#' Mixed effects linear regression models: \code{finalfit} model wrapper

#'

#' Using \code{finalfit} conventions, produces mixed effects linear regression

#' models for a set of explanatory variables against a continuous dependent.

#'

#' Uses \code{lme4::\link[lme4]{lmer}} with \code{finalfit} modelling

#' conventions. Output can be passed to \code{\link{fit2df}}. This is only

#' currently set-up to take a single random effect as a random intercept. Can be

#' updated in future to allow multiple random intercepts, random gradients and

#' interactions on random effects if there is a need.

#'

#' @param .data Dataframe.

#' @param dependent Character vector of length 1, name of depdendent variable

#'   (must be continuous vector).

#' @param explanatory Character vector of any length: name(s) of explanatory

#'   variables.

#' @param random_effect Character vector of length 1, either, (1) name of random

#'   intercept variable, e.g. "var1", (automatically convered to "(1 | var1)");

#'   or, (2) the full \code{lme4} specification, e.g. "(var1 | var2)". Note

#'   parenthesis MUST be included in (2)2 but NOT included in (1).

#' @param ... Other arguments to pass to \code{lme4::\link[lme4]{lmer}}.

#' @return A list of multivariable \code{lme4::\link[lme4]{lmer}} fitted model

#'   outputs. Output is of class \code{lmerMod}.

#'

#' @seealso \code{\link{fit2df}}

#' @family finalfit model wrappers

#' @export

#'

#' @examples

#' library(finalfit)

#' library(dplyr)

#'

#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")

#' random_effect = "hospital"

#' dependent = "nodes"

#'

#' colon_s %>%

#'   lmmixed(dependent, explanatory, random_effect) %>%

#' 	 fit2df(estimate_suffix=" (multilevel")

lmmixed <- function(.data, dependent, explanatory, random_effect, ...){

	# If single term random effect, default to random intercept model

}