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-#' Create saemix models from mmkin row objects
-#'
-#' This function sets up a nonlinear mixed effects model for an mmkin row
-#' object for use with the saemix package. An mmkin row object is essentially a
-#' list of mkinfit objects that have been obtained by fitting the same model to
-#' a list of datasets.
-#'
-#' Starting values for the fixed effects (population mean parameters, argument psi0 of
-#' [saemix::saemixModel()] are the mean values of the parameters found using
-#' mmkin. Starting variances of the random effects (argument omega.init) are the
-#' variances of the deviations of the parameters from these mean values.
-#'
-#' @param object An mmkin row object containing several fits of the same model to different datasets
-#' @param cores The number of cores to be used for multicore processing.
-#'   On Windows machines, cores > 1 is currently not supported.
-#' @rdname saemix
-#' @importFrom saemix saemixData saemixModel
-#' @importFrom stats var
-#' @examples
-#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
-#'  function(x) subset(x$data[c("name", "time", "value")]))
-#' names(ds) <- paste("Dataset", 6:10)
-#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-#'   A1 = mkinsub("SFO"))
-#' \dontrun{
-#' f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE)
-#' library(saemix)
-#' m_saemix <- saemix_model(f_mmkin)
-#' d_saemix <- saemix_data(f_mmkin)
-#' saemix_options <- list(seed = 123456,
-#'   save = FALSE, save.graphs = FALSE, displayProgress = FALSE,
-#'   nbiter.saemix = c(200, 80))
-#' f_saemix <- saemix(m_saemix, d_saemix, saemix_options)
-#' plot(f_saemix, plot.type = "convergence")
-#' }
-#' @return An [saemix::SaemixModel] object.
-#' @export
-saemix_model <- function(object, cores = parallel::detectCores()) {
-  if (nrow(object) > 1) stop("Only row objects allowed")
-
-  mkin_model <- object[[1]]$mkinmod
-  analytical <- is.function(mkin_model$deg_func)
-
-  degparms_optim <-  mean_degparms(object)
-  psi0 <- matrix(degparms_optim, nrow = 1)
-  colnames(psi0) <- names(degparms_optim)
-
-  degparms_fixed <- object[[1]]$bparms.fixed
-
-  odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE)
-  odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE)
-
-  odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names)
-  odeparms_fixed <- degparms_fixed[odeparms_fixed_names]
-
-  odeini_fixed <- degparms_fixed[odeini_fixed_parm_names]
-  names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names)
-
-  model_function <- function(psi, id, xidep) {
-
-    uid <- unique(id)
-
-    res_list <- parallel::mclapply(uid, function(i) {
-        transparms_optim <- psi[i, ]
-        names(transparms_optim) <- names(degparms_optim)
-
-        odeini_optim <- transparms_optim[odeini_optim_parm_names]
-        names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names)
-
-        odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
-
-        ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names)
-        odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,
-          transform_rates = object[[1]]$transform_rates,
-          transform_fractions = object[[1]]$transform_fractions)
-        odeparms <- c(odeparms_optim, odeparms_fixed)
-
-        xidep_i <- subset(xidep, id == i)
-
-        if (analytical) {
-          out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms)
-        } else {
-
-          i_time <- xidep_i$time
-          i_name <- xidep_i$name
-
-          out_wide <- mkinpredict(mkin_model,
-            odeparms = odeparms, odeini = odeini,
-            solution_type = object[[1]]$solution_type,
-            outtimes = sort(unique(i_time)))
-
-          out_index <- cbind(as.character(i_time), as.character(i_name))
-          out_values <- out_wide[out_index]
-        }
-        return(out_values)
-      }, mc.cores = cores)
-      res <- unlist(res_list)
-      return(res)
-  }
-
-  raneff_0 <- mean_degparms(object, random = TRUE)$random$ds
-  var_raneff_0 <- apply(raneff_0, 2, var)
-
-  res <- saemixModel(model_function, psi0,
-    "Mixed model generated from mmkin object",
-    transform.par = rep(0, length(degparms_optim)),
-    omega.init = diag(var_raneff_0)
-  )
-  return(res)
-}
-
-#' @rdname saemix
-#' @param \dots Further parameters passed to [saemix::saemixData]
-#' @return An [saemix::SaemixData] object.
-#' @export
-saemix_data <- function(object, ...) {
-  if (nrow(object) > 1) stop("Only row objects allowed")
-  ds_names <- colnames(object)
-
-  ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
-  names(ds_list) <- ds_names
-  ds_saemix_all <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
-  ds_saemix <- data.frame(ds = ds_saemix_all$ds,
-    name = as.character(ds_saemix_all$variable),
-    time = ds_saemix_all$time,
-    value = ds_saemix_all$observed,
-    stringsAsFactors = FALSE)
-
-  res <- saemixData(ds_saemix,
-    name.group = "ds",
-    name.predictors = c("time", "name"),
-    name.response = "value", ...)
-  return(res)
-}