true(all(sapply(coefs_crr[[zz]], is.numeric)),
                    info = paste(info_str, j, zz, sep = "__"))
      }
    }
  } else if (!has_grp && has_add) {
    for (j in seq_along(outdmin_totest)) {
      expect_s3_class(outdmin_totest[[!!j]], "gam")
    }
    # TODO (GAMs): Add more expectations for GAMs.
  } else if (has_grp && has_add) {
    for (j in seq_along(outdmin_totest)) {
      expect_s3_class(outdmin_totest[[!!j]], "gamm4")
    }
    # TODO (GAMMs): Add more expectations for GAMMs.
  }

  return(invisible(TRUE))
}

# A helper function for testing the structure of a list of fits for the same
# single submodel, with that submodel coming from an augmented-data projection.
#
# @inheritParams outdmin_tester_trad
#
# @return `TRUE` (invisible).
outdmin_tester_aug <- function(
    outdmin_totest,
    nprjdraws_expected,
    sub_formul,
    sub_data,
    sub_fam,
    has_grp = formula_contains_group_terms(sub_formul[[1]]),
    has_add = formula_contains_additive_terms(sub_formul[[1]]),
    wobs_expected = wobs_tst,
    with_offs = FALSE,
    augdat_cats = NULL,
    allow_w_zero = FALSE,
    check_y_from_resp = TRUE,
    seq_extensive_tests = seq_extensive_tests,
    nobsv = nobsv,
    info_str
) {
  sub_formul <- sub_formul[[1]]
  if (has_add) {
    stop("This case should not occur (yet).")
  } else if (!has_grp) {
    if (sub_fam %in% c("cumulative", "cumulative_rstanarm")) {
      for (j in seq_along(outdmin_totest)) {
        expect_s3_class(outdmin_totest[[!!j]], "polr")
      }
      for (j in seq_extensive_tests) {
        # coef()
        coefs_crr <- coef(outdmin_totest[[j]])
        expect_true(is.vector(coefs_crr, "numeric"), info = info_str)
        expect_named(
          coefs_crr,
          grep("Intercept", colnames(model.matrix(sub_formul, data = sub_data)),
               value = TRUE, invert = TRUE),
          info = info_str
        )

        # zeta
        zeta_crr <- outdmin_totest[[j]]$zeta
        expect_true(is.vector(zeta_crr, "numeric"), info = info_str)
        expect_named(
          zeta_crr,
          paste(head(augdat_cats, -1), tail(augdat_cats, -1), sep = "|"),
          info = info_str
        )

        # lev
        expect_identical(outdmin_totest[[!!j]]$lev, augdat_cats,
                         info = info_str)

        # method
        expect_identical(outdmin_totest[[!!j]]$method, "logistic",
                         info = info_str)
      }
    } else if (sub_fam == "categorical") {
      for (j in seq_along(outdmin_totest)) {
        expect_s3_class(outdmin_totest[[!!j]], c("multinom", "nnet"))
      }
      for (j in seq_extensive_tests) {
        # coef()
        coefs_crr <- coef(outdmin_totest[[j]])
        expect_true(is.matrix(coefs_crr), info = info_str)
        expect_true(is.numeric(coefs_crr), info = info_str)
        expect_identical(
          dimnames(coefs_crr),
          list(tail(augdat_cats, -1),
               colnames(model.matrix(sub_formul, data = sub_data))),
          info = info_str
        )

        # lev
        expect_identical(outdmin_totest[[!!j]]$lev, augdat_cats,
                         info = info_str)
      }
    } else {
      stop("Unexpected `sub_fam` value of `", sub_fam, "`. Info: ", info_str)
    }
  } else if (has_grp) {
    grp_trms_for_coef <- extract_terms_response(sub_formul)$group_terms
    grp_trms_for_coef <- sub("[[:blank:]]*\\|.*$", "", grp_trms_for_coef)
    coef_nms <- strsplit(grp_trms_for_coef, "[[:blank:]]*\\+[[:blank:]]*")
    coef_nms <- union("1", coef_nms)
    coef_nms <- sub("^1$", "(Intercept)", unlist(coef_nms))
    if (sub_fam %in% c("cumulative", "cumulative_rstanarm")) {
      for (j in seq_along(outdmin_totest)) {
        expect_s3_class(outdmin_totest[[!!j]], "clmm")
      }
      for (j in seq_extensive_tests) {
        # alpha
        alpha_crr <- outdmin_totest[[j]]$alpha
        expect_true(is.vector(alpha_crr, "numeric"), info = info_str)
        expect_named(
          alpha_crr,
          paste(head(augdat_cats, -1), tail(augdat_cats, -1), sep = "|"),
          info = info_str
        )

        # beta
        coefs_crr <- outdmin_totest[[j]]$beta
        expect_true(is.vector(coefs_crr, "numeric"), info = info_str)
        ### A quick-and-dirty workaround to get rid of group-level terms:
        stopifnot(identical(trms_grp, c("(xco.1 | z.1)")))
        sub_formul_no_grp <- update(sub_formul,
                                    . ~ . - (1 | z.1) - (xco.1 | z.1))
        ###
        coefs_nms_expected <- grep(
          "Intercept",
          colnames(model.matrix(sub_formul_no_grp, data = sub_data)),
          value = TRUE, invert = TRUE
        )
        if (length(coefs_nms_expected)) {
          expect_named(coefs_crr, coefs_nms_expected, info = info_str)
        } else {
          expect_length(coefs_crr, 0)
        }

        # ordinal::ranef()
        ranef_crr <- ordinal::ranef(outdmin_totest[[j]])
        expect_type(ranef_crr, "list")
        expect_named(ranef_crr, "z.1", info = info_str)
        expect_true(is.data.frame(ranef_crr[["z.1"]]), info = info_str)
        expect_named(ranef_crr[["z.1"]], coef_nms, info = info_str)
        expect_true(is.vector(ranef_crr[["z.1"]][["(Intercept)"]], "numeric"),
                    info = info_str)
        expect_length(ranef_crr[["z.1"]][["(Intercept)"]], nlevels(dat$z.1))
        if ("xco.1" %in% coef_nms) {
          expect_true(is.vector(ranef_crr[["z.1"]][["xco.1"]], "numeric"),
                      info = info_str)
          expect_length(ranef_crr[["z.1"]][["xco.1"]], nlevels(dat$z.1))
        }

        # ordinal::VarCorr()
        VarCorr_crr <- ordinal::VarCorr(outdmin_totest[[j]])
        expect_type(VarCorr_crr, "list")
        expect_named(VarCorr_crr, "z.1", info = info_str)
        expect_true(is.matrix(VarCorr_crr[["z.1"]]), info = info_str)
        expect_true(is.numeric(VarCorr_crr[["z.1"]]), info = info_str)
        expect_identical(dimnames(VarCorr_crr[["z.1"]]),
                         replicate(2, coef_nms, simplify = FALSE),
                         info = info_str)
        expect_true(is.vector(attr(VarCorr_crr[["z.1"]], "stddev"), "numeric"),
                    info = info_str)
        expect_named(attr(VarCorr_crr[["z.1"]], "stddev"), coef_nms,
                     info = info_str)
        if ("xco.1" %in% coef_nms) {
          expect_true(is.matrix(attr(VarCorr_crr[["z.1"]], "correlation")),
                      info = info_str)
          expect_true(is.numeric(attr(VarCorr_crr[["z.1"]], "correlation")),
                      info = info_str)
          expect_identical(dimnames(attr(VarCorr_crr[["z.1"]], "correlation")),
                           replicate(2, coef_nms, simplify = FALSE),
                           info = info_str)
        }

        # formula()
        formula_crr <- formula(outdmin_totest[[j]])
        # Unfortunately, there are some minor caveats to take care of when
        # comparing `formula_crr` with `sub_formul`: (i) the intercept (`1`)
        # needs to included explicitly, (ii) group-level terms need to be
        # "flattened" (but flatten_formula() would omit offset terms; adding an
        # argument `incl_offs` to flatten_formula() could solve this, but the
        # internal update() call would then still move offset terms to the end):
        sub_trms <- labels(terms(sub_formul))
        nongrp_trms <- grep("\\|", sub_trms, value = TRUE, invert = TRUE)
        grp_trms <- grep("\\|", sub_trms, value = TRUE)
        grp_trms_flat <- flatten_group_terms(grp_trms)
        offs_trms <- if (with_offs) "offset(offs_col)" else NULL
        expect_identical(
          formula_crr,
          # Add the intercept explicitly and use the same environment:
          as.formula(paste(as.character(sub_formul[[2]]), "~ 1 +",
                           paste(c(nongrp_trms, offs_trms, grp_trms_flat),
                                 collapse = " + ")),
                     env = environment(formula_crr)),
          info = info_str
        )

        # xlevels
        xlevels_crr <- outdmin_totest[[j]]$xlevels
        xca_nms <- grep("^xca\\.", labels(terms(sub_formul)), value = TRUE)
        if (length(xca_nms)) {
          expect_identical(xlevels_crr, lapply(dat[xca_nms], levels),
                           info = info_str)
        } else {
          expect_null(xlevels_crr, info = info_str)
        }
      }
    } else if (sub_fam == "categorical") {
      for (j in seq_along(outdmin_totest)) {
        expect_s3_class(outdmin_totest[[!!j]],
                        c("mmblogit", "mblogit", "mmclogit", "mclogit", "lm"))
      }
      coef_nms <- sub("^\\(Intercept\\)$", "1", coef_nms)
      for (j in seq_extensive_tests) {
        # coefmat
        coefs_crr <- outdmin_totest[[j]]$coefmat
        expect_true(is.matrix(coefs_crr), info = info_str)
        expect_true(is.numeric(coefs_crr), info = info_str)
        ### A quick-and-dirty workaround to get rid of group-level terms:
        stopifnot(identical(trms_grp, c("(xco.1 | z.1)")))
        sub_formul_no_grp <- update(sub_formul,
                                    . ~ . - (1 | z.1) - (xco.1 | z.1))
        ###
        expect_identical(
          dimnames(coefs_crr),
          list("Response categories" = tail(augdat_cats, -1),
               "Predictors" = colnames(model.matrix(sub_formul_no_grp,
                                                    data = sub_data))),
          info = info_str
        )

        # random.effects
        ranef_crr <- outdmin_totest[[j]]$random.effects
        expect_type(ranef_crr, "list")
        expect_length(ranef_crr, 1)
        expect_named(ranef_crr, NULL, info = info_str)
        expect_true(is.matrix(ranef_crr[[1]]), info = info_str)
        expect_true(is.numeric(ranef_crr[[1]]), info = info_str)
        if (packageVersion("Matrix") >= "1.5-0") {
          expect_null(dimnames(ranef_crr[[1]]), info = info_str)
        } else {
          expect_identical(dimnames(ranef_crr[[1]]),
                           replicate(2, NULL, simplify = FALSE),
                           info = info_str)
        }
        expect_identical(dim(ranef_crr[[1]]),
                         c(nthres * length(coef_nms) * nlevels(dat$z.1), 1L),
                         info = info_str)

        # VarCov
        VarCorr_crr <- outdmin_totest[[j]]$VarCov
        expect_type(VarCorr_crr, "list")
        expect_named(VarCorr_crr, "z.1", info = info_str)
        expect_true(is.matrix(VarCorr_crr[["z.1"]]), info = info_str)
        expect_true(is.numeric(VarCorr_crr[["z.1"]]), info = info_str)
        coef_nms_y <- unlist(lapply(coef_nms, function(coef_nm) {
          paste(tail(augdat_cats, -1), coef_nm, sep = "~")
        }))
        expect_identical(dimnames(VarCorr_crr[["z.1"]]),
                         replicate(2, coef_nms_y, simplify = FALSE),
                         info = info_str)

        # D
        D_crr <- outdmin_totest[[j]]$D
        expect_identical(D_crr, contrasts(as.factor(augdat_cats)),
                         info = info_str)

        # random
        random_crr <- outdmin_totest[[j]]$random
        expect_type(random_crr, "list")
        expect_length(random_crr, 1)
        expect_named(random_crr, NULL, info = info_str)
        expect_named(random_crr[[1]], c("formula", "groups"), info = info_str)
        coef_nms_no_icpt <- setdiff(coef_nms, "1")
        if (length(coef_nms_no_icpt)) {
          expect_equal(
            random_crr[[1]]$formula,
            as.formula(paste("~", paste(coef_nms_no_icpt, collapse = "+"))),
            info = info_str
          )
        } else {
          expect_equal(random_crr[[1]]$formula, ~ 1, info = info_str)
        }
        expect_identical(random_crr[[1]]$groups, "z.1", info = info_str)

        # groups
        groups_crr <- outdmin_totest[[j]]$groups
        expect_type(groups_crr, "list")
        expect_named(groups_crr, "z.1", info = info_str)
        expect_identical(levels(groups_crr[["z.1"]]), levels(dat$z.1),
                         info = info_str)

        # xlevels
        xlevels_crr <- outdmin_totest[[j]]$xlevels
        xca_nms <- grep("^xca\\.", labels(terms(sub_formul)), value = TRUE)
        if (length(xca_nms)) {
          expect_identical(xlevels_crr, lapply(dat[xca_nms], levels),
                           info = info_str)
        } else {
          expect_null(xlevels_crr, info = info_str)
        }
      }
    } else {
      stop("Unexpected `sub_fam` value of `", sub_fam, "`. Info: ", info_str)
    }
  }

  return(invisible(TRUE))
}

# A helper function for testing the structure of a list of fits for the same
# single submodel.
#
# @param outdmin_totest The `outdmin` object (a list of fits for a single
#   submodel, with one fit per projected draw) to test.
# @param nprjdraws_expected A single numeric value giving the expected number of
#   projected draws.
# @param sub_formul A list of formulas for the submodel (with one element per
#   projected draw).
# @param sub_data The dataset used for fitting the submodel.
# @param sub_fam A single character string giving the submodel's family.
# @param has_grp A single logical value indicating whether the fits in
#   `outdmin_totest` are expected to be of class `lmerMod` or `glmerMod` (if, at
#   the same time, `has_add` is `FALSE`).
# @param has_add A single logical value indicating whether the fits in
#   `outdmin_totest` are expected to be of class `gam` or `gamm4` (depending on
#   whether the submodel is non-multilevel or multilevel, respectively).
# @param wobs_expected The expected numeric vector of observation weights.
# @param prd_trms_vsel_L1_search If `outdmin_totest` comes from the L1
#   `search_path` of an object of class `vsel`, provide here the predictor
#   ranking. Otherwise, use `NULL`.
# @param with_offs A single logical value indicating whether `outdmin_totest` is
#   expected to include offsets (`TRUE`) or not (`FALSE`).
# @param augdat_cats A character vector of response levels in case of the
#   augmented-data projection. Needs to be `NULL` for the traditional and the
#   latent projection.
# @param allow_w_zero A single logical value indicating whether observation
#   weights are allowed to have a value of zero (`TRUE`) or not (`FALSE`).
# @param check_y_from_resp A single logical value indicating whether to check
#   elements `outdmin_totest[[j]]@resp$y` for GLMMs (`TRUE`) or not (`FALSE`).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
outdmin_tester <- function(
    outdmin_totest,
    nprjdraws_expected,
    sub_formul,
    sub_data,
    sub_fam,
    has_grp = formula_contains_group_terms(sub_formul[[1]]),
    has_add = formula_contains_additive_terms(sub_formul[[1]]),
    wobs_expected = wobs_tst,
    prd_trms_vsel_L1_search = NULL,
    with_offs = FALSE,
    augdat_cats = NULL,
    allow_w_zero = FALSE,
    check_y_from_resp = TRUE,
    info_str
) {
  expect_type(outdmin_totest, "list")
  expect_length(outdmin_totest, nprjdraws_expected)

  seq_extensive_tests <- unique(round(
    seq(1, length(outdmin_totest),
        length.out = min(length(outdmin_totest), nclusters_pred_tst))
  ))

  if (!is.null(augdat_cats)) {
    nobsv <- nobsv * length(augdat_cats)
    expect_length(sub_formul, 1)
    sub_formul <- replicate(nprjdraws_expected, sub_formul[[1]],
                            simplify = FALSE)
  }

  if (!is.null(augdat_cats) && sub_fam %in% fam_nms_aug_long) {
    outdmin_tester_aug(
      outdmin_totest = outdmin_totest,
      nprjdraws_expected = nprjdraws_expected,
      sub_formul = sub_formul,
      sub_data = sub_data,
      sub_fam = sub_fam,
      has_grp = has_grp,
      has_add = has_add,
      wobs_expected = wobs_expected,
      with_offs = with_offs,
      augdat_cats = augdat_cats,
      allow_w_zero = allow_w_zero,
      check_y_from_resp = check_y_from_resp,
      seq_extensive_tests = seq_extensive_tests,
      nobsv = nobsv,
      info_str = info_str
    )
  } else {
    outdmin_tester_trad(
      outdmin_totest = outdmin_totest,
      nprjdraws_expected = nprjdraws_expected,
      sub_formul = sub_formul,
      sub_data = sub_data,
      sub_fam = sub_fam,
      has_grp = has_grp,
      has_add = has_add,
      wobs_expected = wobs_expected,
      prd_trms_vsel_L1_search = prd_trms_vsel_L1_search,
      with_offs = with_offs,
      augdat_cats = augdat_cats,
      allow_w_zero = allow_w_zero,
      check_y_from_resp = check_y_from_resp,
      seq_extensive_tests = seq_extensive_tests,
      nobsv = nobsv,
      info_str = info_str
    )
  }

  return(invisible(TRUE))
}

# A helper function for testing the structure of get_refdist()'s output.
#
# @param refd Output of get_refdist().
# @param nprjdraws_expected A single numeric value giving the expected number of
#   projected draws.
# @param nrefdraws_expected A single numeric value giving the expected number of
#   posterior draws in the reference model.
# @param clust_expected A single logical value giving the expected value for
#   `refd$clust_used`.
# @param aug_expected A single logical value indicating whether get_refdist() is
#   assumed to have been applied for an augmented-data projection.
# @param fam_expected The name of the expected `family` (as a character string).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
refdist_tester <- function(refd,
                           nobsv_expected = nobsv,
                           nprjdraws_expected = nclusters_pred_tst,
                           nrefdraws_expected = nrefdraws,
                           clust_expected = TRUE,
                           aug_expected = FALSE,
                           fam_expected,
                           info_str) {
  # General structure:
  expect_type(refd, "list")
  refd_nms <- c("mu", "mu_offs", "var", "dis", "wdraws_prj", "const_wdraws_prj",
                "nprjdraws", "cl", "wdraws_orig", "clust_used")
  expect_named(refd, refd_nms, info = info_str)

  # mu
  expect_equal(dim(refd$mu), c(nobsv_expected, nprjdraws_expected),
               info = info_str)
  expect_true(is.numeric(refd$mu), info = info_str)
  if (aug_expected) {
    expect_s3_class(refd$mu, "augmat")
  }

  # mu_offs
  expect_equal(dim(refd$mu_offs), c(nobsv_expected, nprjdraws_expected),
               info = info_str)
  expect_true(is.numeric(refd$mu_offs), info = info_str)
  if (aug_expected) {
    expect_s3_class(refd$mu_offs, "augmat")
  }

  # var
  expect_equal(dim(refd$var), c(nobsv_expected, nprjdraws_expected),
               info = info_str)
  if (fam_expected == "gaussian") {
    expect_true(is.numeric(refd$var), info = info_str)
  } else {
    expect_true(all(is.na(refd$var)), info = info_str)
  }
  if (aug_expected) {
    expect_s3_class(refd$var, "augmat")
  }

  # dis
  if (fam_expected == "gaussian") {
    expect_true(is.vector(refd$dis, "numeric"), info = info_str)
  } else {
    expect_true(all(is.na(refd$dis)), info = info_str)
  }
  expect_length(refd$dis, nprjdraws_expected)

  # wdraws_prj
  expect_true(is.vector(refd$wdraws_prj, "numeric"), info = info_str)
  expect_length(refd$wdraws_prj, nprjdraws_expected)

  # const_wdraws_prj
  expect_identical(refd$const_wdraws_prj, length(unique(refd$wdraws_prj)) == 1,
                   info = info_str)

  # nprjdraws
  expect_equal(refd$nprjdraws, nprjdraws_expected, info = info_str)

  # cl
  expect_true(is.vector(refd$cl, "numeric"), info = info_str)
  expect_length(refd$cl, nrefdraws_expected)

  # wdraws_orig
  expect_identical(refd$wdraws_orig, rep(1, nrefdraws_expected),
                   info = info_str)

  # clust_used
  expect_identical(refd$clust_used, clust_expected, info = info_str)

  return(invisible(TRUE))
}

# A helper function for testing the structure of an expected `projection`
# object.
#
# @param p An object of class `projection` (at least expected so).
# @param prd_trms_expected Either a single numeric value giving the expected
#   length of the predictor ranking (not counting the intercept, even for the
#   intercept-only model), a character vector giving the expected predictor
#   ranking, or `NULL` for not testing the predictor ranking at all.
# @param nprjdraws_expected A single numeric value giving the expected number of
#   projected draws.
# @param with_clusters A single logical value indicating whether clustering was
#   used (`TRUE`) or not (`FALSE`).
# @param seed_expected The seed which was used for clustering the posterior
#   draws of the reference model.
# @param prjdraw_weights_expected The expected weights for the projected draws
#   or `NULL` for not testing these weights at all.
# @param from_vsel_L1_search A single logical value indicating whether `p` uses
#   the L1 `search_path` of an object of class `vsel` for extracting the
#   subfit(s) (`TRUE`) or not (`FALSE`).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
projection_tester <- function(p,
                              refmod_expected,
                              prd_trms_expected,
                              nprjdraws_expected,
                              with_clusters,
                              seed_expected = seed_tst,
                              prjdraw_weights_expected = NULL,
                              from_vsel_L1_search = FALSE,
                              info_str = "") {
  expect_s3_class(p, "projection")
  expect_type(p, "list")
  # Check the names using `ignore.order = FALSE` because an incorrect
  # order would mean that the documentation of project()'s return value
  # would have to be updated:
  expect_named(
    p,
    c("dis", "ce", "wdraws_prj", "const_wdraws_prj", "predictor_terms",
      "outdmin", "cl_ref", "wdraws_ref", "clust_used", "nprjdraws", "refmodel"),
    info = info_str
  )

  # refmodel
  # Note: Extensive tests for `refmodel`s and `datafit`s may be run via
  # refmodel_tester().
  expect_identical(p$refmodel, refmod_expected, info = info_str)

  # predictor_terms
  if (is.numeric(prd_trms_expected)) {
    expect_length(p$predictor_terms, prd_trms_expected)
    # Same check, but using count_terms_chosen():
    expect_equal(count_terms_chosen(p$predictor_terms), prd_trms_expected + 1,
                 info = info_str)
  } else if (is.character(prd_trms_expected)) {
    expect_identical(p$predictor_terms, prd_trms_expected, info = info_str)
  }

  # outdmin
  sub_trms_crr <- p$predictor_terms
  if (length(sub_trms_crr) == 0) {
    sub_trms_crr <- "1"
  }
  if (!from_vsel_L1_search) {
    y_nm <- as.character(p$refmodel$formula)[2]
    prd_trms_vsel_L1_search_crr <- NULL
  } else {
    y_nm <- ""
    prd_trms_vsel_L1_search_crr <- p$predictor_terms
  }
  y_nms <- y_nm
  # For checking for the augmented-data projection case, we use the "unsafer"
  # `p$refmodel$family$for_augdat` here instead of an extra argument such as
  # `augdat_expected` because such an argument already exists in
  # extfam_tester():
  if (!p$refmodel$family$for_augdat) {
    y_nms <- paste0(".", y_nms)
  }
  # A preliminary check for `nprjdraws_expected` (doesn't work for `datafit`s
  # and, because of issue #131, for submodels which are GAMMs):
  sub_formul_crr_rhs <- as.formula(paste(
    "~", paste(sub_trms_crr, collapse = " + ")
  ))
  if (all(grepl("\\+", sub_trms_crr))) {
    # Avoid duplicated terms in the "empty_size" `search_terms` setting:
    sub_formul_crr_rhs <- update(sub_formul_crr_rhs, . ~ .)
  }
  if (!inherits(p$refmodel, "datafit") &&
      !(formula_contains_additive_terms(sub_formul_crr_rhs) &&
        formula_contains_group_terms(sub_formul_crr_rhs))) {
    # Number of projected draws in as.matrix.projection() (note that more
    # extensive tests for as.matrix.projection() may be found in
    # "test_as_matrix.R"):
    expect_identical(
      nrow(as.matrix(p, allow_nonconst_wdraws_prj = TRUE)), nprjdraws_expected,
      info = info_str
    )
  }
  if (!p$refmodel$family$for_augdat && nprjdraws_expected > 1) {
    y_nms <- paste0(y_nms, ".", seq_len(nprjdraws_expected))
  }
  sub_formul_crr <- lapply(y_nms, function(y_nm_i) {
    fml_tmp <- as.formula(paste(
      y_nm_i, "~", paste(sub_trms_crr, collapse = " + ")
    ))
    if (all(grepl("\\+", sub_trms_crr))) {
      # Avoid duplicated terms in the "empty_size" `search_terms` setting:
      fml_tmp <- update(fml_tmp, . ~ .)
    }
    return(fml_tmp)
  })
  sub_data_crr <- p$refmodel$fetch_data()
  if (with_clusters) {
    if (exists(".Random.seed", envir = .GlobalEnv)) {
      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
    }
    set.seed(seed_expected)
    clust_ref <- get_refdist(p$refmodel, nclusters = nprjdraws_expected)
  } else {
    clust_ref <- get_refdist(p$refmodel, ndraws = nprjdraws_expected)
  }
  if (p$refmodel$family$for_augdat) {
    # Create the augmented dataset:
    y_unqs <- p$refmodel$family$cats
    sub_data_crr <- do.call(rbind, lapply(y_unqs, function(y_unq) {
      sub_data_crr_j <- sub_data_crr
      sub_data_crr_j[[y_nms]] <- y_unq
      return(sub_data_crr_j)
    }))
    sub_data_crr[[y_nms]] <- factor(sub_data_crr[[y_nms]], levels = y_unqs)
  } else {
    for (i in seq_len(nprjdraws_expected)) {
      sub_data_crr[[y_nms[i]]] <- clust_ref$mu[, i]
    }
  }
  if (p$refmodel$family$for_augdat) {
    wobs_expected_crr <- unclass(clust_ref$mu)
  } else {
    wobs_expected_crr <- p$refmodel$wobs
  }
  if (p$refmodel$family$for_augdat) {
    augdat_cats_crr <- p$refmodel$family$cats
  } else {
    augdat_cats_crr <- NULL
  }
  outdmin_tester(p$outdmin,
                 nprjdraws_expected = nprjdraws_expected,
                 sub_formul = sub_formul_crr,
                 sub_data = sub_data_crr,
                 sub_fam = p$refmodel$family$family,
                 wobs_expected = wobs_expected_crr,
                 prd_trms_vsel_L1_search = prd_trms_vsel_L1_search_crr,
                 augdat_cats = augdat_cats_crr,
                 info_str = info_str)

  # dis
  expect_length(p$dis, nprjdraws_expected)

  # ce
  expect_type(p$ce, "double")
  expect_length(p$ce, 1)
  expect_true(!is.na(p$ce), info = info_str)

  # wdraws_prj
  expect_length(p$wdraws_prj, nprjdraws_expected)
  if (!is.null(prjdraw_weights_expected)) {
    expect_identical(p$wdraws_prj, prjdraw_weights_expected, info = info_str)
  }
  if (nprjdraws_expected == 1) {
    expect_identical(p$wdraws_prj, 1, info = info_str)
  }

  # cl_ref
  expect_true(is.vector(p$cl_ref, "numeric"), info = info_str)
  expect_length(p$cl_ref, length(p$refmodel$wdraws_ref))

  # wdraws_ref
  expect_identical(p$wdraws_ref, rep(1, length(p$refmodel$wdraws_ref)),
                   info = info_str)

  # clust_used
  expect_identical(p$clust_used, with_clusters, info = info_str)

  # nprjdraws
  expect_equal(p$nprjdraws, nprjdraws_expected, info = info_str)

  # const_wdraws_prj
  expect_identical(p$const_wdraws_prj, length(unique(p$wdraws_prj)) == 1,
                   info = info_str)

  return(invisible(TRUE))
}

# A helper function for testing the structure of an expected `proj_list`
# object.
#
# @param p An object of (informal) class `proj_list` (at least expected so).
# @param len_expected The expected length of `p`.
# @param is_seq A single logical value indicating whether `p` is expected to be
#   sequential (i.e., the number of predictor terms increases by 1 from one
#   element of `p` to the next).
# @param extra_tol A single numeric value giving the relative tolerance when
#   checking the monotonicity of the KL divergences. Because this is a
#   *relative* tolerance, 1 is the neutral value.
# @param info_str A single character string giving information to be printed in
#   case of failure.
# @param ... Arguments passed to projection_tester(), apart from
#   projection_tester()'s arguments `p`, `prd_trms_expected`, and `info_str`.
#
# @return `TRUE` (invisible).
proj_list_tester <- function(p,
                             len_expected = nterms_max_tst + 1L,
                             is_seq = TRUE,
                             extra_tol = 1.1,
                             info_str = "",
                             ...) {
  expect_type(p, "list")
  expect_length(p, len_expected)
  expect_true(is_proj_list(p), info = info_str)

  for (j in seq_along(p)) {
    if (is_seq) {
      # The j-th element should have j predictor terms (not counting the
      # intercept, even for the intercept-only model):
      prd_trms_expected_crr <- j - 1
    } else {
      prd_trms_expected_crr <- NULL
    }
    projection_tester(p[[j]],
                      prd_trms_expected = prd_trms_expected_crr,
                      info_str = paste(info_str, j, sep = "__"),
                      ...)
  }
  if (is_seq) {
    # For a sequential `proj_list` object and training data, `ce` should be
    # non-increasing for increasing model size:
    ceseq <- sapply(p, function(x) sum(x$ce))
    expect_true(all(ifelse(sign(head(ceseq, -1)) == 1,
                           tail(ceseq, -1) <= extra_tol * head(ceseq, -1),
                           tail(ceseq, -1) <= 1 / extra_tol * head(ceseq, -1))),
                info = info_str)
  }
  return(invisible(TRUE))
}

# A helper function for testing the structure of an object returned by
# proj_linpred().
#
# @param pl An object resulting from a call to proj_linpred().
# @param len_expected The number of `projection` objects used for `pl`.
# @param nprjdraws_expected The expected number of projected draws in `pl`.
# @param wdraws_prj_expected The expected attribute `wdraws_prj` (containing the
#   weights of the projected draws) of the `pred` and `lpd` elements.
# @param nobsv_expected The expected number of observations in `pl`.
# @param lpd_null_expected A single logical value indicating whether output
#   element `lpd` is expected to be `NULL` (`TRUE`) or not (`FALSE`).
# @param ncats_nlats_expected A list of length `len_expected`. If the
#   augmented-data projection is expected to have been applied in case of
#   element `j`, then element `j` has to be a single integer value giving the
#   number of response categories or latent response categories (depending on
#   whether the linear predictor was transformed to response scale or not).
#   Else, element `j` has to be `integer()`.
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
pl_tester <- function(pl,
                      len_expected = 1,
                      nprjdraws_expected = nclusters_pred_tst,
                      wdraws_prj_expected = NULL,
                      nobsv_expected = nobsv,
                      lpd_null_expected = FALSE,
                      ncats_nlats_expected = replicate(len_expected,
                                                       integer(),
                                                       simplify = FALSE),
                      info_str) {
  if (len_expected == 1) {
    pl <- list(pl)
  } else {
    expect_type(pl, "list")
    expect_length(pl, len_expected)
  }
  for (j in seq_along(pl)) {
    expect_named(pl[[!!j]], c("pred", "lpd"), info = info_str)
    expect_identical(
      dim(pl[[!!j]]$pred),
      c(nprjdraws_expected, nobsv_expected, ncats_nlats_expected[[!!j]]),
      info = info_str
    )
    expect_identical(attr(pl[[!!j]]$pred, "wdraws_prj"), wdraws_prj_expected,
                     info = info_str)
    if (!lpd_null_expected) {
      expect_identical(dim(pl[[!!j]]$lpd),
                       c(nprjdraws_expected, nobsv_expected),
                       info = info_str)
      expect_identical(attr(pl[[!!j]]$lpd, "wdraws_prj"), wdraws_prj_expected,
                       info = info_str)
    } else {
      expect_null(pl[[!!j]]$lpd, info = info_str)
    }
  }
  return(invisible(TRUE))
}

# A helper function for testing the structure of an object returned by
# proj_predict().
#
# @param pp An object resulting from a call to proj_predict().
# @param len_expected The number of `projection` objects used for `pp`.
# @param nprjdraws_out_expected The expected number of projected draws in `pp`.
#   In contrast to argument `nprjdraws_expected` of pl_tester(), this also needs
#   to take proj_predict()'s argument `nresample_clusters` into account.
# @param nobsv_expected The expected number of observations in `pp`.
# @param lpd_null_expected A single logical value indicating whether output
#   element `lpd` is expected to be `NULL` (`TRUE`) or not (`FALSE`).
# @param cats_expected A list of length `len_expected`. If the
#   augmented-data projection is expected to have been applied in case of
#   element `j`, then element `j` has to be a character vector giving the
#   response categories. Else, element `j` has to be `NULL`.
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
pp_tester <- function(pp,
                      len_expected = 1,
                      nprjdraws_out_expected = nresample_clusters_default,
                      nobsv_expected = nobsv,
                      cats_expected = replicate(len_expected,
                                                NULL,
                                                simplify = FALSE),
                      info_str) {
  if (len_expected == 1) {
    pp <- list(pp)
  } else {
    expect_type(pp, "list")
    expect_length(pp, len_expected)
  }
  for (j in seq_along(pp)) {
    expect_identical(dim(pp[[!!j]]),
                     c(nprjdraws_out_expected, nobsv_expected),
                     info = info_str)
    expect_identical(attr(pp[[!!j]], "cats"), cats_expected[[j]],
                     info = info_str)
  }
  return(invisible(TRUE))
}

# A helper function for testing the structure of an expected `vsel` object.
#
# @param vs An object of class `vsel` (at least expected so).
# @param with_cv A single logical value indicating whether `vs` was created by
#   cv_varsel() (`TRUE`) or not (`FALSE`).
# @param refmod_expected The expected `vs$refmodel` object.
# @param ywtest_expected If `vs` was created with a non-`NULL` argument `d_test`
#   (which is only possible for varsel()), then this needs to be the expected
#   `vs$y_wobs_test` object. Otherwise, this needs to be `NULL`.
# @param prd_trms_len_expected A single numeric value giving the expected length
#   of the predictor ranking (not counting the intercept, even for the
#   intercept-only model).
# @param method_expected The expected `vs$method` object.
# @param cv_method_expected The expected `vs$cv_method` object.
# @param valsearch_expected The expected `vs$validate_search` object.
# @param refit_prj_expected A single logical value indicating whether argument
#   `refit_prj` was set to `TRUE` when calling varsel() or cv_varsel() for
#   creating `vs` (`TRUE`) or not (`FALSE`).
# @param cl_search_expected The expected `vs$clust_used_search` object.
# @param cl_eval_expected The expected `vs$clust_used_eval` object.
# @param nprjdraws_search_expected The expected `vs$nprjdraws_search` object.
# @param nprjdraws_eval_expected The expected `vs$nprjdraws_eval` object.
# @param seed_expected The seed which was used for clustering the posterior
#   draws of the reference model.
# @param nloo_expected Only relevant if `with_cv` is `TRUE`. The value which was
#   used for argument `nloo` of cv_varsel().
# @param search_trms_empty_size A single logical value indicating whether
#   `search_terms` was constructed in a way that causes a model size to be
#   without candidate models.
# @param extra_tol A single numeric value giving the relative tolerance when
#   checking the monotonicity of the KL divergences. Because this is a
#   *relative* tolerance, 1 is the neutral value.
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
vsel_tester <- function(
    vs,
    with_cv = FALSE,
    from_datafit = FALSE,
    refmod_expected,
    ywtest_expected = NULL,
    cvfits_expected = refmod_expected$cvfits,
    prd_trms_len_expected,
    method_expected,
    cv_method_expected = NULL,
    valsearch_expected = NULL,
    refit_prj_expected = !from_datafit,
    cl_search_expected = !from_datafit,
    cl_eval_expected = !from_datafit,
    nprjdraws_search_expected = if (from_datafit || method_expected == "L1") {
      1L
    } else {
      nclusters_tst
    },
    nprjdraws_eval_expected = if (from_datafit || (!refit_prj_expected &&
                                                   method_expected == "L1")) {
      1L
    } else if (!refit_prj_expected) {
      nclusters_tst
    } else {
      nclusters_pred_tst
    },
    seed_expected = seed_tst,
    nloo_expected = if (with_cv && !identical(cv_method_expected, "kfold")) {
      refmod_expected$nobs
    } else {
      NULL
    },
    K_expected = NULL,
    penalty_expected = NULL,
    search_terms_expected = NULL,
    search_trms_empty_size = FALSE,
    search_control_expected = NULL,
    extra_tol = 1.1,
    info_str = ""
) {
  # Preparations:
  if (with_cv) {
    if (is.null(cv_method_expected)) {
      cv_method_expected <- "LOO"
    }
    if (is.null(valsearch_expected)) {
      valsearch_expected <- TRUE
    }
  }
  if (method_expected == "L1") {
    cl_search_expected <- !from_datafit
  }
  if (with_cv && identical(cv_method_expected, "LOO")) {
    if (is.null(nloo_expected) || nloo_expected > nobsv) {
      nloo_expected <- nobsv
    }
  }
  if (search_trms_empty_size) {
    # This is the "empty_size" setting, so we have to subtract the skipped model
    # size (see issue #307):
    prd_trms_len_expected <- prd_trms_len_expected - 1L
  }

  # Test the general structure of the object:
  expect_s3_class(vs, "vsel")
  expect_type(vs, "list")
  expect_named(vs, vsel_nms, info = info_str)

  # refmodel
  expect_identical(vs$refmodel, refmod_expected, info = info_str)

  # nobs_train
  expect_identical(vs$nobs_train, vs$refmodel$nobs, info = info_str)

  # search_path
  expect_type(vs$search_path, "list")
  expect_named(vs$search_path, c("predictor_ranking", "outdmins", "p_sel"),
               info = info_str)
  expect_identical(vs$search_path$predictor_ranking, vs$predictor_ranking,
                   info = info_str)
  expect_type(vs$search_path$outdmins, "list")
  expect_length(vs$search_path$outdmins, prd_trms_len_expected + 1)
  from_vsel_L1_search <- method_expected == "L1"
  if (exists(".Random.seed", envir = .GlobalEnv)) {
    rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
    on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
  }
  set.seed(seed_expected)
  if (cl_search_expected) {
    clust_ref <- get_refdist(vs$refmodel,
                             nclusters = nprjdraws_search_expected)
  } else {
    clust_ref <- get_refdist(vs$refmodel,
                             ndraws = nprjdraws_search_expected)
  }
  if (!from_vsel_L1_search) {
    y_nm <- as.character(vs$refmodel$formula)[2]
    prd_trms_vsel_L1_search_crr <- NULL
  } else {
    y_nm <- ""
    prd_trms_vsel_L1_search_crr <- vs$predictor_ranking
  }
  y_nms <- y_nm
  # For checking for the augmented-data projection case, we use the "unsafer"
  # `vs$refmodel$family$for_augdat` here instead of an extra argument such as
  # `augdat_expected` because such an argument already exists in
  # extfam_tester():
  if (!vs$refmodel$family$for_augdat) {
    y_nms <- paste0(".", y_nms)
  }
  if (!vs$refmodel$family$for_augdat && nprjdraws_search_expected > 1) {
    y_nms <- paste0(y_nms, ".", seq_len(nprjdraws_search_expected))
  }
  sub_data_crr <- vs$refmodel$fetch_data()
  if (vs$refmodel$family$for_augdat) {
    # Create the augmented dataset:
    y_unqs <- vs$refmodel$family$cats
    sub_data_crr <- do.call(rbind, lapply(y_unqs, function(y_unq) {
      sub_data_crr_j <- sub_data_crr
      sub_data_crr_j[[y_nms]] <- y_unq
      return(sub_data_crr_j)
    }))
    sub_data_crr[[y_nms]] <- factor(sub_data_crr[[y_nms]], levels = y_unqs)
  } else {
    for (i in seq_len(nprjdraws_search_expected)) {
      sub_data_crr[[y_nms[i]]] <- clust_ref$mu[, i]
    }
  }
  if (vs$refmodel$family$for_augdat) {
    wobs_expected_crr <- unclass(clust_ref$mu)
  } else {
    wobs_expected_crr <- vs$refmodel$wobs
  }
  prd_trms_for_sub <- c("1", vs$predictor_ranking)
  for (i in seq_along(vs$search_path$outdmins)) {
    sub_trms_crr <- head(prd_trms_for_sub, i)
    if (length(sub_trms_crr) > 1) {
      sub_trms_crr <- setdiff(sub_trms_crr, "1")
    }
    sub_formul_crr <- lapply(y_nms, function(y_nm_i) {
      fml_tmp <- as.formula(paste(
        y_nm_i, "~", paste(sub_trms_crr, collapse = " + ")
      ))
      if (all(grepl("\\+", sub_trms_crr))) {
        # Avoid duplicated terms in the "empty_size" `search_terms` setting:
        fml_tmp <- update(fml_tmp, . ~ .)
      }
      return(fml_tmp)
    })
    if (vs$refmodel$family$for_augdat) {
      augdat_cats_crr <- vs$refmodel$family$cats
    } else {
      augdat_cats_crr <- NULL
    }
    outdmin_tester(
      vs$search_path$outdmins[[i]],
      nprjdraws_expected = nprjdraws_search_expected,
      sub_formul = sub_formul_crr,
      sub_data = sub_data_crr,
      sub_fam = vs$refmodel$family$family,
      wobs_expected = wobs_expected_crr,
      prd_trms_vsel_L1_search = prd_trms_vsel_L1_search_crr,
      augdat_cats = augdat_cats_crr,
      info_str = paste(info_str, i, sep = "__")
    )
  }
  if (!vs$refmodel$family$for_augdat) {
    ncats <- 1L
  } else {
    ncats <- length(vs$refmodel$family$cats)
  }
  nobsv_aug <- nobsv * ncats
  refdist_tester(vs$search_path$p_sel,
                 nobsv_expected = nobsv_aug,
                 nprjdraws_expected = nprjdraws_search_expected,
                 nrefdraws_expected = ncol(vs$refmodel$mu),
                 clust_expected = cl_search_expected,
                 aug_expected = vs$refmodel$family$for_augdat,
                 fam_expected = vs$refmodel$family$family,
                 info_str = info_str)

  # predictor_ranking
  expect_type(vs$predictor_ranking, "character")
  expect_length(vs$predictor_ranking, prd_trms_len_expected)
  rk_fulldata <- vs$predictor_ranking
  for (rk_fulldata_plus in grep("\\+", rk_fulldata, value = TRUE)) {
    rk_fulldata <- setdiff(rk_fulldata, rk_fulldata_plus)
    rk_fulldata <- c(rk_fulldata,
                     labels(terms(as.formula(paste(". ~", rk_fulldata_plus)))))
  }
  expect_true(all(rk_fulldata %in% trms_universe_split), info = info_str)

  # predictor_ranking_cv
  if (with_cv && isTRUE(vs$validate_search)) {
    expect_true(is.matrix(vs$predictor_ranking_cv), info = info_str)
    expect_type(vs$predictor_ranking_cv, "character")
    if (identical(cv_method_expected, "kfold")) {
      n_folds <- K_tst
    } else {
      n_folds <- nobsv
    }
    expect_identical(dim(vs$predictor_ranking_cv),
                     c(n_folds, prd_trms_len_expected),
                     info = info_str)
    # We need the addition of `NA_character_` because of subsampled PSIS-LOO CV:
    rk_cv <- unique(as.vector(vs$predictor_ranking_cv))
    for (rk_cv_plus in grep("\\+", rk_cv, value = TRUE)) {
      rk_cv <- setdiff(rk_cv, rk_cv_plus)
      rk_cv <- c(rk_cv, labels(terms(as.formula(paste(". ~", rk_cv_plus)))))
    }
    expect_true(
      all(rk_cv %in%
            c(trms_universe_split,
              sub("^s\\((.*)\\)$", "\\1",
                  grep("^s\\(.*\\)$", trms_universe_split, value = TRUE)),
              NA_character_)),
      info = info_str
    )
  } else {
    expect_null(vs$predictor_ranking_cv, info = info_str)
  }

  # ce
  if (with_cv && (valsearch_expected || cv_method_expected == "kfold")) {
    expect_identical(vs$ce, rep(NA_real_, prd_trms_len_expected + 1))
  } else {
    expect_type(vs$ce, "double")
    expect_length(vs$ce, prd_trms_len_expected + 1)
    # Expected to be non-increasing for increasing model size:
    expect_true(all(ifelse(sign(head(vs$ce, -1)) == 1,
                           tail(vs$ce, -1) <= extra_tol * head(vs$ce, -1),
                           tail(vs$ce, -1) <= 1 / extra_tol * head(vs$ce, -1))),
                info = info_str)
  }

  # type_test
  type_test_expected <- cv_method_expected
  if (length(type_test_expected) == 0) {
    if (is.null(ywtest_expected)) {
      type_test_expected <- "train"
    } else {
      type_test_expected <- "test_hold-out"
    }
  }
  expect_identical(vs$type_test, type_test_expected, info = info_str)

  # y_wobs_test
  if (is.null(ywtest_expected)) {
    expect_true(is.data.frame(vs$y_wobs_test))
    expect_named(vs$y_wobs_test, nms_y_wobs_test(), info = info_str)
    expect_identical(vs$y_wobs_test$wobs, vs$refmodel$wobs, info = info_str)
    if (vs$refmodel$family$for_latent && with_cv) {
      if (identical(cv_method_expected, "kfold")) {
        expect_true(all(is.na(vs$y_wobs_test$y)), info = info_str)
      } else if (inherits(vs$refmodel$fit, "stanreg") &&
                 formula_contains_additive_terms(vs$refmodel$formula) &&
                 formula_contains_group_terms(vs$refmodel$formula)) {
        # Due to rstanarm issue stan-dev/rstanarm#604, we need to be consistent
        # with loo_varsel() in case of an rstanarm GAMM reference model:
        mu_offs_oscale_tst <- vs$refmodel$family$latent_ilink(
          t(vs$refmodel$mu_offs), cl_ref = seq_along(vs$refmodel$wdraws_ref),
          wdraws_ref = vs$refmodel$wdraws_ref
        )
        ll_forPSIS <- vs$refmodel$family$latent_ll_oscale(
          mu_offs_oscale_tst, dis = vs$refmodel$dis,
          y_oscale = vs$refmodel$y_oscale, wobs = vs$refmodel$wobs,
          cens = eval_el2_not_null(attr(vs$refmodel$family$latent_ll_oscale,
                                        "cens_var"),
                                   vs$refmodel$fetch_data()),
          cl_ref = seq_along(vs$refmodel$wdraws_ref),
          wdraws_ref = vs$refmodel$wdraws_ref
        )
        psisloo_tst <- suppressWarnings(
          loo::psis(-ll_forPSIS, cores = 1, r_eff = NA)
        )
        y_lat_loo <- loo::E_loo(
          t(vs$refmodel$ref_predfun(
            vs$refmodel$fit, excl_offs = FALSE,
            mlvl_allrandom = getOption("projpred.mlvl_proj_ref_new", FALSE)
          )),
          psis_object = psisloo_tst,
          log_ratios = -ll_forPSIS
        )
        expect_equal(vs$y_wobs_test$y, y_lat_loo$value, info = info_str)
      } else {
        ll_forPSIS <- rstantools::log_lik(vs$refmodel$fit)
        lwdraws_ref <- weights(suppressWarnings(
          loo::psis(-ll_forPSIS, cores = 1, r_eff = NA)
        ))
        y_lat_loo <- colSums(
          t(vs$refmodel$ref_predfun(
            vs$refmodel$fit, excl_offs = FALSE,
            mlvl_allrandom = getOption("projpred.mlvl_proj_ref_new", FALSE)
          )) * exp(lwdraws_ref)
        )
        expect_equal(vs$y_wobs_test$y, y_lat_loo, info = info_str)
      }
    } else {
      expect_identical(vs$y_wobs_test$y, vs$refmodel$y, info = info_str)
    }
    expect_identical(vs$y_wobs_test$y_oscale, vs$refmodel$y_oscale,
                     info = info_str)
  } else {
    expect_identical(vs$y_wobs_test, ywtest_expected, info = info_str)
  }

  # nobs_test
  expect_identical(vs$nobs_test, nrow(vs$y_wobs_test), info = info_str)

  # summaries
  smmrs_tester(vs$summaries,
               from_datafit = from_datafit,
               with_cv = with_cv,
               valsearch_expected = valsearch_expected,
               cv_method_expected = cv_method_expected,
               nloo_expected = nloo_expected,
               ncats = ncats,
               ywtest_expected = ywtest_expected,
               ref_fam = vs$refmodel$family,
               prd_trms_len_expected = prd_trms_len_expected,
               info_str = info_str)

  # summaries_fast
  if (isTRUE(vs$nloo < vs$refmodel$nobs)) {
    smmrs_tester(vs$summaries_fast,
                 from_datafit = from_datafit,
                 with_cv = with_cv,
                 valsearch_expected = valsearch_expected,
                 cv_method_expected = cv_method_expected,
                 nloo_expected = nobsv,
                 ncats = ncats,
                 ywtest_expected = ywtest_expected,
                 ref_fam = vs$refmodel$family,
                 prd_trms_len_expected = prd_trms_len_expected,
                 info_str = info_str)
    expect_identical(vs$summaries_fast$ref,
                     vs$summaries$ref,
                     info = info_str)
  } else {
    expect_null(vs$summaries_fast, info = info_str)
  }

  # nterms_all
  expect_identical(vs$nterms_all,
                   count_terms_in_formula(vs$refmodel$formula) - 1L,
                   info = info_str)

  # nterms_max
  nterms_max_expected <- prd_trms_len_expected
  if (search_trms_empty_size) {
    nterms_max_expected <- nterms_max_expected + 1
  }
  expect_equal(vs$nterms_max, nterms_max_expected, info = info_str)

  # method
  expect_identical(vs$method, method_expected, info = info_str)

  # cv_method
  expect_identical(vs$cv_method, cv_method_expected, info = info_str)

  # nloo
  expect_identical(vs$nloo, nloo_expected, info = info_str)

  # K
  if (!is.null(K_expected)) {
    expect_identical(vs$K, K_expected, info = info_str)
  } else if (identical(cv_method_expected, "kfold")) {
    expect_identical(vs$K, K_tst, info = info_str)
  } else if (with_cv) {
    expect_identical(vs$K, if (from_datafit) 10 else 5, info = info_str)
  } else {
    expect_null(vs$K, info = info_str)
  }

  # validate_search
  expect_identical(vs$validate_search, valsearch_expected, info = info_str)

  # cvfits
  expect_identical(vs$cvfits, cvfits_expected, info = info_str)

  # args_search
  sce <- search_control_expected[!sapply(search_control_expected, is.null)]
  if (!length(sce)) {
    sce <- if (method_expected == "forward") list() else NULL
  }
  expect_equal(
    vs$args_search,
    list(
      method = method_expected,
      ndraws = NULL,
      nclusters = if (from_datafit && method_expected == "forward") {
        20
      } else if (cl_search_expected || from_datafit) {
        nprjdraws_search_expected
      } else {
        NULL
      },
      nterms_max = vs$nterms_max,
      search_control = sce,
      penalty = penalty_expected,
      search_terms = if (is.null(search_terms_expected)) {
        NULL
      } else {
        union("1", search_terms_expected)
      }
    ),
    info = info_str
  )

  # clust_used_search
  expect_equal(vs$clust_used_search, cl_search_expected, info = info_str)

  # clust_used_eval
  expect_equal(vs$clust_used_eval, cl_eval_expected, info = info_str)

  # nprjdraws_search
  expect_equal(vs$nprjdraws_search, nprjdraws_search_expected, info = info_str)

  # nprjdraws_eval
  expect_equal(vs$nprjdraws_eval, nprjdraws_eval_expected, info = info_str)

  # refit_prj
  expect_equal(vs$refit_prj, refit_prj_expected, info = info_str)

  # projpred_version
  expect_true(is.package_version(vs$projpred_version), info = info_str)

  return(invisible(TRUE))
}

# A helper function for testing the structure of element `summaries` or
# `summaries_fast` of a `vsel` object.
#
# @param smmrs Element `summaries` or `summaries_fast` of a `vsel` object.
# @param from_datafit A single logical value indicating whether an object of
#   class `datafit` was used for creating the `vsel` object (`TRUE`) or not
#   (`FALSE`).
# @param with_cv A single logical value indicating whether the `vsel` object was
#   created by cv_varsel() (`TRUE`) or not (`FALSE`).
# @param valsearch_expected The expected element `validate_search` of the `vsel`
#   object.
# @param cv_method_expected Either `character()` for the no-CV case or a single
#   character string giving the CV method (see argument `cv_method` of
#   cv_varsel()).
# @param nloo_expected Only relevant if `with_cv` is `TRUE`. The value which was
#   used for argument `nloo` of cv_varsel().
# @param ncats The expected number of categories (for augmented-data and
#   possibly latent projection).
# @param ywtest_expected If `vs` was created with a non-`NULL` argument `d_test`
#   (which is only possible for varsel()), then this needs to be the expected
#   `vs$y_wobs_test` object. Otherwise, this needs to be `NULL`.
# @param ref_fam Element `family` of element `refmodel` of the `vsel` object.
# @param prd_trms_len_expected A single numeric value giving the expected length
#   of the predictor ranking (not counting the intercept, even for the
#   intercept-only model).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
smmrs_tester <- function(smmrs,
                         from_datafit,
                         with_cv,
                         valsearch_expected,
                         cv_method_expected,
                         nloo_expected,
                         ncats,
                         ywtest_expected,
                         ref_fam,
                         prd_trms_len_expected,
                         info_str) {
  expect_type(smmrs, "list")
  expect_named(smmrs, c("sub", "ref"), info = info_str)
  expect_type(smmrs$sub, "list")
  expect_length(smmrs$sub, prd_trms_len_expected + 1)
  nobsv_summ <- nobsv
  nobsv_aug <- nobsv * ncats
  nobsv_summ_aug <- nobsv_aug
  if (!is.null(ywtest_expected)) {
    nobsv_summ <- nrow(ywtest_expected)
    nobsv_summ_aug <- nobsv_summ * ncats
  }
  if (identical(cv_method_expected, "LOO")) {
    # Re-order:
    vsel_smmrs_sub_nms[1:2] <- vsel_smmrs_sub_nms[2:1]
    vsel_smmrs_ref_nms[1:2] <- vsel_smmrs_ref_nms[2:1]
  }
  if (ref_fam$for_latent) {
    vsel_smmrs_sub_nms <- c(vsel_smmrs_sub_nms, "oscale")
    vsel_smmrs_ref_nms <- c(vsel_smmrs_ref_nms, "oscale")
  }
  smmrs_sub_j_tester <- function(smmrs_sub_j, tests_oscale = FALSE) {
    if (tests_oscale) {
      vsel_smmrs_sub_nms <- setdiff(vsel_smmrs_sub_nms, "oscale")
      if (!is.null(ref_fam$cats)) {
        ncats <- length(ref_fam$cats)
      } else {
        ncats <- 1L
      }
      nobsv_summ_aug <- nobsv_summ * ncats
    }
    expect_type(smmrs_sub_j, "list")
    expect_named(smmrs_sub_j, vsel_smmrs_sub_nms, info = info_str)
    expect_type(smmrs_sub_j$mu, "double")
    expect_length(smmrs_sub_j$mu, nobsv_summ_aug)
    if (ref_fam$for_augdat ||
        (ref_fam$for_latent && tests_oscale && !is.null(ref_fam$cats))) {
      expect_s3_class(smmrs_sub_j$mu, "augvec")
    }
    if (with_cv && valsearch_expected && identical(cv_method_expected, "LOO")) {
      expect_identical(sum(!is.na(smmrs_sub_j$mu)), nloo_expected * ncats,
                       info = info_str)
    } else {
      expect_true(all(!is.na(smmrs_sub_j$mu)), info = info_str)
    }
    expect_type(smmrs_sub_j$lppd, "double")
    expect_length(smmrs_sub_j$lppd, nobsv_summ)
    if (with_cv) {
      if (ref_fam$for_latent && !tests_oscale &&
          identical(cv_method_expected, "kfold")) {
        expect_true(all(is.na(smmrs_sub_j$lppd)), info = info_str)
      } else if (valsearch_expected && identical(cv_method_expected, "LOO")) {
        expect_identical(sum(!is.na(smmrs_sub_j$lppd)), nloo_expected,
                         info = info_str)
      }
    } else {
      expect_true(all(!is.na(smmrs_sub_j$lppd)), info = info_str)
    }
    return(invisible(TRUE))
  }
  for (j in seq_along(smmrs$sub)) {
    smmrs_sub_j_tester(smmrs$sub[[j]])
    if (ref_fam$for_latent) {
      smmrs_sub_j_tester(smmrs$sub[[j]]$oscale, tests_oscale = TRUE)
    }
  }
  smmrs_ref_tester <- function(smmrs_ref, tests_oscale = FALSE) {
    if (tests_oscale) {
      vsel_smmrs_ref_nms <- setdiff(vsel_smmrs_ref_nms, "oscale")
      if (!is.null(ref_fam$cats)) {
        ncats <- length(ref_fam$cats)
      } else {
        ncats <- 1L
      }
      nobsv_summ_aug <- nobsv_summ * ncats
    }
    expect_type(smmrs_ref, "list")
    expect_named(smmrs_ref, vsel_smmrs_ref_nms, info = info_str)
    if (!from_datafit) {
      expect_type(smmrs_ref$mu, "double")
    }
    expect_length(smmrs_ref$mu, nobsv_summ_aug)
    if (ref_fam$for_augdat ||
        (ref_fam$for_latent && tests_oscale && !is.null(ref_fam$cats))) {
      expect_s3_class(smmrs_ref$mu, "augvec")
    }
    if (!from_datafit) {
      expect_true(all(!is.na(smmrs_ref$mu)), info = info_str)
    } else {
      expect_true(all(is.na(smmrs_ref$mu)), info = info_str)
    }
    if (!from_datafit) {
      expect_type(smmrs_ref$lppd, "double")
    }
    expect_length(smmrs_ref$lppd, nobsv_summ)
    if (!from_datafit && !(ref_fam$for_latent && !tests_oscale &&
                           identical(cv_method_expected, "kfold"))) {
      expect_true(all(!is.na(smmrs_ref$lppd)), info = info_str)
    } else {
      expect_true(all(is.na(smmrs_ref$lppd)), info = info_str)
    }
    return(invisible(TRUE))
  }
  smmrs_ref_tester(smmrs$ref)
  if (ref_fam$for_latent) {
    smmrs_ref_tester(smmrs$ref$oscale, tests_oscale = TRUE)
  }
  return(invisible(TRUE))
}

# A helper function for testing the structure of an object as returned by
# summary.vsel().
#
# @param smmry An object as returned by summary.vsel().
# @param vsel_expected The `vsel` object which was used in the summary.vsel()
#   call.
# @param nterms_max_expected A single numeric value as supplied to
#   summary.vsel()'s argument `nterms_max`.
# @param resp_oscale_expected A single logical value indicating whether
#   element `resp_oscale` is expected to be `TRUE` or `FALSE`.
# @param search_trms_empty_size A single logical value indicating whether
#   `search_terms` was constructed in a way that causes a model size to be
#   without candidate models.
# @param cumul_expected A single logical value indicating whether argument
#   `cumulate` of summary.vsel() was set to `TRUE` or `FALSE`.
# @param info_str A single character string giving information to be printed in
#   case of failure.
# @param ... Arguments passed to smmry_sub_tester() / smmry_sub_tester(), apart
#   from smmry_sub_tester() / smmry_ref_tester()'s arguments `smmry_sub` /
#   `smmry_ref`, `nterms_max_expected`, and `info_str`.
#
# @return `TRUE` (invisible).
smmry_tester <- function(smmry, vsel_expected, nterms_max_expected = NULL,
                         resp_oscale_expected = TRUE,
                         search_trms_empty_size = FALSE, cumul_expected = FALSE,
                         info_str, ...) {
  expect_s3_class(smmry, "vselsummary")
  expect_type(smmry, "list")
  expect_named(
    smmry,
    c("formula", "family", "nobs_train", "type_test", "nobs_test", "method",
      "cv_method", "nloo", "K", "validate_search", "clust_used_search",
      "clust_used_eval", "nprjdraws_search", "nprjdraws_eval", "refit_prj",
      "search_included", "nterms", "perf_sub", "perf_ref", "resp_oscale",
      "deltas", "cumulate"),
    info = info_str
  )

  expect_null(smmry$fit, info = info_str)
  expect_identical(smmry$formula, vsel_expected$refmodel$formula,
                   info = info_str)
  expect_identical(smmry$family, vsel_expected$refmodel$family,
                   info = info_str)
  for (nm in c(
    "nobs_train", "type_test", "nobs_test", "method", "cv_method", "nloo", "K",
    "validate_search", "clust_used_search", "clust_used_eval",
    "nprjdraws_search", "nprjdraws_eval", "refit_prj"
  )) {
    expect_identical(smmry[[nm]], vsel_expected[[nm]],
                     info = paste(info_str, nm, sep = "__"))
  }
  expect_true(
    smmry$search_included %in% c(
      "search included (i.e., fold-wise searches)",
      "search not included (i.e., a full-data search only)"
    ),
    info = info_str
  )
  expect_identical(
    smmry$search_included == "search included (i.e., fold-wise searches)",
    isTRUE(vsel_expected$validate_search),
    info = info_str
  )
  if (is.null(nterms_max_expected)) {
    nterms_ch <- vsel_expected$nterms_max
  } else {
    nterms_ch <- nterms_max_expected
  }
  if (search_trms_empty_size) {
    # This is the "empty_size" setting, so we have to subtract the skipped model
    # size (see issue #307):
    nterms_ch <- nterms_ch - 1
  }
  expect_equal(smmry$nterms, nterms_ch, info = info_str)
  if (isTRUE(vsel_expected$validate_search)) {
    pr_rk_diag_expected <- head(diag(cv_proportions(vsel_expected,
                                                    cumulate = cumul_expected)),
                                nterms_ch)
  } else {
    pr_rk_diag_expected <- rep(NA, nterms_ch)
  }
  smmry_sub_tester(smmry$perf_sub,
                   summaries_ref = vsel_expected$summaries$ref,
                   nterms_max_expected = nterms_max_expected,
                   latent_expected = vsel_expected$refmodel$family$for_latent,
                   resp_oscale_expected = resp_oscale_expected,
                   pr_rk_diag_expected = pr_rk_diag_expected,
                   info_str = info_str, ...)
  smmry_ref_tester(smmry$perf_ref,
                   latent_expected = vsel_expected$refmodel$family$for_latent,
                   resp_oscale_expected = resp_oscale_expected,
                   info_str = info_str, ...)
  expect_identical(smmry$resp_oscale, resp_oscale_expected, info = info_str)
  expect_identical(smmry$deltas, FALSE, info = info_str)
  expect_identical(smmry$cumulate, cumul_expected, info = info_str)

  return(invisible(TRUE))
}

# A helper function for testing the structure of a `data.frame` as returned by
# summary.vsel() in its output element `perf_sub`.
#
# @param smmry_sub A `data.frame` as returned by summary.vsel() in its output
#   element `perf_sub`.
# @param summaries_ref The reference model's summaries, as stored in
#   `<vsel_object>$summaries$ref`.
# @param stats_expected A character vector of expected `stats` (see the
#   corresponding argument of summary.vsel()). Use `NULL` for the default.
# @param type_expected A character vector of expected `type`s (see the
#   corresponding argument of summary.vsel()). Use `NULL` for the default.
# @param nterms_max_expected A single numeric value as supplied to
#   summary.vsel()'s argument `nterms_max`.
# @param cv_method_expected Either `character()` for the no-CV case or a single
#   character string giving the CV method (see argument `cv_method` of
#   cv_varsel()).
# @param prd_trms_expected A character vector giving the expected predictor
#   ranking (not counting the intercept, even for the intercept-only model).
# @param from_datafit A single logical value indicating whether an object of
#   class `datafit` was used for creating the `vsel` object (from which
#   `smmry_sub` was created) (`TRUE`) or not (`FALSE`).
# @param latent_expected A single logical value indicating whether the reference
#   model is expected to be for latent projection (`TRUE`) or not (`FALSE`).
# @param resp_oscale_expected A single logical value indicating whether argument
#   `resp_oscale` of summary.vsel() was set to `TRUE` or `FALSE`.
# @param pr_rk_diag_expected A numeric vector giving the expected values for
#   column `cv_proportions_diag` of `smmry_sub` (except for the first one).
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
smmry_sub_tester <- function(
    smmry_sub,
    summaries_ref,
    stats_expected = NULL,
    type_expected = NULL,
    nterms_max_expected = NULL,
    cv_method_expected = character(),
    prd_trms_expected,
    from_datafit = FALSE,
    latent_expected = FALSE,
    resp_oscale_expected = TRUE,
    pr_rk_diag_expected = rep(
      NA, nterms_max_expected %||% length(prd_trms_expected)
    ),
    info_str
) {
  if (is.null(stats_expected)) {
    stats_expected <- "elpd"
  }
  if (is.null(type_expected)) {
    type_expected <- c("mean", "se", "diff", "diff.se")
  }
  if (is.null(nterms_max_expected)) {
    nterms_max_expected <- length(prd_trms_expected)
  } else {
    prd_trms_expected <- head(prd_trms_expected, nterms_max_expected)
  }

  expect_s3_class(smmry_sub, "data.frame")

  # Rows:
  expect_identical(nrow(smmry_sub), nterms_max_expected + 1L,
                   info = info_str)

  # Columns:
  smmry_nms <- c("size", "ranking_fulldata", "cv_proportions_diag")
  stats_mean_name <- stats_expected
  smmry_nms <- c(smmry_nms,
                 sapply(seq_along(stats_expected), function(stat_idx) {
                   c(stats_mean_name[stat_idx],
                     paste(stats_expected[stat_idx],
                           setdiff(type_expected, "mean"),
                           sep = "."))
                 }))
  expect_named(smmry_sub, smmry_nms, info = info_str)

  # Columns in detail:
  expect_identical(smmry_sub$size, seq_len(nrow(smmry_sub)) - 1,
                   info = info_str)
  expect_identical(smmry_sub$ranking_fulldata,
                   c("(Intercept)", prd_trms_expected),
                   info = info_str)
  expect_identical(smmry_sub$cv_proportions_diag,
                   c(NA, pr_rk_diag_expected),
                   info = info_str)
  is_lat_kfold <-  latent_expected && !resp_oscale_expected &&
    identical(cv_method_expected, "kfold")
  if (is_lat_kfold) {
    for (stat_idx in seq_along(stats_expected)) {
      expect_true(all(is.na(smmry_sub[, stats_mean_name[stat_idx]])),
                  info = info_str)
    }
  }
  if ("diff" %in% type_expected) {
    diff_nm <- paste(stats_expected, "diff", sep = ".")
    for (stat_idx in seq_along(stats_expected)) {
      if (!from_datafit && !is_lat_kfold) {
        if (stats_expected[stat_idx] != "gmpd") {
          # A very basic test:
          expect_equal(
            diff(smmry_sub[, stats_mean_name[stat_idx]]),
            diff(smmry_sub[, diff_nm[stat_idx]]),
            info = info_str
          )
        } else {
          # A very basic test:
          expect_equal(
            exp(diff(log(smmry_sub[, stats_mean_name[stat_idx]]))),
            exp(diff(log(smmry_sub[, diff_nm[stat_idx]]))),
            info = info_str
          )
        }
        if ("oscale" %in% names(summaries_ref) && resp_oscale_expected) {
          summaries_ref <- summaries_ref[["oscale"]]
        }
        if (stats_expected[stat_idx] == "elpd") {
          stat_ref <- sum(summaries_ref$lppd)
        } else if (stats_expected[stat_idx] %in% c("mlpd", "gmpd")) {
          stat_ref <- mean(summaries_ref$lppd)
        } else {
          # TODO: Implement `stat_ref` for the remaining `stats`.
          stat_ref <- NULL
        }
        if (!is.null(stat_ref)) {
          if (stats_expected[stat_idx] != "gmpd") {
            expect_equal(
              smmry_sub[, stats_mean_name[stat_idx]] - stat_ref,
              smmry_sub[, diff_nm[stat_idx]],
              tolerance = 1e-12, info = info_str
            )
          } else {
            expect_equal(
              exp(log(smmry_sub[, stats_mean_name[stat_idx]]) - stat_ref),
              smmry_sub[, diff_nm[stat_idx]],
              tolerance = 1e-12, info = info_str
            )
          }
        }
      } else {
        expect_true(all(is.na(smmry_sub[, diff_nm[stat_idx]])), info = info_str)
      }
    }
  }
  if ("lower" %in% type_expected && !is_lat_kfold) {
    lower_nm <- paste(stats_expected, "lower", sep = ".")
    for (stat_idx in seq_along(stats_expected)) {
      if (!stats_expected[stat_idx] %in% c("auc")) {
        # AUC is excluded here because of PR #347 (originally, RMSE was excluded
        # as well, but PR #496 switched to the delta method for RMSE).
        expect_true(all(smmry_sub[, stats_mean_name[stat_idx]] >=
                          smmry_sub[, lower_nm[stat_idx]]),
                    info = info_str)
      }
    }
  }
  if ("upper" %in% type_expected && !is_lat_kfold) {
    upper_nm <- paste(stats_expected, "upper", sep = ".")
    for (stat_idx in seq_along(stats_expected)) {
      if (!stats_expected[stat_idx] %in% c("auc")) {
        # AUC is excluded here because of PR #347 (originally, RMSE was excluded
        # as well, but PR #496 switched to the delta method for RMSE).
        expect_true(all(smmry_sub[, stats_mean_name[stat_idx]] <=
                          smmry_sub[, upper_nm[stat_idx]]),
                    info = info_str)
      }
    }
  }

  return(invisible(TRUE))
}

# A helper function for testing the structure of a `data.frame` as returned by
# summary.vsel() in its output element `perf_ref`.
#
# @param smmry_ref A vector as returned by summary.vsel() in its output element
#   `perf_ref`.
# @param stats_expected A character vector of expected `stats` (see the
#   corresponding argument of summary.vsel()). Use `NULL` for the default.
# @param type_expected A character vector of expected `type`s (see the
#   corresponding argument of summary.vsel()). Use `NULL` for the default.
# @param cv_method_expected Either `character()` for the no-CV case or a single
#   character string giving the CV method (see argument `cv_method` of
#   cv_varsel()).
# @param from_datafit A single logical value indicating whether an object of
#   class `datafit` was used for creating the `vsel` object (from which
#   `smmry_sub` was created) (`TRUE`) or not (`FALSE`).
# @param latent_expected A single logical value indicating whether the reference
#   model is expected to be for latent projection (`TRUE`) or not (`FALSE`).
# @param resp_oscale_expected A single logical value indicating whether argument
#   `resp_oscale` of summary.vsel() was set to `TRUE` or `FALSE`.
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
smmry_ref_tester <- function(
    smmry_ref,
    stats_expected = NULL,
    type_expected = NULL,
    cv_method_expected = character(),
    from_datafit = FALSE,
    latent_expected = FALSE,
    resp_oscale_expected = TRUE,
    info_str,
    ...
) {
  if (is.null(stats_expected)) {
    stats_expected <- "elpd"
  }
  if (is.null(type_expected)) {
    type_expected <- c("mean", "se", "diff", "diff.se")
  }
  type_expected <- setdiff(type_expected, c("diff", "diff.se"))
  is_lat_kfold <-  latent_expected && !resp_oscale_expected &&
    identical(cv_method_expected, "kfold")

  expect_true(is.vector(smmry_ref, "numeric"), info = info_str)
  smmry_nms <- character()
  stats_mean_name <- stats_expected
  smmry_nms <- c(smmry_nms,
                 sapply(seq_along(stats_expected), function(stat_idx) {
                   c(stats_mean_name[stat_idx],
                     paste(stats_expected[stat_idx],
                           setdiff(type_expected, "mean"),
                           sep = "."))
                 }))
  expect_named(smmry_ref, smmry_nms, info = info_str)

  # Elements in detail:
  if (is_lat_kfold) {
    for (stat_idx in seq_along(stats_expected)) {
      expect_true(all(is.na(smmry_ref[stats_mean_name[stat_idx]])),
                  info = info_str)
    }
  }
  if ("lower" %in% type_expected && !is_lat_kfold && !from_datafit) {
    lower_nm <- paste(stats_expected, "lower", sep = ".")
    for (stat_idx in seq_along(stats_expected)) {
      if (!stats_expected[stat_idx] %in% c("auc")) {
        # AUC is excluded here because of PR #347 (originally, RMSE was excluded
        # as well, but PR #496 switched to the delta method for RMSE).
        expect_true(all(smmry_ref[stats_mean_name[stat_idx]] >=
                          smmry_ref[lower_nm[stat_idx]]),
                    info = info_str)
      }
    }
  }
  if ("upper" %in% type_expected && !is_lat_kfold && !from_datafit) {
    upper_nm <- paste(stats_expected, "upper", sep = ".")
    for (stat_idx in seq_along(stats_expected)) {
      if (!stats_expected[stat_idx] %in% c("auc")) {
        # AUC is excluded here because of PR #347 (originally, RMSE was excluded
        # as well, but PR #496 switched to the delta method for RMSE).
        expect_true(all(smmry_ref[stats_mean_name[stat_idx]] <=
                          smmry_ref[upper_nm[stat_idx]]),
                    info = info_str)
      }
    }
  }

  return(invisible(TRUE))
}

# A helper function for testing the structure of the return value of
# performances().
#
# @param perf_vsel The return value of performances().
# @param smmry_expected The `vselsummary` object which was used in the
#   performances() call.
# @param info_str A single character string giving information to be printed in
#   case of failure and also for naming vdiffr::expect_doppelganger() output.
#
# @return `TRUE` (invisible).
performances_tester <- function(
    perf_vsel,
    smmry_expected,
    info_str
) {
  expect_type(perf_vsel, "list")
  expect_named(perf_vsel, c("submodels", "reference_model"), info = info_str)

  # submodels
  smmry_expected_sub <- smmry_expected[["perf_sub"]]
  smmry_expected_sub <- smmry_expected_sub[
    , -grep("ranking_fulldata|cv_proportions_diag", names(smmry_expected_sub))
  ]
  expect_identical(perf_vsel[["submodels"]], smmry_expected_sub,
                   info = info_str)

  # reference_model
  expect_identical(perf_vsel[["reference_model"]], smmry_expected[["perf_ref"]],
                   info = info_str)

  return(invisible(TRUE))
}

# A helper function for testing the structure of the return value of
# plot.vsel().
#
# @param plot_vsel The return value of plot.vsel().
# @param nterms_max_expected The value that was passed to argument `nterms_max`
#   of plot.vsel().
# @param rk_max_expected The value that was passed to argument
#   `ranking_nterms_max` of plot.vsel().
# @param rk_expected The full-data predictor ranking that is expected to have
#   been used in `plot_vsel`.
# @param abbv_expected The value that was passed to argument
#   `ranking_abbreviate` of plot.vsel().
# @param abbv_args_expected The `list` that was passed to argument
#   `ranking_abbreviate_args` of plot.vsel().
# @param info_str A single character string giving information to be printed in
#   case of failure and also for naming vdiffr::expect_doppelganger() output.
#
# @return `TRUE` (invisible).
plot_vsel_tester <- function(
    plot_vsel,
    nterms_max_expected = NULL,
    rk_max_expected = NULL,
    rk_expected,
    abbv_expected = NULL,
    abbv_args_expected = list(),
    info_str
) {
  expect_s3_class(plot_vsel, c("gg", "ggplot"))
  if (isTRUE(abbv_expected) &&
      (is.null(rk_max_expected) || !is.na(rk_max_expected))) {
    if (!is.null(rk_max_expected)) {
      rk_expected <- head(rk_expected, rk_max_expected)
    } else if (!is.null(nterms_max_expected)) {
      rk_expected <- head(rk_expected, nterms_max_expected)
    }
    attr_abbv_expected <- do.call(
      abbreviate,
      c(list(names.arg = c("(Intercept)", rk_expected)), abbv_args_expected)
    )
  } else {
    attr_abbv_expected <- NULL
  }
  expect_identical(attr(plot_vsel, "projpred_ranking_abbreviated"),
                   attr_abbv_expected, info = info_str)
  if (run_snaps) {
    vdiffr::expect_doppelganger(info_str, plot_vsel)
  }

  return(invisible(TRUE))
}

# A helper function for testing the structure of a `ranking` object as returned
# by ranking().
#
# @param rk A `ranking` object as returned by ranking().
# @param fulldata_expected The expected `fulldata` element of `rk`.
# @param foldwise_expected The expected `foldwise` element of `rk`.
# @param nterms_max_expected A single numeric value as supplied to
#   ranking.vsel()'s argument `nterms_max`.
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
ranking_tester <- function(rk, fulldata_expected, foldwise_expected,
                           nterms_max_expected, info_str) {
  expect_s3_class(rk, "ranking", exact = TRUE)
  expect_type(rk, "list")
  expect_named(rk, c("fulldata", "foldwise"), info = info_str)
  # Since we test elements `predictor_ranking` and `predictor_ranking_cv`
  # already thoroughly in vsel_tester(), we can simply plug these into
  # `fulldata_expected` and `foldwise_expected` and then test via identical()
  # (after taking `nterms_max` into account):
  if (!is.null(nterms_max_expected)) {
    fulldata_expected <- head(fulldata_expected, nterms_max_expected)
    if (!is.null(foldwise_expected)) {
      foldwise_expected <- foldwise_expected[, seq_len(nterms_max_expected),
                                             drop = FALSE]
    }
  }
  expect_identical(rk[["fulldata"]], fulldata_expected, info = info_str)
  expect_identical(rk[["foldwise"]], foldwise_expected, info = info_str)
  return(invisible(TRUE))
}

# A helper function for testing the structure of a `cv_proportions` object as
# returned by cv_proportions().
#
# @param pr A `cv_proportions` object as returned by cv_proportions().
# @param cumulate_expected A single logical value indicating whether
#   cv_proportions() was run with `cumulate = TRUE` (`TRUE`) or not (`FALSE`).
# @param nterms_max_expected A single numeric value as supplied to the
#   underlying ranking.vsel() call's argument `nterms_max`, but must not be
#   `NULL`.
# @param info_str A single character string giving information to be printed in
#   case of failure.
#
# @return `TRUE` (invisible).
cv_proportions_tester <- function(pr, cumulate_expected = FALSE,
                                  nterms_max_expected, cnms_expected,
                                  info_str) {
  classes_expected <- "cv_proportions"
  if (cumulate_expected) {
    classes_expected <- c("cv_proportions_cumul", classes_expected)
  }
  expect_s3_class(pr, classes_expected, exact = TRUE)
  expect_equal(dim(pr), c(nterms_max_expected, nterms_max_expected),
               info = info_str)
  expect_true(is.numeric(pr), info = info_str)
  rnms_expected <- as.character(seq_len(nterms_max_expected))
  if (cumulate_expected) {
    rnms_expected <- paste0("<=", rnms_expected)
  }
  if (length(cnms_expected) > nterms_max_expected) {
    cnms_expected <- head(cnms_expected, nterms_max_expected)
  }
  expect_identical(dimnames(pr),
                   list("size" = rnms_expected, "predictor" = cnms_expected),
                   info = info_str)
  return(invisible(TRUE))
}
