R/restructure.R

In brms: Bayesian Regression Models using 'Stan'

#' Restructure Old \code{brmsfit} Objects
#'
#' Restructure old \code{brmsfit} objects to work with
#' the latest \pkg{brms} version. This function is called
#' internally when applying post-processing methods.
#' However, in order to avoid unnecessary run time caused
#' by the restructuring, I recommend explicitly calling
#' \code{restructure} once per model after updating \pkg{brms}.
#'
#' @param x An object of class \code{brmsfit}.
#' @param ... Currently ignored.
#'
#' @details
#' If you are restructuring an old spline model (fitted with brms < 2.19.3) to
#' avoid prediction inconsistencies between machines (see GitHub issue #1465),
#' please make sure to \code{restructure} your model on the machine on which it
#' was originally fitted.
#'
#' @return A \code{brmsfit} object compatible with the latest version
#'   of \pkg{brms}.
#'
#' @export
restructure <- function(x, ...) {
  stopifnot(is.brmsfit(x))
  if (is.null(x$version)) {
    # this is the latest version without saving the version number
    x$version <- list(brms = package_version("0.9.1"))
  } else if (is.package_version(x$version)) {
    # also added the rstan version in brms 1.5.0
    x$version <- list(brms = x$version)
  }
  current_version <- utils::packageVersion("brms")
  restr_version <- restructure_version(x)
  if (restr_version >= current_version) {
    # object is up to date with the current brms version
    return(x)
  }
  if (restr_version < "2.0.0") {
    x <- restructure_v1(x)
  }
  if (restr_version < "3.0.0") {
    x <- restructure_v2(x)
  }
  # remember the version with which the object was restructured
  x$version$restructure <- current_version
  # remove unused attribute
  attr(x, "restructured") <- NULL
  x
}

restructure_v2 <- function(x) {
  # restructure models fitted with brms 2.x
  x$formula <- update_old_family(x$formula)
  bterms <- SW(brmsterms(x$formula))
  pars <- variables(x)
  version <- restructure_version(x)
  if (version < "2.1.2") {
    x <- do_renaming(x, rename_old_bsp(pars))
  }
  if (version < "2.1.3") {
    if ("weibull" %in% family_names(x)) {
      stop_parameterization_changed("weibull", "2.1.3")
    }
  }
  if (version < "2.1.8") {
    if ("exgaussian" %in% family_names(x)) {
      stop_parameterization_changed("exgaussian", "2.1.8")
    }
  }
  if (version < "2.1.9") {
    # reworked 'me' terms (#372)
    meef <- tidy_meef(bterms, model.frame(x))
    if (isTRUE(nrow(meef) > 0)) {
      warning2(
        "Measurement error ('me') terms have been reworked ",
        "in version 2.1.9. I strongly recommend refitting your ",
        "model with the latest version of brms."
      )
    }
  }
  if (version < "2.2.4") {
    # added 'dist' argument to grouping terms
    x$ranef <- tidy_ranef(bterms, model.frame(x))
  }
  if (version < "2.3.7") {
    check_old_nl_dpars(bterms)
  }
  if (version < "2.8.3") {
    # argument 'sparse' is now specified within 'formula'
    sparse <- if (grepl("sparse matrix", stancode(x))) TRUE
    x$formula <- SW(validate_formula(x$formula, data = x$data, sparse = sparse))
  }
  if (version < "2.8.4") {
    x <- rescale_old_mo(x)
  }
  if (version < "2.8.5") {
    if (any(grepl("^arr(\\[|_|$)", pars))) {
      warning2("ARR structures are no longer supported.")
    }
  }
  if (version < "2.8.6") {
    # internal handling of special effects terms has changed
    # this requires updating the 'terms' attribute of the data
    x$data <- rm_attr(x$data, c("brmsframe", "terms"))
    x$data <- validate_data(x$data, bterms)
  }
  if (version < "2.8.9") {
    if (any(grepl("^loclev(\\[|_|$)", pars))) {
      warning2("BSTS structures are no longer supported.")
    }
  }
  if (version < "2.10.4") {
    # model fit criteria have been moved to x$criteria
    criterion_names <- c("loo", "waic", "kfold", "R2", "marglik")
    criteria <- x[intersect(criterion_names, names(x))]
    x[criterion_names] <- NULL
    # rename 'R2' to 'bayes_R2' according to #793
    names(criteria)[names(criteria) == "R2"] <- "bayes_R2"
    x$criteria <- criteria
  }
  if (version < "2.10.5") {
    # new slot 'thres' stored inside ordinal families
    if (is_ordinal(x$formula)) {
      x$formula <- SW(validate_formula(x$formula, data = x$data))
    }
  }
  if (version < "2.11.2") {
    # 'autocor' was integrated into the formula interface
    x$formula <- SW(validate_formula(x$formula))
    x$data2 <- validate_data2(
      data2 = list(), bterms = bterms,
      get_data2_autocor(x$formula)
    )
  }
  if (version < "2.11.3") {
    # ordering after IDs matches the order of the posterior draws
    # if multiple IDs are used for the same grouping factor (#835)
    x$ranef <- x$ranef[order(x$ranef$id), , drop = FALSE]
  }
  if (version < "2.11.5") {
    # 'cats' is stored inside ordinal families again
    if (is_ordinal(x$formula)) {
      x$formula <- SW(validate_formula(x$formula, data = x$data))
    }
  }
  if (version < "2.12.5") {
    # 'cov_ranef' was integrated into the formula interface
    if (length(x$cov_ranef)) {
      x$formula <- SW(validate_formula(x$formula, cov_ranef = x$cov_ranef))
      cov_ranef <- get_data2_cov_ranef(x$formula)
      x$data2[names(cov_ranef)] <- cov_ranef
    }
  }
  if (version < "2.12.6") {
    # minor structural changes as part of internal interface improvements
    attr(x$data, "data_name") <- x$data.name
    x$stanvars <- SW(validate_stanvars(x$stanvars, stan_funs = x$stan_funs))
  }
  if (version < "2.12.11") {
    # argument 'position' was added to stanvars
    for (i in seq_along(x$stanvars)) {
      x$stanvars[[i]]$position <- "start"
    }
  }
  if (version < "2.13.2") {
    # added support for 'cmdstanr' as additional backend
    x$backend <- "rstan"
  }
  if (version < "2.13.5") {
    # see issue #962 for discussion
    if ("cox" %in% family_names(x)) {
      stop_parameterization_changed("cox", "2.13.5")
    }
  }
  if (version < "2.13.8") {
    x$prior$source <- ""
    # ensure correct ordering of columns
    cols_prior <- intersect(all_cols_prior(), names(x$prior))
    x$prior <- x$prior[, cols_prior]
  }
  if (version < "2.13.10") {
    # added support for threading
    x$threads <- threading()
  }
  if (version < "2.13.12") {
    # added more control over which parameters to save
    save_ranef <- isTRUE(attr(x$exclude, "save_ranef"))
    save_mevars <- isTRUE(attr(x$exclude, "save_mevars"))
    save_all_pars <- isTRUE(attr(x$exclude, "save_all_pars"))
    x$save_pars <- SW(validate_save_pars(
      save_pars(), save_ranef = save_ranef,
      save_mevars = save_mevars,
      save_all_pars = save_all_pars
    ))
    x$exclude <- NULL
  }
  if (version < "2.15.6") {
    # added support for OpenCL
    x$opencl <- opencl()
  }
  if (version < "2.16.1") {
    # problems with rstan::read_stan_csv as well as
    # non-unique variable names became apparent (#1218)
    x$fit <- repair_stanfit(x$fit)
  }
  if (version < "2.16.12") {
    # added full user control over parameter boundaries (#1324)
    # explicit bounds need to be added to old priors as a result
    x$prior$ub <- x$prior$lb <- NA
    for (i in which(nzchar(x$prior$bound))) {
      bounds <- convert_stan2bounds(x$prior$bound[i], default = c("", ""))
      x$prior[i, c("lb", "ub")] <- bounds
    }
    x$prior$bound <- NULL
    all_priors <- get_prior(x$formula, x$data, data2 = x$data2, internal = TRUE)
    # checking for lb is sufficient because both bounds are NA at the same time
    which_needs_bounds <- which(is.na(x$prior$lb) & !nzchar(x$prior$coef))
    for (i in which_needs_bounds) {
      # take the corresponding bounds from the default prior
      prior_sub_i <- rbind(x$prior[i, ], all_priors)
      prior_sub_i <- prior_sub_i[duplicated(prior_sub_i), ]
      # should always have exactly one row but still check whether it has
      # any rows at all to prevent things from breaking accidentally
      if (NROW(prior_sub_i)) {
        x$prior[i, c("lb", "ub")] <- prior_sub_i[1, c("lb", "ub")]
      } else {
        x$prior[i, c("lb", "ub")] <- ""
      }
    }
    x$prior$lb[is.na(x$prior$lb)] <- x$prior$ub[is.na(x$prior$ub)] <- ""
    x$prior <- move2end(x$prior, "source")
  }
  if (version < "2.17.6") {
    # a slot was added that stores additional control arguments
    # that are directly passed to the Stan backends for later reuse (#1373)
    x$stan_args <- list()
  }
  if (version < "2.19.3") {
    # a slot was added to store parts of the Stan data computed at fitting time.
    # storing this is strictly required only for spline models but there it is
    # critical due to the machine-specific output of SVD (#1465)
    bterms <- brmsterms(x$formula)
    x$basis <- standata_basis(bterms, data = x$data)
  }
  x
}

# restructure models fitted with brms 1.x
restructure_v1 <- function(x) {
  version <- restructure_version(x)
  if (version < "1.0.0") {
    warning2(
      "Models fitted with brms < 1.0 are no longer offically ",
      "supported and post-processing them may fail. I recommend ",
      "refitting the model with the latest version of brms."
    )
  }
  x$formula <- restructure_formula_v1(formula(x), x$nonlinear)
  x$formula <- SW(validate_formula(
    formula(x), data = model.frame(x), family = family(x),
    autocor = x$autocor, threshold = x$threshold
  ))
  x$nonlinear <- x$partial <- x$threshold <- NULL
  bterms <- brmsterms(formula(x))
  x$data <- rm_attr(x$data, "brmsframe")
  x$data <- validate_data(x$data, bterms)
  x$ranef <- tidy_ranef(bterms, model.frame(x))
  if ("prior_frame" %in% class(x$prior)) {
    class(x$prior) <- c("brmsprior", "data.frame")
  }
  if (is(x$autocor, "cov_fixed")) {
    # deprecated as of brms 1.4.0
    class(x$autocor) <- "cor_fixed"
  }
  if (version < "0.10.1") {
    if (length(bterms$dpars$mu$nlpars)) {
      # nlpar and group have changed positions
      change <- rename_old_re(x$ranef, variables(x), x$fit@sim$dims_oi)
      x <- do_renaming(x, change)
    }
  }
  if (version < "1.0.0") {
    # double underscores were added to group-level parameters
    change <- rename_old_re2(x$ranef, variables(x), x$fit@sim$dims_oi)
    x <- do_renaming(x, change)
  }
  if (version < "1.0.1.1") {
    # names of spline parameters had to be changed after
    # allowing for multiple covariates in one spline term
    change <- rename_old_sm(
      bterms, model.frame(x), variables(x), x$fit@sim$dims_oi
    )
    x <- do_renaming(x, change)
  }
  if (version < "1.8.0.1") {
    att <- attributes(x$exclude)
    if (is.null(att$save_ranef)) {
      attr(x$exclude, "save_ranef") <-
        any(grepl("^r_", variables(x))) || !nrow(x$ranef)
    }
    if (is.null(att$save_mevars)) {
      attr(x$exclude, "save_mevars") <-
        any(grepl("^Xme_", variables(x)))
    }
  }
  if (version < "1.8.0.2") {
    x$prior$resp <- x$prior$dpar <- ""
    # ensure correct ordering of columns
    cols_prior <- intersect(all_cols_prior(), names(x$prior))
    x$prior <- x$prior[, cols_prior]
  }
  if (version < "1.9.0.4") {
    # names of monotonic parameters had to be changed after
    # allowing for interactions in monotonic terms
    change <- rename_old_mo(bterms, x$data, pars = variables(x))
    x <- do_renaming(x, change)
  }
  if (version >= "1.0.0" && version < "2.0.0") {
    change <- rename_old_categorical(bterms, x$data, pars = variables(x))
    x <- do_renaming(x, change)
  }
  x
}

# get version with which a brmsfit object was restructured
restructure_version <- function(x) {
  stopifnot(is.brmsfit(x))
  out <- x$version$restructure
  if (!is.package_version(out)) {
    # models restructured with brms 2.11.1 store it as an attribute
    out <- attr(x, "restructured", exact = TRUE)
  }
  if (!is.package_version(out)) {
    out <- x$version$brms
  }
  out
}

# convert old model formulas to brmsformula objects
restructure_formula_v1 <- function(formula, nonlinear = NULL) {
  if (is.brmsformula(formula) && is.formula(formula)) {
    # convert deprecated brmsformula objects back to formula
    class(formula) <- "formula"
  }
  if (is.brmsformula(formula)) {
    # already up to date
    return(formula)
  }
  old_nonlinear <- attr(formula, "nonlinear")
  nl <- length(nonlinear) > 0
  if (is.logical(old_nonlinear)) {
    nl <- nl || old_nonlinear
  } else if (length(old_nonlinear)) {
    nonlinear <- c(nonlinear, old_nonlinear)
    nl <- TRUE
  }
  out <- structure(nlist(formula), class = "brmsformula")
  old_forms <- rmNULL(attributes(formula)[old_dpars()])
  old_forms <- c(old_forms, nonlinear)
  out$pforms[names(old_forms)] <- old_forms
  bf(out, nl = nl)
}

# parameters to be restructured in old brmsformula objects
old_dpars <- function() {
  c("mu", "sigma", "shape", "nu", "phi", "kappa", "beta", "xi",
    "zi", "hu", "zoi", "coi", "disc", "bs", "ndt", "bias",
    "quantile", "alpha", "theta")
}

# interchanges group and nlpar in names of group-level parameters
# required for brms <= 0.10.0.9000
# @param ranef output of tidy_ranef
# @param pars names of all parameters in the model
# @param dims dimension of parameters
# @return a list whose elements can be interpreted by do_renaming
rename_old_re <- function(ranef, pars, dims) {
  out <- list()
  for (id in unique(ranef$id)) {
    r <- subset2(ranef, id = id)
    g <- r$group[1]
    nlpar <- r$nlpar[1]
    stopifnot(nzchar(nlpar))
    # rename sd-parameters
    old_sd_names <- paste0("sd_", nlpar, "_", g, "_", r$coef)
    new_sd_names <- paste0("sd_", g, "_", nlpar, "_", r$coef)
    for (i in seq_along(old_sd_names)) {
      lc(out) <- rename_simple(
        old_sd_names[i], new_sd_names[i], pars, dims
      )
    }
    # rename cor-parameters
    new_cor_names <- get_cornames(
      paste0(nlpar, "_", r$coef), type = paste0("cor_", g),
      brackets = FALSE, sep = "_"
    )
    old_cor_names <- get_cornames(
      r$coef, brackets = FALSE, sep = "_",
      type = paste0("cor_", nlpar, "_", g)
    )
    for (i in seq_along(old_cor_names)) {
      lc(out) <- rename_simple(
        old_cor_names[i], new_cor_names[i], pars, dims
      )
    }
    # rename r-parameters
    old_r_name <- paste0("r_", nlpar, "_", g)
    new_r_name <- paste0("r_", g, "_", nlpar)
    levels <- gsub("[ \t\r\n]", ".", attr(ranef, "levels")[[g]])
    index_names <- make_index_names(levels, r$coef, dim = 2)
    new_r_names <- paste0(new_r_name, index_names)
    lc(out) <- rename_simple(
      old_r_name, new_r_names, pars, dims,
      pnames = new_r_name
    )
  }
  out
}

# add double underscore in group-level parameters
# required for brms < 1.0.0
# @note assumes that group and nlpar are correctly ordered already
# @param ranef output of tidy_ranef
# @param pars names of all parameters in the model
# @param dims dimension of parameters
# @return a list whose elements can be interpreted by do_renaming
rename_old_re2 <- function(ranef, pars, dims) {
  out <- list()
  for (id in unique(ranef$id)) {
    r <- subset2(ranef, id = id)
    g <- r$group[1]
    nlpars_usc <- usc(r$nlpar, "suffix")
    # rename sd-parameters
    old_sd_names <- paste0("sd_", g, "_", nlpars_usc, r$coef)
    new_sd_names <- paste0("sd_", g, "__", nlpars_usc, r$coef)
    for (i in seq_along(old_sd_names)) {
      lc(out) <- rename_simple(old_sd_names[i], new_sd_names[i], pars, dims)
    }
    # rename cor-parameters
    new_cor_names <- get_cornames(
      paste0(nlpars_usc, r$coef), type = paste0("cor_", g),
      brackets = FALSE
    )
    old_cor_names <- get_cornames(
      paste0(nlpars_usc, r$coef), type = paste0("cor_", g),
      brackets = FALSE, sep = "_"
    )
    for (i in seq_along(old_cor_names)) {
      lc(out) <- rename_simple(old_cor_names[i], new_cor_names[i], pars, dims)
    }
    # rename r-parameters
    for (nlpar in unique(r$nlpar)) {
      sub_r <- r[r$nlpar == nlpar, ]
      old_r_name <- paste0("r_", g, usc(nlpar))
      new_r_name <- paste0("r_", g, usc(usc(nlpar)))
      levels <- gsub("[ \t\r\n]", ".", attr(ranef, "levels")[[g]])
      index_names <- make_index_names(levels, sub_r$coef, dim = 2)
      new_r_names <- paste0(new_r_name, index_names)
      lc(out) <- rename_simple(
        old_r_name, new_r_names, pars, dims,
        pnames = new_r_name
      )
    }
  }
  out
}

# change names of spline parameters fitted with brms <= 1.0.1
# this became necessary after allowing smooths with multiple covariates
rename_old_sm <- function(bterms, data, pars, dims) {
  .rename_old_sm <- function(bt) {
    out <- list()
    smef <- tidy_smef(bt, data)
    if (nrow(smef)) {
      p <- usc(combine_prefix(bt), "suffix")
      old_smooths <- rename(paste0(p, smef$term))
      new_smooths <- rename(paste0(p, smef$label))
      old_sds_pars <- paste0("sds_", old_smooths)
      new_sds_pars <- paste0("sds_", new_smooths, "_1")
      old_s_pars <- paste0("s_", old_smooths)
      new_s_pars <- paste0("s_", new_smooths, "_1")
      for (i in seq_along(old_smooths)) {
        lc(out) <- rename_simple(old_sds_pars[i], new_sds_pars[i], pars, dims)
        dim_s <- dims[[old_s_pars[i]]]
        if (!is.null(dim_s)) {
          new_s_par_indices <- paste0(new_s_pars[i], "[", seq_len(dim_s), "]")
          lc(out) <- rename_simple(
            old_s_pars[i], new_s_par_indices, pars, dims,
            pnames = new_s_pars[i]
          )
        }
      }
    }
    return(out)
  }

  out <- list()
  if (is.mvbrmsterms(bterms)) {
    for (r in bterms$responses) {
      c(out) <- .rename_old_sm(bterms$terms[[r]]$dpars$mu)
    }
  } else if (is.brmsterms(bterms)) {
    for (dp in names(bterms$dpars)) {
      bt <- bterms$dpars[[dp]]
      if (length(bt$nlpars)) {
        for (nlp in names(bt$nlpars)) {
          c(out) <- .rename_old_sm(bt$nlpars[[nlp]])
        }
      } else {
        c(out) <- .rename_old_sm(bt)
      }
    }
  }
  out
}

# change names of monotonic effects fitted with brms <= 1.9.0
# this became necessary after implementing monotonic interactions
rename_old_mo <- function(bterms, data, pars) {
  .rename_old_mo <- function(bt) {
    out <- list()
    spef <- tidy_spef(bt, data)
    has_mo <- lengths(spef$calls_mo) > 0
    if (!any(has_mo)) {
      return(out)
    }
    spef <- spef[has_mo, ]
    p <- usc(combine_prefix(bt))
    bmo_prefix <- paste0("bmo", p, "_")
    bmo_regex <- paste0("^", bmo_prefix, "[^_]+$")
    bmo_old <- pars[grepl(bmo_regex, pars)]
    bmo_new <- paste0(bmo_prefix, spef$coef)
    if (length(bmo_old) != length(bmo_new)) {
      stop2("Restructuring failed. Please refit your ",
            "model with the latest version of brms.")
    }
    for (i in seq_along(bmo_old)) {
      pos <- grepl(paste0("^", bmo_old[i]), pars)
      lc(out) <- rlist(pos, fnames = bmo_new[i])
    }
    simo_regex <- paste0("^simplex", p, "_[^_]+$")
    simo_old_all <- pars[grepl(simo_regex, pars)]
    simo_index <- get_matches("\\[[[:digit:]]+\\]$", simo_old_all)
    simo_old <- unique(sub("\\[[[:digit:]]+\\]$", "", simo_old_all))
    simo_coef <- get_simo_labels(spef)
    for (i in seq_along(simo_old)) {
      regex_pos <- paste0("^", simo_old[i])
      pos <- grepl(regex_pos, pars)
      simo_new <- paste0("simo", p, "_", simo_coef[i])
      simo_index_part <- simo_index[grepl(regex_pos, simo_old_all)]
      simo_new <- paste0(simo_new, simo_index_part)
      lc(out) <- rlist(pos, fnames = simo_new)
    }
    return(out)
  }

  out <- list()
  if (is.mvbrmsterms(bterms)) {
    for (r in bterms$responses) {
      c(out) <- .rename_old_mo(bterms$terms[[r]]$dpars$mu)
    }
  } else if (is.brmsterms(bterms)) {
    for (dp in names(bterms$dpars)) {
      bt <- bterms$dpars[[dp]]
      if (length(bt$nlpars)) {
        for (nlp in names(bt$nlpars)) {
          c(out) <- .rename_old_mo(bt$nlpars[[nlp]])
        }
      } else {
        c(out) <- .rename_old_mo(bt)
      }
    }
  }
  out
}

# between version 1.0 and 2.0 categorical models used
# the internal multivariate interface
rename_old_categorical <- function(bterms, data, pars) {
  stopifnot(is.brmsterms(bterms))
  if (!is_categorical(bterms$family)) {
    return(list())
  }
  # compute the old category names
  respform <- bterms$respform
  old_dpars <- model.response(model.frame(respform, data = data))
  old_dpars <- levels(factor(old_dpars))
  old_dpars <- make.names(old_dpars[-1], unique = TRUE)
  old_dpars <- rename(old_dpars, ".", "x")
  new_dpars <- bterms$family$dpars
  stopifnot(length(old_dpars) == length(new_dpars))
  pos <- rep(FALSE, length(pars))
  new_pars <- pars
  for (i in seq_along(old_dpars)) {
    # not perfectly save but hopefully mostly correct
    regex <- paste0("(?<=_)", old_dpars[i], "(?=_|\\[)")
    pos <- pos | grepl(regex, pars, perl = TRUE)
    new_pars <- gsub(regex, new_dpars[i], new_pars, perl = TRUE)
  }
  list(nlist(pos, fnames = new_pars[pos]))
}

# as of brms 2.2 'mo' and 'me' terms are handled together
rename_old_bsp <- function(pars) {
  pos <- grepl("^(bmo|bme)_", pars)
  if (!any(pos)) return(list())
  fnames <- gsub("^(bmo|bme)_", "bsp_", pars[pos])
  list(nlist(pos, fnames))
}

# prepare for renaming of parameters in old models
rename_simple <- function(oldname, fnames, pars, dims, pnames = fnames) {
  pos <- grepl(paste0("^", oldname), pars)
  if (any(pos)) {
    out <- nlist(pos, oldname, pnames, fnames, dims = dims[[oldname]])
    class(out) <- c("rlist", "list")
  } else {
    out <- NULL
  }
  out
}

# rescale old 'b' coefficients of monotonic effects
# to represent average instead of total differences
rescale_old_mo <- function(x, ...) {
  UseMethod("rescale_old_mo")
}

#' @export
rescale_old_mo.brmsfit <- function(x, ...) {
  bterms <- brmsterms(x$formula)
  rescale_old_mo(bterms, fit = x, ...)
}

#' @export
rescale_old_mo.mvbrmsterms <- function(x, fit, ...) {
  for (resp in x$responses) {
    fit <- rescale_old_mo(x$terms[[resp]], fit = fit, ...)
  }
  fit
}

#' @export
rescale_old_mo.brmsterms <- function(x, fit, ...) {
  for (dp in names(x$dpars)) {
    fit <- rescale_old_mo(x$dpars[[dp]], fit = fit, ...)
  }
  for (nlp in names(x$nlpars)) {
    fit <- rescale_old_mo(x$nlpars[[nlp]], fit = fit, ...)
  }
  fit
}

#' @export
rescale_old_mo.btnl <- function(x, fit, ...) {
  fit
}

#' @export
rescale_old_mo.btl <- function(x, fit, ...) {
  spef <- tidy_spef(x, fit$data)
  has_mo <- lengths(spef$Imo) > 0L
  if (!any(has_mo)) {
    return(fit)
  }
  warning2(
    "The parameterization of monotonic effects has changed in brms 2.8.4 ",
    "so that corresponding 'b' coefficients now represent average instead ",
    "of total differences between categories. See vignette('brms_monotonic') ",
    "for more details. Parameters of old models are adjusted automatically."
  )
  p <- combine_prefix(x)
  all_pars <- variables(fit)
  chains <- fit$fit@sim$chains
  for (i in which(has_mo)) {
    bsp_par <- paste0("bsp", p, "_", spef$coef[i])
    simo_regex <- paste0(spef$coef[i], seq_along(spef$Imo[[i]]))
    simo_regex <- paste0("simo", p, "_", simo_regex, "[")
    simo_regex <- paste0("^", escape_all(simo_regex))
    # scaling factor by which to divide the old 'b' coefficients
    D <- prod(ulapply(simo_regex, function(r) sum(grepl(r, all_pars))))
    for (j in seq_len(chains)) {
      fit$fit@sim$samples[[j]][[bsp_par]] <-
        fit$fit@sim$samples[[j]][[bsp_par]] / D
    }
  }
  fit
}

# update old families to work with the latest brms version
update_old_family <- function(x, ...) {
  UseMethod("update_old_family")
}

#' @export
update_old_family.default <- function(x, ...) {
  validate_family(x)
}

#' @export
update_old_family.brmsfamily <- function(x, ...) {
  # new specials may have been added in new brms versions
  family_info <- get(paste0(".family_", x$family))()
  x$specials <- family_info$specials
  x
}

#' @export
update_old_family.customfamily <- function(x, ...) {
  if (!is.null(x$predict)) {
    x$posterior_predict <- x$predict
    x$predict <- NULL
  }
  if (!is.null(x$fitted)) {
    x$posterior_epred <- x$fitted
    x$fitted <- NULL
  }
  x
}

#' @export
update_old_family.mixfamily <- function(x, ...) {
  x$mix <- lapply(x$mix, update_old_family, ...)
  x
}

#' @export
update_old_family.brmsformula <- function(x, ...) {
  x$family <- update_old_family(x$family, ...)
  x
}

#' @export
update_old_family.mvbrmsformula <- function(x, ...) {
  x$forms <- lapply(x$forms, update_old_family, ...)
  x
}

stop_parameterization_changed <- function(family, version) {
  stop2(
    "The parameterization of '", family, "' models has changed in brms ",
    version, ". Please refit your model with the current version of brms."
  )
}

check_old_nl_dpars <- function(bterms) {
  .check_nl_dpars <- function(x) {
    stopifnot(is.brmsterms(x))
    non_mu_dpars <- x$dpars[names(x$dpars) != "mu"]
    if (any(ulapply(non_mu_dpars, is.btnl))) {
      stop2(
        "Non-linear parameters are global within univariate models ",
        "as of version 2.3.7. Please refit your model with the ",
        "latest version of brms."
      )
    }
    return(TRUE)
  }
  if (is.mvbrmsterms(bterms)) {
    lapply(bterms$terms, .check_nl_dpars)
  } else {
    .check_nl_dpars(bterms)
  }
  TRUE
}