# Divergence minimizers ---------------------------------------------------

## Traditional (and latent) projection ------------------------------------

# Needed to avoid a NOTE in `R CMD check`:
if (getRversion() >= package_version("2.15.1")) {
  utils::globalVariables("formula_s")
  utils::globalVariables("projpred_var_s")
  utils::globalVariables("projpred_formula_no_random_s")
}

divmin <- function(formula, projpred_var, projpred_verbose = FALSE, ...) {
  trms_all <- extract_terms_response(formula)
  has_grp <- length(trms_all$group_terms) > 0
  has_add <- length(trms_all$additive_terms) > 0
  projpred_formulas_no_random <- NA
  projpred_random <- NA
  # Define sdivmin(), the divergence minimizer for each draw s = 1, ..., S (and
  # perform other actions, if necessary):
  if (!has_grp && !has_add) {
    sdivmin <- get(getOption("projpred.glm_fitter", "fit_glm_ridge_callback"),
                   mode = "function")
  } else if (has_grp && !has_add) {
    sdivmin <- fit_glmer_callback
    if (getOption("projpred.PQL", FALSE)) {
      # Split up the formula into a fixed and a random part (note: we could also
      # use lme4::nobars() and lme4::findbars() here):
      formula_random <- split_formula_random_gamm4(formula)
      projpred_formulas_no_random <- validate_response_formula(
        formula_random$formula
      )
      projpred_random <- formula_random$random
    }
  } else if (!has_grp && has_add) {
    sdivmin <- fit_gam_callback
  } else if (has_grp && has_add) {
    sdivmin <- fit_gamm_callback
    formula_random <- split_formula_random_gamm4(formula)
    projpred_formulas_no_random <- validate_response_formula(
      formula_random$formula
    )
    projpred_random <- formula_random$random
  }
  formulas <- validate_response_formula(formula)
  if (is.list(projpred_formulas_no_random)) {
    stopifnot(length(projpred_formulas_no_random) == length(formulas))
  } else {
    projpred_formulas_no_random <- as.list(
      rep(projpred_formulas_no_random, length(formulas))
    )
  }

  if (length(formulas) < getOption("projpred.prll_prj_trigger", Inf)) {
    # Sequential case. Actually, we could simply use ``%do_projpred%` <-
    # foreach::`%do%`` here and then proceed as in the parallel case, but that
    # would require adding more "hard" dependencies (because packages 'foreach'
    # and 'iterators' would have to be moved from `Suggests:` to `Imports:`).
    if (projpred_verbose) {
      pb <- utils::txtProgressBar(min = 0, max = length(formulas), style = 3,
                                  initial = 0)
      on.exit(close(pb))
    }
    return(lapply(seq_along(formulas), function(s) {
      if (projpred_verbose) {
        on.exit(utils::setTxtProgressBar(pb, s))
      }
      sdivmin(
        formula = formulas[[s]],
        projpred_var = projpred_var[, s, drop = FALSE],
        projpred_formula_no_random = projpred_formulas_no_random[[s]],
        projpred_random = projpred_random,
        ...
      )
    }))
  } else {
    # Parallel case.
    if (!requireNamespace("foreach", quietly = TRUE)) {
      stop("Please install the 'foreach' package.")
    }
    if (!requireNamespace("iterators", quietly = TRUE)) {
      stop("Please install the 'iterators' package.")
    }
    dot_args <- list(...)
    `%do_projpred%` <- foreach::`%dopar%`
    return(foreach::foreach(
      formula_s = formulas,
      projpred_var_s = iterators::iter(projpred_var, by = "column"),
      projpred_formula_no_random_s = projpred_formulas_no_random,
      .export = c("sdivmin", "projpred_random", "dot_args"),
      .noexport = c(
        "object", "p_sel", "search_path", "p_ref", "refmodel", "formulas",
        "projpred_var", "projpred_formulas_no_random"
      )
    ) %do_projpred% {
      do.call(
        sdivmin,
        c(list(formula = formula_s,
               projpred_var = projpred_var_s,
               projpred_formula_no_random = projpred_formula_no_random_s,
               projpred_random = projpred_random),
          dot_args)
      )
    })
  }
}

# Use projpred's own implementation to fit non-multilevel non-additive
# submodels:
fit_glm_ridge_callback <- function(formula, data,
                                   projpred_var = matrix(nrow = nrow(data)),
                                   projpred_regul = 1e-4, ...) {
  # Preparations:
  fr <- model.frame(formula, data = data, drop.unused.levels = TRUE)
  da_classes <- attr(attr(fr, "terms"), "dataClasses")
  nms_chr_fac <- names(da_classes)[da_classes %in% c("character", "factor")]
  resp_nm <- all.vars(attr(fr, "terms"))[attr(attr(fr, "terms"), "response")]
  nms_chr_fac <- setdiff(nms_chr_fac, resp_nm)
  if (length(nms_chr_fac) > 0) {
    xlvls <- lapply(setNames(nm = nms_chr_fac), function(nm_chr_fac) {
      levels(as.factor(fr[[nm_chr_fac]]))
    })
  } else {
    xlvls <- NULL
  }
  # TODO: In the following model.matrix() call, allow user-specified contrasts
  # to be passed to argument `contrasts.arg`. The `contrasts.arg` default
  # (`NULL`) uses `options("contrasts")` internally, but it might be more
  # convenient to let users specify contrasts directly. At that occasion,
  # contrasts should also be tested thoroughly (not done until now).
  x <- model.matrix(formula, data = fr)
  x <- x[, colnames(x) != "(Intercept)", drop = FALSE]
  y <- model.response(fr)
  # Exclude arguments from `...` which cannot be passed to glm_ridge():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    methods::formalArgs(glm_ridge)
  )]
  # Call the submodel fitter:
  fit <- do.call(glm_ridge, c(
    list(x = x, y = y, lambda = projpred_regul, obsvar = projpred_var),
    dot_args
  ))
  # Post-processing:
  rownames(fit$beta) <- colnames(x)
  sub <- nlist(alpha = fit$beta0, beta = fit$beta, w = fit$w, formula, x, y,
               xlvls)
  class(sub) <- "subfit"
  return(sub)
}

# Alternative to fit_glm_ridge_callback() (may be used via global option
# `projpred.glm_fitter`):
fit_glm_callback <- function(formula, family, ...) {
  if (family$family == "gaussian" && family$link == "identity" &&
      getOption("projpred.gaussian_not_as_generalized", TRUE)) {
    # Exclude arguments from `...` which cannot be passed to stats::lm():
    dot_args <- list(...)
    dot_args <- dot_args[intersect(
      names(dot_args),
      c(methods::formalArgs(stats::lm),
        methods::formalArgs(stats::lm.fit),
        methods::formalArgs(stats::lm.wfit))
    )]
    # Call the submodel fitter:
    return(suppressMessages(suppressWarnings(do.call(stats::lm, c(
      list(formula = formula),
      dot_args
    )))))
  } else {
    # Exclude arguments from `...` which cannot be passed to stats::glm():
    dot_args <- list(...)
    dot_args <- dot_args[intersect(
      names(dot_args),
      c(methods::formalArgs(stats::glm),
        methods::formalArgs(stats::glm.control))
    )]
    # Call the submodel fitter:
    return(suppressMessages(suppressWarnings(do.call(stats::glm, c(
      list(formula = formula, family = family),
      dot_args
    )))))
  }
}

# Use package "mgcv" to fit additive non-multilevel submodels:
fit_gam_callback <- function(formula, ...) {
  # Exclude arguments from `...` which cannot be passed to mgcv::gam():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    c(methods::formalArgs(mgcv::gam),
      methods::formalArgs(mgcv::gam.fit))
  )]
  # Call the submodel fitter:
  return(suppressMessages(suppressWarnings(do.call(mgcv::gam, c(
    list(formula = formula),
    dot_args
  )))))
}

# Use package "gamm4" to fit additive multilevel submodels:
fit_gamm_callback <- function(formula, projpred_formula_no_random,
                              projpred_random, data, family,
                              control = control_callback(family), ...) {
  # Exclude arguments from `...` which cannot be passed to gamm4::gamm4():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    c(methods::formalArgs(gamm4::gamm4),
      methods::formalArgs(lme4::lFormula),
      methods::formalArgs(lme4::glFormula))
  )]
  # Call the submodel fitter:
  fit <- tryCatch({
    suppressMessages(suppressWarnings(do.call(gamm4::gamm4, c(
      list(formula = projpred_formula_no_random, random = projpred_random,
           data = data, family = family, control = control),
      dot_args
    ))))
  }, error = function(e) {
    if (grepl("not positive definite", as.character(e))) {
      if ("optimx" %in% control$optimizer &&
          length(control$optCtrl$method) > 0 &&
          control$optCtrl$method == "nlminb") {
        stop("Encountering the `not positive definite` error while running ",
             "the lme4 fitting procedure, but cannot fix this automatically ",
             "anymore. You will probably have to tweak gamm4 tuning ",
             "parameters manually (via `...`).")
      }
      if (!requireNamespace("optimx", quietly = TRUE)) {
        stop("Trying to fix an lme4 error with the help of an optimx ",
             "optimizer, but cannot find the optimx package. Please install ",
             "it to be able to try this fix.")
      }
      return(fit_gamm_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        data = data,
        family = family,
        control = control_callback(family,
                                   optimizer = "optimx",
                                   optCtrl = list(method = "nlminb")),
        ...
      ))
    } else {
      stop(e)
    }
  })

  fit$random <- projpred_random
  fit$formula <- projpred_formula_no_random
  class(fit) <- c("gamm4")
  return(fit)
}

# Use package "lme4" to fit multilevel submodels:
fit_glmer_callback <- function(formula, projpred_formula_no_random,
                               projpred_random, family,
                               control = control_callback(family), ...) {
  tryCatch({
    if (getOption("projpred.PQL", FALSE)) {
      # Exclude arguments from `...` which cannot be passed to MASS::glmmPQL():
      dot_args <- list(...)
      dot_args <- dot_args[intersect(
        names(dot_args),
        methods::formalArgs(MASS::glmmPQL)
      )]
      # Strip parentheses from group-level terms:
      random_trms <- labels(terms(projpred_random))
      random_fmls <- lapply(random_trms, function(random_trm) {
        as.formula(paste("~", random_trm))
      })
      if (length(random_fmls) == 1) {
        random_fmls <- random_fmls[[1]]
      } else if (length(random_fmls) > 1) {
        # Use the workaround(s) from <https://stackoverflow.com/questions/
        # 36643713/how-to-specify-different-random-effects-in-nlme-vs-lme4/
        # 38805602#38805602>:
        random_trms_nogrp <- lapply(random_trms, function(random_trm) {
          random_fml_nogrp <- as.formula(
            paste("~", sub("[[:blank:]]*\\|.*$", "", random_trm))
          )
          return(labels(terms(random_fml_nogrp)))
        })
        ### TODO (glmmPQL): Do this via adding argument `data` explicitly:
        stopifnot(!is.null(dot_args$data))
        if ("projpred_internal_dummy1s_PQL" %in% names(dot_args$data)) {
          stop("Need to write to column `projpred_internal_dummy1s_PQL` of ",
               "`data`, but that column already exists. Please rename this ",
               "column in `data` and try again.")
        }
        dot_args$data$projpred_internal_dummy1s_PQL <- factor(1)
        ###
        list_pdIdent <- lapply(random_trms, function(random_trm) {
          return(nlme::pdIdent(as.formula(
            paste("~", sub("^.*\\|[[:blank:]]*", "", random_trm), "- 1")
          )))
        })
        idxs_multi_trms <- which(lengths(random_trms_nogrp) > 0)
        if (length(idxs_multi_trms) > 0) {
          warning("In order to be able to use MASS::glmmPQL(), we have to use ",
                  "a workaround by which the random intercepts and random ",
                  "slopes will be assumed to have a correlation of zero.")
          list_pdIdent_add <- lapply(idxs_multi_trms, function(idx_multi_trms) {
            random_grp <- sub("^.*\\|[[:blank:]]*", "",
                              random_trms[[idx_multi_trms]])
            random_IAs <- paste0(random_trms_nogrp[[idx_multi_trms]], ":",
                                 random_grp)
            return(lapply(random_IAs, function(random_IA) {
              nlme::pdIdent(as.formula(paste("~", random_IA)))
            }))
          })
          list_pdIdent_add <- unlist(list_pdIdent_add, recursive = FALSE)
          list_pdIdent <- c(list_pdIdent, list_pdIdent_add)
        }
        random_fmls <- list(
          projpred_internal_dummy1s_PQL = nlme::pdBlocked(list_pdIdent)
        )
      } else if (length(random_fmls) == 0) {
        stop("Unexpected length of `random_fmls`.")
      }
      # Call the submodel fitter:
      return(suppressMessages(suppressWarnings(do.call(MASS::glmmPQL, c(
        list(fixed = projpred_formula_no_random, random = random_fmls,
             family = family, control = control),
        dot_args
      )))))
    } else if (family$family == "gaussian" && family$link == "identity" &&
               getOption("projpred.gaussian_not_as_generalized", TRUE)) {
      # Exclude arguments from `...` which cannot be passed to lme4::lmer():
      dot_args <- list(...)
      dot_args <- dot_args[intersect(
        names(dot_args),
        methods::formalArgs(lme4::lmer)
      )]
      # Call the submodel fitter:
      return(suppressMessages(suppressWarnings(do.call(lme4::lmer, c(
        list(formula = formula, control = control),
        dot_args
      )))))
    } else {
      # Exclude arguments from `...` which cannot be passed to lme4::glmer():
      dot_args <- list(...)
      dot_args <- dot_args[intersect(
        names(dot_args),
        methods::formalArgs(lme4::glmer)
      )]
      # Call the submodel fitter:
      return(suppressMessages(suppressWarnings(do.call(lme4::glmer, c(
        list(formula = formula, family = family, control = control),
        dot_args
      )))))
    }
  }, error = function(e) {
    if (grepl("No random effects", as.character(e))) {
      # This case should not occur anymore (because divmin() should pick the
      # correct submodel fitter based on the submodel's formula), but leave it
      # here in case user-specified divergence minimizers make use of it.
      glm_fitter <- get(getOption("projpred.glm_fitter",
                                  "fit_glm_ridge_callback"),
                        mode = "function")
      return(glm_fitter(
        formula = formula, family = family, ...
      ))
    } else if (grepl("not positive definite", as.character(e))) {
      if ("optimx" %in% control$optimizer &&
          length(control$optCtrl$method) > 0 &&
          control$optCtrl$method == "nlminb") {
        stop("Encountering the `not positive definite` error while running ",
             "the lme4 fitting procedure, but cannot fix this automatically ",
             "anymore. You will probably have to tweak lme4 tuning parameters ",
             "manually (via `...`).")
      }
      if (!requireNamespace("optimx", quietly = TRUE)) {
        stop("Trying to fix an lme4 error with the help of an optimx ",
             "optimizer, but cannot find the optimx package. Please install ",
             "it to be able to try this fix.")
      }
      return(fit_glmer_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        family = family,
        control = control_callback(family,
                                   optimizer = "optimx",
                                   optCtrl = list(method = "nlminb")),
        ...
      ))
    } else if (grepl("PIRLS", as.character(e))) {
      if (length(dot_args$nAGQ) > 0) {
        nAGQ_new <- dot_args$nAGQ + 1L
      } else {
        nAGQ_new <- 20L
      }
      if (nAGQ_new > 30L) {
        stop("Encountering a PIRLS error while running the lme4 fitting ",
             "procedure, but cannot fix this automatically anymore. You will ",
             "probably have to tweak lme4 tuning parameters manually (via ",
             "`...`).")
      }
      return(fit_glmer_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        family = family,
        control = control,
        nAGQ = nAGQ_new,
        ...
      ))
    } else if (grepl("pwrssUpdate did not converge in \\(maxit\\) iterations",
                     as.character(e))) {
      tolPwrss_new <- 10 * control$tolPwrss
      if (length(control$optCtrl$maxfun) > 0) {
        maxfun_new <- 10 * control$optCtrl$maxfun
      } else {
        maxfun_new <- 1e4
      }
      if (length(control$optCtrl$maxit) > 0) {
        maxit_new <- 10 * control$optCtrl$maxit
      } else {
        maxit_new <- 1e4
      }
      if (tolPwrss_new > 1e-4 && maxfun_new > 1e7 && maxit_new > 1e7) {
        stop("Encountering the ",
             "`pwrssUpdate did not converge in (maxit) iterations` error ",
             "while running the lme4 fitting procedure, but cannot fix this ",
             "automatically anymore. You will probably have to tweak lme4 ",
             "tuning parameters manually (via `...`).")
      }
      return(fit_glmer_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        family = family,
        control = control_callback(family, tolPwrss = tolPwrss_new,
                                   optCtrl = list(maxfun = maxfun_new,
                                                  maxit = maxit_new)),
        ...
      ))
    } else if (getOption("projpred.PQL", FALSE) &&
               grepl("iteration limit reached without convergence",
                     as.character(e))) {
      if (length(control$msMaxIter) > 0 && control$msMaxIter >= 100) {
        stop("Encountering the `iteration limit reached without convergence` ",
             "error while running the MASS::glmmPQL() fitting procedure, but ",
             "cannot fix this automatically anymore. You will probably have ",
             "to tweak MASS::glmmPQL() tuning parameters manually (via `...`).")
      }
      return(fit_glmer_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        family = family,
        control = control_callback(msMaxIter = 100),
        ...
      ))
    } else if (getOption("projpred.PQL", FALSE) &&
               grepl("false convergence", as.character(e))) {
      if (length(control$niterEM) > 0 && control$niterEM >= 50) {
        stop("Encountering the `false convergence` ",
             "error while running the MASS::glmmPQL() fitting procedure, but ",
             "cannot fix this automatically anymore. You will probably have ",
             "to tweak MASS::glmmPQL() tuning parameters manually (via `...`).")
      }
      return(fit_glmer_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        family = family,
        control = control_callback(niterEM = 50),
        ...
      ))
    } else if (getOption("projpred.PQL", FALSE) &&
               grepl("fewer observations than random effects",
                     as.character(e))) {
      if (length(control$allow.n.lt.q) > 0 && isTRUE(control$allow.n.lt.q)) {
        stop("Encountering the `fewer observations than random effects` ",
             "error while running the MASS::glmmPQL() fitting procedure, but ",
             "cannot fix this automatically anymore. You will probably have ",
             "to tweak MASS::glmmPQL() tuning parameters manually (via `...`).")
      }
      return(fit_glmer_callback(
        formula = formula,
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        family = family,
        control = control_callback(allow.n.lt.q = TRUE),
        ...
      ))
    } else {
      stop(e)
    }
  })
}

# Helper function for fit_glmer_callback() and fit_gamm_callback() to get the
# appropriate control options depending on the family:
control_callback <- function(family, ...) {
  if (getOption("projpred.PQL", FALSE)) {
    return(nlme::lmeControl(...))
  } else if (family$family == "gaussian" && family$link == "identity" &&
             getOption("projpred.gaussian_not_as_generalized", TRUE)) {
    return(lme4::lmerControl(...))
  } else {
    return(lme4::glmerControl(...))
  }
}

## Augmented-data projection ----------------------------------------------

# Needed to avoid a NOTE in `R CMD check`:
if (getRversion() >= package_version("2.15.1")) {
  utils::globalVariables("projpred_w_aug_s")
  utils::globalVariables("projpred_internal_w_aug")
}

divmin_augdat <- function(formula, data, family, weights, projpred_var,
                          projpred_ws_aug, projpred_verbose = FALSE, ...) {
  trms_all <- extract_terms_response(formula)
  has_grp <- length(trms_all$group_terms) > 0
  projpred_formula_no_random <- NA
  projpred_random <- NA
  if (family$family == "binomial") {
    # Switch back to the traditionally extended binomial family (so that the
    # original link and inverse-link functions are used, for example):
    family <- extend_family(
      get(family$family, mode = "function")(link = family$link)
    )
    if (!has_grp) {
      sdivmin <- get(getOption("projpred.glm_fitter", "fit_glm_ridge_callback"),
                     mode = "function")
      if (getOption("projpred.glm_fitter", "fit_glm_ridge_callback") ==
          "fit_glm_ridge_callback") {
        # glm_ridge() cannot handle `factor` responses:
        response_name <- extract_terms_response(formula)$response
        stopifnot(is.factor(data[[response_name]]))
        data[[response_name]] <- as.integer(data[[response_name]]) - 1L
      }
    } else {
      sdivmin <- fit_glmer_callback
      if (getOption("projpred.PQL", FALSE)) {
        formula_random <- split_formula_random_gamm4(formula)
        projpred_formula_no_random <- formula_random$formula
        projpred_random <- formula_random$random
      }
    }
  } else if (family$family %in% c("cumulative", "cumulative_rstanarm")) {
    if (!has_grp) {
      sdivmin <- fit_cumul
    } else {
      sdivmin <- fit_cumul_mlvl
    }
  } else if (family$family == "categorical") {
    if (!has_grp) {
      sdivmin <- fit_categ
    } else {
      sdivmin <- fit_categ_mlvl
      formula_random <- split_formula_random_gamm4(formula)
      projpred_formula_no_random <- formula_random$formula
      projpred_random <- formula_random$random
    }
  } else {
    stop("Family `", family$family, "` is not supported by divmin_augdat().")
  }

  # Coerce augmented-data matrices to ordinary matrices and augmented-length
  # vectors to ordinary vectors so that external packages may subset as usual:
  projpred_ws_aug <- unclass(projpred_ws_aug)
  attr(projpred_ws_aug, "nobs_orig") <- NULL

  if (ncol(projpred_ws_aug) < getOption("projpred.prll_prj_trigger", Inf)) {
    # Sequential case. Actually, we could simply use ``%do_projpred%` <-
    # foreach::`%do%`` here and then proceed as in the parallel case, but that
    # would require adding more "hard" dependencies (because packages 'foreach'
    # and 'iterators' would have to be moved from `Suggests:` to `Imports:`).
    if (projpred_verbose) {
      pb <- utils::txtProgressBar(min = 0, max = ncol(projpred_ws_aug),
                                  style = 3, initial = 0)
      on.exit(close(pb))
    }
    return(lapply(seq_len(ncol(projpred_ws_aug)), function(s) {
      if (projpred_verbose) {
        on.exit(utils::setTxtProgressBar(pb, s))
      }
      sdivmin(
        formula = formula,
        data = data,
        family = family,
        weights = projpred_ws_aug[, s],
        projpred_formula_no_random = projpred_formula_no_random,
        projpred_random = projpred_random,
        ...
      )
    }))
  } else {
    # Parallel case.
    if (!requireNamespace("foreach", quietly = TRUE)) {
      stop("Please install the 'foreach' package.")
    }
    if (!requireNamespace("iterators", quietly = TRUE)) {
      stop("Please install the 'iterators' package.")
    }
    dot_args <- list(...)
    `%do_projpred%` <- foreach::`%dopar%`
    return(foreach::foreach(
      projpred_w_aug_s = iterators::iter(projpred_ws_aug, by = "column"),
      .export = c(
        "sdivmin", "formula", "data", "family", "projpred_formula_no_random",
        "projpred_random", "dot_args"
      ),
      .noexport = c(
        "object", "p_sel", "search_path", "p_ref", "refmodel", "projpred_var",
        "projpred_ws_aug", "linkobjs"
      )
    ) %do_projpred% {
      do.call(
        sdivmin,
        c(list(formula = formula,
               data = data,
               family = family,
               weights = as.vector(projpred_w_aug_s),
               projpred_formula_no_random = projpred_formula_no_random,
               projpred_random = projpred_random),
          dot_args)
      )
    })
  }
}

# Use MASS::polr() to fit submodels for the brms::cumulative() family:
fit_cumul <- function(formula, data, family, weights, ...) {
  # Argument `weights` of MASS::polr() expects a column name (as a symbol):
  if ("projpred_internal_w_aug" %in% names(data)) {
    stop("Need to write to column `projpred_internal_w_aug` of `data`, but ",
         "that column already exists. Please rename this column in `data` and ",
         "try again.")
  }
  stopifnot(length(weights) == nrow(data))
  data$projpred_internal_w_aug <- weights
  # Exclude arguments from `...` which cannot be passed to MASS::polr():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    c(methods::formalArgs(MASS::polr),
      methods::formalArgs(stats::optim))
  )]
  # Adapt `link`:
  link_nm <- family$link
  if (link_nm == "logit") {
    link_nm <- "logistic"
  } else if (link_nm == "probit_approx") {
    link_nm <- "probit"
  }
  # For catching warnings via capture.output() (which is necessary to filter out
  # the warning "non-integer #successes in a binomial glm!"):
  warn_orig <- options(warn = 1)
  # Call the submodel fitter:
  warn_capt <- utils::capture.output({
    fitobj <- try(do.call(MASS::polr, c(
      list(formula = formula,
           data = data,
           weights = quote(projpred_internal_w_aug),
           model = FALSE,
           method = link_nm),
      dot_args
    )), silent = TRUE)
  }, type = "message")
  options(warn_orig)
  if (inherits(fitobj, "try-error") &&
      grepl(paste("initial value in 'vmmin' is not finite",
                  "attempt to find suitable starting values failed",
                  sep = "|"),
            attr(fitobj, "condition")$message)) {
    # Try to fix this automatically by specifying `start` values (the check for
    # the intercept being present is just performed to show that *by
    # construction*, we expect an intercept here; later code might be general
    # enough to deal with a missing intercept, but that's not the point).
    stopifnot(attr(terms(formula), "intercept") == 1)
    ncoefs <- count_terms_in_formula(formula) -
      attr(terms(formula), "intercept")
    start_coefs <- rep(0, ncoefs)
    ncats <- length(unique(eval_lhs(formula = formula, data = data)))
    nthres <- ncats - 1L
    # Start with thresholds which imply equal probabilities for the response
    # categories:
    start_thres <- linkfun_raw(seq_len(nthres) / ncats, link_nm = link_nm)
    warn_orig <- options(warn = 1)
    warn_capt <- utils::capture.output({
      fitobj <- try(do.call(MASS::polr, c(
        list(formula = formula,
             data = data,
             weights = quote(projpred_internal_w_aug),
             model = FALSE,
             method = link_nm,
             start = c(start_coefs, start_thres)),
        dot_args
      )), silent = TRUE)
    }, type = "message")
    options(warn_orig)
  }
  if (inherits(fitobj, "try-error")) {
    stop(attr(fitobj, "condition")$message)
  }
  warn_capt <- grep("Warning in .*:$", warn_capt, value = TRUE, invert = TRUE)
  warn_capt <- grep("non-integer #successes in a binomial glm!$", warn_capt,
                    value = TRUE, invert = TRUE)
  if (length(warn_capt) > 0) {
    warning(warn_capt)
  }
  return(fitobj)
}

# Use ordinal::clmm() to fit multilevel submodels for the brms::cumulative()
# family:
fit_cumul_mlvl <- function(formula, data, family, weights, ...) {
  # Argument `weights` of ordinal::clmm() expects a column name (as a symbol):
  if ("projpred_internal_w_aug" %in% names(data)) {
    stop("Need to write to column `projpred_internal_w_aug` of `data`, but ",
         "that column already exists. Please rename this column in `data` and ",
         "try again.")
  }
  stopifnot(length(weights) == nrow(data))
  data$projpred_internal_w_aug <- weights
  # Exclude arguments from `...` which cannot be passed to ordinal::clmm():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    c(methods::formalArgs(ordinal::clmm),
      methods::formalArgs(ordinal::clm.control),
      if (requireNamespace("ucminf", quietly = TRUE)) {
        methods::formalArgs(ucminf::ucminf)
      } else {
        character()
      },
      methods::formalArgs(stats::nlminb),
      methods::formalArgs(stats::optim))
  )]
  # Since ordinal::clmm() sometimes seems to have issues with formulas lacking
  # an intercept, add it here (note that this is a quite "hacky" solution;
  # perhaps reformulate() could be used instead):
  stopifnot(attr(terms(formula), "intercept") == 1)
  formula[[3]] <- str2lang(paste0("1 + ", as.character(formula)[[3]]))
  # Adapt `link`:
  link_nm <- family$link
  if (link_nm == "probit_approx") {
    link_nm <- "probit"
  }
  # For catching warnings via capture.output() (which is necessary to filter out
  # the warning "Using formula(x) is deprecated when x is a character vector of
  # length > 1. [...]"):
  warn_orig <- options(warn = 1)
  # Call the submodel fitter:
  warn_capt <- utils::capture.output({
    fitobj <- try(do.call(ordinal::clmm, c(
      list(formula = formula,
           data = data,
           weights = quote(projpred_internal_w_aug),
           contrasts = NULL,
           Hess = FALSE,
           model = FALSE,
           link = link_nm),
      dot_args
    )), silent = TRUE)
  }, type = "message")
  options(warn_orig)
  if (inherits(fitobj, "try-error")) {
    stop(attr(fitobj, "condition")$message)
  }
  warn_capt <- grep(
    paste("Using formula\\(x\\) is deprecated when x is a character vector of",
          "length > 1\\.$"),
    warn_capt, value = TRUE, invert = TRUE
  )
  warn_capt <- grep(
    "Consider formula\\(paste\\(x, collapse = .*\\)\\) instead\\.$",
    warn_capt, value = TRUE, invert = TRUE
  )
  if (length(warn_capt) > 0) {
    warning(warn_capt)
  }
  # Needed for the ordinal:::predict.clm() workaround (the value `"negative"` is
  # the default, see `?ordinal::clm.control`):
  fitobj$control$sign.location <- "negative"
  return(fitobj)
}

# Use nnet::multinom() to fit submodels for the brms::categorical() family:
fit_categ <- function(formula, data, family, weights, ...) {
  # Exclude arguments from `...` which cannot be passed to nnet::multinom():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    c(methods::formalArgs(nnet::multinom),
      methods::formalArgs(nnet::nnet))
  )]
  # Adapt `link`:
  link_nm <- family$link
  if (link_nm != "logit") {
    stop("For the brms::categorical() family, projpred only supports the ",
         "logit link.")
  }
  # Handle offsets (but note that currently, offsets in the submodel fitter are
  # unexpected; this case could only occur in `test_div_minimizer.R`):
  trms <- terms(formula, data = data)
  offs_attr <- attr(trms, "offset")
  if (length(offs_attr)) {
    mfr <- model.frame(formula = formula, data = data)
    offs_obj <- model.offset(mfr)
    if (is.null(dim(offs_obj))) {
      # Recycle the vector of offsets to a matrix with number of columns equal
      # to the number of response categories:
      resp_vec <- model.response(mfr)
      ncats <- length(unique(resp_vec))
      offs_mat <- matrix(offs_obj, nrow = nrow(data), ncol = ncats)
      offs_nm <- as.character(attr(trms, "variables")[[offs_attr + 1L]][[2]])
      data[[offs_nm]] <- offs_mat
    }
  }
  # Call the submodel fitter:
  out_capt <- utils::capture.output({
    fitobj <- do.call(nnet::multinom, c(
      list(formula = formula,
           data = data,
           weights = weights),
      dot_args
    ))
  })
  if (utils::tail(out_capt, 1) != "converged") {
    warning("The nnet::multinom() submodel fit did not converge.")
  }
  return(fitobj)
}

# Use mclogit::mblogit() to fit multilevel submodels for the brms::categorical()
# family:
fit_categ_mlvl <- function(formula, projpred_formula_no_random,
                           projpred_random, data, family, weights, ...) {
  # Exclude arguments from `...` which cannot be passed to mclogit::mblogit():
  dot_args <- list(...)
  dot_args <- dot_args[intersect(
    names(dot_args),
    c(methods::formalArgs(mclogit::mblogit),
      methods::formalArgs(mclogit::mmclogit.control))
  )]
  # Adapt `link`:
  link_nm <- family$link
  if (link_nm != "logit") {
    stop("For the brms::categorical() family, projpred only supports the ",
         "logit link.")
  }
  # Handle offsets (but note that currently, offsets in the submodel fitter are
  # unexpected; this case could only occur in `test_div_minimizer.R`):
  trms <- terms(projpred_formula_no_random, data = data)
  offs_attr <- attr(trms, "offset")
  if (length(offs_attr)) {
    # It is not clear whether mclogit::mblogit() supports offsets correctly (but
    # currently, offsets are unexpected here anyway):
    warning("Offsets for a multilevel submodel of a brms::categorical() ",
            "reference model are currently experimental.")
    mfr <- model.frame(formula = projpred_formula_no_random, data = data)
    offs_obj <- model.offset(mfr)
    if (is.null(dim(offs_obj))) {
      # Recycle the vector of offsets to a matrix with number of columns equal
      # to the number of response categories:
      resp_vec <- model.response(mfr)
      ncats <- length(unique(resp_vec))
      offs_mat <- matrix(offs_obj, nrow = nrow(data), ncol = ncats)
      offs_nm <- as.character(attr(trms, "variables")[[offs_attr + 1L]][[2]])
      data[[offs_nm]] <- offs_mat
    }
  }
  # Strip parentheses from group-level terms:
  random_trms <- labels(terms(projpred_random))
  if (utils::packageVersion("mclogit") < "0.9" && length(random_trms) > 1) {
    stop("'mclogit' versions < 0.9 can only handle a single group-level term.")
  }
  random_fmls <- lapply(random_trms, function(random_trm) {
    as.formula(paste("~", random_trm))
  })
  if (length(random_fmls) == 1) {
    random_fmls <- random_fmls[[1]]
  }
  # Call the submodel fitter:
  out_capt <- utils::capture.output({
    fitobj <- do.call(mclogit::mblogit, c(
      list(formula = projpred_formula_no_random,
           data = data,
           random = random_fmls,
           weights = weights,
           model = FALSE,
           x = FALSE,
           y = FALSE,
           method = "PQL"),
      dot_args
    ))
  })
  if (utils::tail(out_capt, 1) != "converged") {
    warning("The mclogit::mblogit() submodel fit did not converge.")
  }
  return(fitobj)
}

# Convergence checker -----------------------------------------------------

check_conv <- function(fit) {
  is_conv <- unlist(lapply(fit, function(fit_s) {
    if (inherits(fit_s, "gam")) {
      # TODO (GAMs): There is also `fit_s$mgcv.conv` (see `?mgcv::gamObject`).
      # Do we need to take this into account?
      return(fit_s$converged)
    } else if (inherits(fit_s, "gamm4")) {
      # TODO (GAMMs): Needs to be implemented. Return `TRUE` for now.
      return(TRUE)
    } else if (inherits(fit_s, c("lmerMod", "glmerMod"))) {
      # The following was inferred from the source code of lme4::checkConv() and
      # lme4::.prt.warn() (see also `?lme4::mkMerMod`).
      return(fit_s@optinfo$conv$opt == 0 && (
        # Since lme4::.prt.warn() does not refer to `optinfo$conv$lme4$code`,
        # that element might not always exist:
        (!is.null(fit_s@optinfo$conv$lme4$code) &&
           all(fit_s@optinfo$conv$lme4$code == 0)) ||
          is.null(fit_s@optinfo$conv$lme4$code)
      ) && length(unlist(fit_s@optinfo$conv$lme4$messages)) == 0 &&
        length(fit_s@optinfo$warnings) == 0)
    } else if (inherits(fit_s, "glm")) {
      return(fit_s$converged)
    } else if (inherits(fit_s, "lm")) {
      # Note: There doesn't seem to be a better way to check for convergence
      # other than checking `NA` coefficients (see below).
      return(all(!is.na(coef(fit_s))))
    } else if (inherits(fit_s, "subfit")) {
      # Note: There doesn't seem to be any way to check for convergence, so
      # return `TRUE` for now.
      # TODO (GLMs with ridge regularization): Add a logical indicating
      # convergence to objects of class `subfit` (i.e., from glm_ridge())?
      return(TRUE)
    } else {
      stop("Unrecognized submodel fit. Please notify the package maintainer.")
    }
  }))
  if (any(!is_conv)) {
    warning(sum(!is_conv), " out of ", length(is_conv), " submodel fits ",
            "(there is one submodel fit per projected draw) probably have not ",
            "converged (appropriately). It is recommended to inspect this in ",
            "detail and (if necessary) to adjust lme4's tuning parameters via ",
            "`...` or via a custom `divergence_minimizer` function. ",
            "Formula (right-hand side): ", update(formula(fit[[1]]), NULL ~ .))
  }
  return(invisible(TRUE))
}

# Prediction functions for submodels --------------------------------------

subprd <- function(fits, newdata) {
  prd_list <- lapply(fits, function(fit) {
    is_glmmPQL <- inherits(fit, "glmmPQL")
    is_glmm <- inherits(fit, c("lmerMod", "glmerMod"))
    is_gam_gamm <- inherits(fit, c("gam", "gamm4"))
    if (is_gam_gamm && !is.null(newdata)) {
      newdata <- cbind(`(Intercept)` = rep(1, NROW(newdata)), newdata)
    }
    if (is_glmmPQL) {
      ### TODO (glmmPQL): Remove this as soon as a repair_re.glmmPQL() method
      ### has been added:
      if (!is.null(newdata)) {
        has_new_grps <- sapply(names(fit$groups), function(grp_nm) {
          any(!unique(newdata[[grp_nm]]) %in% unique(fit$groups[[grp_nm]]))
        })
        if (any(has_new_grps)) {
          stop("Under construction (a repair_re.glmmPQL() method needs to be ",
               "added to projpred.")
        }
      }
      ###
      if ("projpred_internal_dummy1s_PQL" %in% names(fit$data) &&
          !is.null(newdata)) {
        if ("projpred_internal_dummy1s_PQL" %in% names(newdata)) {
          stop("Need to write to column `projpred_internal_dummy1s_PQL` of ",
               "`newdata`, but that column already exists. Please rename this ",
               "column in `newdata` and try again.")
        }
        newdata$projpred_internal_dummy1s_PQL <- factor(1)
      }
      return(
        predict(fit, newdata = newdata)
        ### TODO (glmmPQL): Add a repair_re.glmmPQL() method for this:
        # predict(fit, newdata = newdata, level = 0) +
        #   repair_re(fit, newdata = newdata)
        ###
      )
    } else if (is_glmm) {
      if (getOption("projpred.mlvl_pred_new", FALSE)) {
        re_fml_predict <- ~0
      } else {
        re_fml_predict <- NULL
      }
      return(
        predict(fit, newdata = newdata, re.form = re_fml_predict,
                allow.new.levels = TRUE) +
          repair_re(fit, newdata = newdata)
      )
    } else {
      return(predict(fit, newdata = newdata))
    }
  })
  return(do.call(cbind, prd_list))
}

subprd_augdat <- function(fits, newdata) {
  if (inherits(fits[[1]], "clmm")) {
    y_nm <- extract_terms_response(formula(fits[[1]]))$response
  }
  prd_list <- lapply(fits, function(fit) {
    # Basic predictions (if possible, these should be already on link scale; if
    # this is not possible, the transformation to link scale has to follow):
    if (inherits(fit, "polr")) {
      prbs <- predict(fit, newdata = newdata, type = "probs")
      if (nrow(newdata) == 1) {
        prbs <- t(prbs)
      }
      link_nm <- fit$method
    } else if (inherits(fit, "clmm")) {
      # A predict() method for objects of class `clmm` does not seem to exist,
      # so use a workaround based on ordinal:::predict.clm().
      # Note: There is also `type = "linear.predictor"`, but that returns the
      # predictions for each j and j - 1 (with j = 1, ..., C_cat indexing the
      # response categories), so performs redundant calculations.
      prbs <- predict(structure(fit, class = c(oldClass(fit), "clm")),
                      # Remove the response so ordinal:::predict.clm() returns
                      # predictions for *all* response categories:
                      newdata = within(newdata, assign(y_nm, NULL)),
                      type = "prob")$fit
      link_nm <- fit$link
    } else if (inherits(fit, "multinom")) {
      prbs <- predict(fit, newdata = newdata, type = "probs")
      if (nrow(newdata) == 1) {
        prbs <- t(prbs)
      }
    } else if (inherits(fit, "mmblogit")) {
      # Note: `conditional = FALSE` is used here because
      # mclogit:::predict.mmblogit() doesn't work for new group levels. Instead,
      # repair_re() handles both, existing and new group levels.
      lpreds <- predict(fit, newdata = newdata, conditional = FALSE) +
        repair_re(fit, newdata = newdata)
    } else {
      stop("Unknown `fit`.")
    }
    # Where necessary, transform probabilities to link scale (i.e., to linear
    # predictors):
    if (inherits(fit, c("polr", "clmm"))) {
      prbs <- do.call(rbind, apply(prbs, 1, cumsum, simplify = FALSE))
      prbs <- prbs[, -ncol(prbs), drop = FALSE]
      lpreds <- linkfun_raw(prbs, link_nm = link_nm)
      if (inherits(fit, "clmm")) {
        lpreds <- lpreds + repair_re(fit, newdata = newdata)
      }
    } else if (inherits(fit, "multinom")) {
      lpreds <- log(sweep(prbs[, -1, drop = FALSE], 1, prbs[, 1], "/"))
    }
    return(lpreds)
  })
  return(do.call(abind::abind, list(prd_list, rev.along = 0)))
}

subprd_augdat_binom <- function(fits, newdata) {
  augprd <- subprd(fits, newdata = newdata)
  return(array(augprd, dim = c(nrow(augprd), 1L, ncol(augprd))))
}

## FIXME: find a way that allows us to remove this
predict.subfit <- function(object, newdata = NULL, ...) {
  beta <- object$beta
  alpha <- object$alpha
  if (is.null(newdata)) {
    if (is.null(beta)) {
      return(as.matrix(rep(alpha, nrow(object$x))))
    } else {
      return(cbind(1, object$x) %*% rbind(alpha, beta))
    }
  } else {
    if (is.null(beta)) {
      return(as.matrix(rep(alpha, nrow(newdata))))
    } else {
      # TODO: In the following model.matrix() call, allow user-specified
      # contrasts to be passed to argument `contrasts.arg`. The `contrasts.arg`
      # default (`NULL`) uses `options("contrasts")` internally, but it might be
      # more convenient to let users specify contrasts directly. At that
      # occasion, contrasts should also be tested thoroughly (not done until
      # now).
      x <- model.matrix(delete.response(terms(object$formula)), data = newdata,
                        xlev = object$xlvls)
      # Need to ensure that the columns of `x` match the coefficients (note that
      # `rownames(beta)` is the same as `colnames(object$x)`, see the tests):
      nms_to_use <- c("(Intercept)", colnames(object$x))
      nms_to_use <- reorder_ias(nms_to_use, colnames(x))
      if (all(!is.na(nms_to_use)) && all(nms_to_use %in% colnames(x))) {
        x <- x[, nms_to_use, drop = FALSE]
      } else {
        stop("The column names of the new model matrix don't match the ",
             "column names of the original model matrix. Please notify the ",
             "package maintainer.")
      }
      return(x %*% rbind(alpha, beta))
    }
  }
}

predict.gamm4 <- function(object, newdata = NULL, ...) {
  if (is.null(newdata)) {
    newdata <- model.frame(object$mer)
  }
  formula <- object$formula
  random <- object$random
  gamm_struct <- model.matrix_gamm4(delete.response(terms(formula)),
                                    random = random, data = newdata)
  ranef <- lme4::ranef(object$mer) # TODO (GAMMs): Add `, condVar = FALSE` here?
  b <- gamm_struct$b
  mf <- gamm_struct$mf

  ## base pred only smooth and fixed effects
  gamm_pred <- predict(object$mer, newdata = mf, re.form = NA)

  ## gamm4 trick to replace dummy smooth variables with actual smooth terms
  sn <- names(ranef)
  tn <- names(b$reTrms$cnms)
  ind <- seq_along(tn)
  for (i in seq_along(tn)) { ## loop through random effect smooths
    k <- ind[sn[i] == tn] ## which term should contain G$random[[i]]
    ii <- (b$reTrms$Gp[k] + 1):b$reTrms$Gp[k + 1]
    r_pred <- t(as.matrix(b$reTrms$Zt[ii, ])) %*%
      as.matrix(c(as.matrix(ranef[[i]])))
    gamm_pred <- gamm_pred + r_pred
  }
  return(as.matrix(unname(gamm_pred)))
}

## Random-effects adjustments ---------------------------------------------

empty_intersection <- function(x, el_nm = "new") {
  length(intersect(x[[1]][[el_nm]], x[[2]][[el_nm]])) == 0
}

# License/copyright notice: mkNewReTrms_man() is strongly based on (i.e., it was
# only slightly modified from) lme4::mkNewReTrms() from lme4 version 1.1-28 (see
# <https://CRAN.R-project.org/package=lme4>).
#
# The copyright statement for lme4 version 1.1-28 is:
# Copyright (C) 2003-2022 The LME4 Authors (see
# <https://CRAN.R-project.org/package=lme4>).
#
# The license of lme4 version 1.1-28 is:
# "GPL (>=2)" (see <https://CRAN.R-project.org/package=lme4>).
mkNewReTrms_man <- function(re.form, newdata, xlevels, re, D = NULL) {
  stopifnot(!is.null(newdata))
  tt <- terms(lme4::subbars(re.form))
  rfd <- suppressWarnings(
    model.frame(tt, newdata, na.action = na.pass, xlev = xlevels)
  )
  ReTrms <- lme4::mkReTrms(lme4::findbars(re.form[[2]]), rfd)
  ns.re <- names(re)
  nRnms <- names(Rcnms <- ReTrms$cnms)
  if (!all(nRnms %in% ns.re)) {
    stop("grouping factors specified in re.form that were not present in ",
         "original model")
  }
  if (!is.null(D)) {
    # categorical() case
    Zt_old_rnms <- rownames(ReTrms$Zt)
    ReTrms$Zt <- as(as.matrix(ReTrms$Zt %x% t(D[-1, , drop = FALSE])),
                    class(ReTrms$Zt))
    rownames(ReTrms$Zt) <- rep(Zt_old_rnms, each = ncol(D))
    for (vnm in nRnms) {
      ReTrms$cnms[[vnm]] <- names(re[[vnm]])
    }
    Rcnms <- ReTrms$cnms
  }
  new_levels <- lapply(ReTrms$flist, function(x) levels(factor(x)))
  re_x <- Map(
    function(r, n) {
      ("lme4" %:::% "levelfun")(r, n, allow.new.levels = TRUE)
    },
    re[names(new_levels)],
    new_levels
  )
  get_re <- function(rname, cnms) {
    nms <- names(re[[rname]])
    miss_names <- setdiff(cnms, nms)
    if (length(miss_names) > 0) {
      stop("random effects specified in re.form that were not present in ",
           "original model ", paste(miss_names, collapse = ", "))
    }
    t(re_x[[rname]][, cnms])
  }
  re_new <- unlist(Map(get_re, nRnms, Rcnms))
  Zt <- ReTrms$Zt
  attr(Zt, "na.action") <- attr(re_new, "na.action") <- NULL
  list(Zt = Zt, b = re_new)
}

repair_re <- function(object, newdata) {
  UseMethod("repair_re")
}

# For objects of class `merMod`, the following repair_re() method will draw the
# random effects for new group levels from a (multivariate) Gaussian
# distribution, but option `projpred.mlvl_pred_new` determines whether existing
# group levels are also considered as new group levels.
#
# License/copyright notice: repair_re.merMod() is inspired by and uses code
# snippets from lme4:::predict.merMod() from lme4 version 1.1-28 (see
# <https://CRAN.R-project.org/package=lme4>). See the `LICENSE` file in
# projpred's root directory for details.
#
# The copyright statement for lme4 version 1.1-28 is:
# Copyright (C) 2003-2022 The LME4 Authors (see
# <https://CRAN.R-project.org/package=lme4>).
#
# The license of lme4 version 1.1-28 is:
# "GPL (>=2)" (see <https://CRAN.R-project.org/package=lme4>).
#' @noRd
#' @export
repair_re.merMod <- function(object, newdata) {
  stopifnot(!is.null(newdata))
  ranef_tmp <- lme4::ranef(object, condVar = FALSE)
  vnms <- names(ranef_tmp)
  lvls_list <- lapply(setNames(nm = vnms), function(vnm) {
    from_fit <- rownames(ranef_tmp[[vnm]])
    if (!vnm %in% names(newdata)) {
      for (special_char in c("/", ":")) {
        if (any(grepl(paste0("\\|.+", special_char),
                      labels(terms(formula(object)))))) {
          if (special_char == "/") {
            add_hint <- paste0(
              " implied by the `", special_char, "` syntax (see lme4 vignette ",
              "\"Fitting Linear Mixed-Effects Models Using lme4\", Table 2)"
            )
          } else {
            add_hint <- ""
          }
          stop("The `", special_char, "` syntax for grouping variables is ",
               "currently not supported. Please write out (i.e., create ",
               "corresponding columns in the dataset manually and then adapt ",
               "the model formula) interaction terms", add_hint, ".")
        }
      }
      stop("Could not find column `", vnm, "` in `newdata`.")
    }
    from_new <- unique(newdata[, vnm])
    if (is.factor(from_new)) {
      # Strictly speaking, this is not necessary (currently), but include it for
      # safety reasons, in case downstream code is changed in the future (or in
      # case the behavior of `factor`s in R is changed in general):
      from_new <- as.character(from_new)
    }
    list(comb = union(from_fit, from_new),
         exist = intersect(from_new, from_fit),
         new = from_new)
  })
  # In case of duplicated levels across group variables, later code would have
  # to be adapted:
  if (getOption("projpred.mlvl_pred_new", FALSE)) {
    el_nm_mer <- "new"
  } else {
    el_nm_mer <- "comb"
  }
  if (length(lvls_list) >= 2 &&
      !all(utils::combn(lvls_list, 2, empty_intersection, el_nm = el_nm_mer))) {
    stop("Currently, projpred requires all variables with group-level effects ",
         "to have disjoint level sets.")
  }
  re_fml <- ("lme4" %:::% "reOnly")(formula(object))
  # Note: Calling lme4::mkNewReTrms() with `re.form = NULL` fails.
  ranefs_prep <- lme4::mkNewReTrms(object,
                                   newdata = newdata,
                                   re.form = re_fml,
                                   allow.new.levels = TRUE)
  names(ranefs_prep$b) <- rownames(ranefs_prep$Zt)

  VarCorr_tmp <- lme4::VarCorr(object)
  for (vnm in vnms) {
    lvls_new <- lvls_list[[vnm]]$new
    if (!getOption("projpred.mlvl_pred_new", FALSE)) {
      lvls_exist <- lvls_list[[vnm]]$exist
      lvls_new <- setdiff(lvls_new, lvls_exist)
      ranefs_prep$b[names(ranefs_prep$b) %in% lvls_exist] <- 0
    }
    if (length(lvls_new) > 0) {
      ranefs_prep$b[names(ranefs_prep$b) %in% lvls_new] <- t(mvtnorm::rmvnorm(
        n = length(lvls_new),
        # Add `[, , drop = FALSE]` to drop attributes:
        sigma = VarCorr_tmp[[vnm]][, , drop = FALSE],
        checkSymmetry = FALSE
      ))
    }
  }
  return(drop(as(ranefs_prep$b %*% ranefs_prep$Zt, "matrix")))
}

# For objects of class `clmm`, the following repair_re() method will re-use the
# estimated random effects for existing group levels and will draw the random
# effects for new group levels from a (multivariate) Gaussian distribution, but
# option `projpred.mlvl_pred_new` determines whether existing group levels are
# also considered as new group levels.
#
# License/copyright notice: repair_re.clmm() is inspired by and uses code
# snippets from lme4:::predict.merMod() from lme4 version 1.1-28 (see
# <https://CRAN.R-project.org/package=lme4>). See the `LICENSE` file in
# projpred's root directory for details.
#
# The copyright statement for lme4 version 1.1-28 is:
# Copyright (C) 2003-2022 The LME4 Authors (see
# <https://CRAN.R-project.org/package=lme4>).
#
# The license of lme4 version 1.1-28 is:
# "GPL (>=2)" (see <https://CRAN.R-project.org/package=lme4>).
#' @noRd
#' @export
repair_re.clmm <- function(object, newdata) {
  stopifnot(!is.null(newdata))
  ranef_tmp <- ordinal::ranef(object)
  vnms <- names(ranef_tmp)
  lvls_list <- lapply(setNames(nm = vnms), function(vnm) {
    from_fit <- rownames(ranef_tmp[[vnm]])
    if (!vnm %in% names(newdata)) {
      for (special_char in c("/", ":")) {
        if (any(grepl(paste0("\\|.+", special_char),
                      labels(terms(formula(object)))))) {
          if (special_char == "/") {
            add_hint <- paste0(
              " implied by the `", special_char, "` syntax (see lme4 vignette ",
              "\"Fitting Linear Mixed-Effects Models Using lme4\", Table 2)"
            )
          } else {
            add_hint <- ""
          }
          stop("The `", special_char, "` syntax for grouping variables is ",
               "currently not supported. Please write out (i.e., create ",
               "corresponding columns in the dataset manually and then adapt ",
               "the model formula) interaction terms", add_hint, ".")
        }
      }
      stop("Could not find column `", vnm, "` in `newdata`.")
    }
    from_new <- unique(newdata[, vnm])
    if (is.factor(from_new)) {
      # Strictly speaking, this is not necessary (currently), but include it for
      # safety reasons, in case downstream code is changed in the future (or in
      # case the behavior of `factor`s in R is changed in general):
      from_new <- as.character(from_new)
    }
    list(comb = union(from_fit, from_new),
         exist = intersect(from_new, from_fit),
         new = from_new)
  })
  # In case of duplicated levels across group variables, later code would have
  # to be adapted:
  if (length(lvls_list) >= 2 &&
      !all(utils::combn(lvls_list, 2, empty_intersection))) {
    stop("Currently, projpred requires all variables with group-level effects ",
         "to have disjoint level sets.")
  }
  re_fml <- ("lme4" %:::% "reOnly")(formula(object))
  ranefs_prep <- mkNewReTrms_man(re.form = re_fml,
                                 newdata = newdata,
                                 xlevels = c(lapply(lvls_list, "[[", "comb"),
                                             object$xlevels),
                                 re = ranef_tmp)
  names(ranefs_prep$b) <- rownames(ranefs_prep$Zt)

  VarCorr_tmp <- ordinal::VarCorr(object)
  for (vnm in vnms) {
    lvls_new <- lvls_list[[vnm]]$new
    if (!getOption("projpred.mlvl_pred_new", FALSE)) {
      lvls_exist <- lvls_list[[vnm]]$exist
      lvls_new <- setdiff(lvls_new, lvls_exist)
    }
    if (length(lvls_new) > 0) {
      ranefs_prep$b[names(ranefs_prep$b) %in% lvls_new] <- t(mvtnorm::rmvnorm(
        n = length(lvls_new),
        # Add `[, , drop = FALSE]` to drop attributes:
        sigma = VarCorr_tmp[[vnm]][, , drop = FALSE],
        checkSymmetry = FALSE
      ))
    }
  }
  return(-drop(as(ranefs_prep$b %*% ranefs_prep$Zt, "matrix")))
}

# For objects of class `mmblogit`, the following repair_re() method will re-use
# the estimated random effects for existing group levels and will draw the
# random effects for new group levels from a (multivariate) Gaussian
# distribution, but option `projpred.mlvl_pred_new` determines whether existing
# group levels are also considered as new group levels.
#
# License/copyright notice: repair_re.mmblogit() is inspired by and uses code
# snippets from lme4:::predict.merMod() from lme4 version 1.1-28 (see
# <https://CRAN.R-project.org/package=lme4>). See the `LICENSE` file in
# projpred's root directory for details.
#
# The copyright statement for lme4 version 1.1-28 is:
# Copyright (C) 2003-2022 The LME4 Authors (see
# <https://CRAN.R-project.org/package=lme4>).
#
# The license of lme4 version 1.1-28 is:
# "GPL (>=2)" (see <https://CRAN.R-project.org/package=lme4>).
#' @noRd
#' @export
repair_re.mmblogit <- function(object, newdata) {
  stopifnot(!is.null(newdata))
  vnms <- names(object$groups)
  stopifnot(length(vnms) == length(object$random.effects))
  if (utils::packageVersion("mclogit") < "0.9") {
    stopifnot(length(vnms) == 1)
  } else if (length(vnms) < length(object$random)) {
    stop("The length of `<mmblogit_object>$random` is greater than that of ",
         "`vnms = c(\"", paste(vnms, collapse = "\", \""), "\")`.")
  }
  # The number of latent response categories:
  nlats <- ncol(object$D)
  # Coerce the random effects into the same format as the output of ranef() from
  # packages 'lme4' and 'ordinal':
  ranef_tmp <- lapply(
    setNames(seq_along(object$random.effects), vnms),
    function(grp_idx) {
      if (utils::packageVersion("mclogit") < "0.9") {
        obj_rand <- object$random
        VarCov_idx <- 1
      } else {
        if (length(object$random) < grp_idx) {
          stop("Unexpected length of `<mmblogit_object>$random`.")
        }
        obj_rand <- object$random[[grp_idx]]
        VarCov_idx <- vnms[grp_idx]
      }
      if (length(obj_rand$groups) > 1) {
        stop("It seems like you fitted a model with a nested group-level ",
             "term. Currently, these are not supported.")
      }
      # The number of "random slopes", including the intercept (the intercept is
      # the reason for `+ 1L`):
      ncoefs <- length(all.vars(obj_rand$formula)) + 1L
      # The coercion itself:
      ranef_i <- matrix(
        object$random.effects[[grp_idx]],
        nrow = nlats * ncoefs,
        ncol = nlevels(object$groups[[vnms[grp_idx]]]),
        dimnames = list(colnames(object$VarCov[[VarCov_idx]]),
                        levels(object$groups[[vnms[grp_idx]]]))
      )
      return(as.data.frame(t(ranef_i)))
    }
  )
  lvls_list <- lapply(setNames(nm = vnms), function(vnm) {
    from_fit <- levels(object$groups[[vnm]])
    if (!vnm %in% names(newdata)) {
      stop("Could not find column `", vnm, "` in `newdata`.")
    }
    from_new <- unique(newdata[, vnm])
    if (is.factor(from_new)) {
      # Strictly speaking, this is not necessary (currently), but include it for
      # safety reasons, in case downstream code is changed in the future (or in
      # case the behavior of `factor`s in R is changed in general):
      from_new <- as.character(from_new)
    }
    list(comb = union(from_fit, from_new),
         exist = intersect(from_new, from_fit),
         new = from_new)
  })
  # In case of duplicated levels across group variables, later code would have
  # to be adapted:
  if (length(lvls_list) >= 2 &&
      !all(utils::combn(lvls_list, 2, empty_intersection))) {
    stop("Currently, projpred requires all variables with group-level effects ",
         "to have disjoint level sets.")
  }
  # Create the lme4-type random-effects formula needed by mkNewReTrms_man():
  if (utils::packageVersion("mclogit") < "0.9") {
    re_fml <- update(object$random$formula,
                     paste("~ . |", object$random$groups))
  } else {
    for (grp_idx in seq_along(object$random)) {
      re_fml_i <- update(object$random[[grp_idx]]$formula,
                         paste("~ . |", object$random[[grp_idx]]$groups))
      if (grp_idx == 1) {
        re_fml <- re_fml_i
      } else {
        re_fml <- update(re_fml, paste("~ . +", as.character(re_fml_i[2])))
      }
    }
  }
  # Get random effects for existing group levels (and zero for new group levels)
  # as well as the random-effects model matrix:
  ranefs_prep <- mkNewReTrms_man(re.form = re_fml,
                                 newdata = newdata,
                                 xlevels = c(lapply(lvls_list, "[[", "comb"),
                                             object$xlevels),
                                 re = ranef_tmp,
                                 D = object$D)
  names(ranefs_prep$b) <- rownames(ranefs_prep$Zt)

  VarCorr_tmp <- object$VarCov
  if (utils::packageVersion("mclogit") < "0.9") {
    VarCorr_tmp <- setNames(VarCorr_tmp, vnms)
  }
  for (vnm in vnms) {
    lvls_new <- lvls_list[[vnm]]$new
    if (!getOption("projpred.mlvl_pred_new", FALSE)) {
      lvls_exist <- lvls_list[[vnm]]$exist
      lvls_new <- setdiff(lvls_new, lvls_exist)
    }
    if (length(lvls_new) > 0) {
      ranefs_prep$b[names(ranefs_prep$b) %in% lvls_new] <- t(mvtnorm::rmvnorm(
        n = length(lvls_new),
        sigma = VarCorr_tmp[[vnm]],
        checkSymmetry = FALSE
      ))
    }
  }
  re_vec <- drop(as(ranefs_prep$b %*% ranefs_prep$Zt, "matrix"))
  re_mat <- matrix(re_vec, nrow = nlats, ncol = nrow(newdata),
                   dimnames = list(colnames(object$D), NULL))
  return(t(re_mat))
}
