#' @title Convergence test for mixed effects models
#' @name check_convergence
#'
#' @description `check_convergence()` provides an alternative convergence
#'   test for `merMod`-objects.
#'
#' @param x A `merMod` or `glmmTMB`-object.
#' @param tolerance Indicates up to which value the convergence result is
#'   accepted. The smaller `tolerance` is, the stricter the test will be.
#' @param ... Currently not used.
#'
#' @return `TRUE` if convergence is fine and `FALSE` if convergence
#'   is suspicious. Additionally, the convergence value is returned as attribute.
#'
#' @details \subsection{Convergence and log-likelihood}{
#'   Convergence problems typically arise when the model hasn't converged
#'   to a solution where the log-likelihood has a true maximum. This may result
#'   in unreliable and overly complex (or non-estimable) estimates and standard
#'   errors.
#'   }
#'   \subsection{Inspect model convergence}{
#'   **lme4** performs a convergence-check (see `?lme4::convergence`),
#'   however, as as discussed [here](https://github.com/lme4/lme4/issues/120)
#'   and suggested by one of the lme4-authors in
#'   [this comment](https://github.com/lme4/lme4/issues/120#issuecomment-39920269),
#'   this check can be too strict. `check_convergence()` thus provides an
#'   alternative convergence test for `merMod`-objects.
#'   }
#'   \subsection{Resolving convergence issues}{
#'   Convergence issues are not easy to diagnose. The help page on
#'   `?lme4::convergence` provides most of the current advice about
#'   how to resolve convergence issues. Another clue might be large parameter
#'   values, e.g. estimates (on the scale of the linear predictor) larger than
#'   10 in (non-identity link) generalized linear model *might* indicate
#'   [complete separation](https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/).
#'   Complete separation can be addressed by regularization, e.g. penalized
#'   regression or Bayesian regression with appropriate priors on the fixed effects.
#'   }
#'   \subsection{Convergence versus Singularity}{
#'   Note the different meaning between singularity and convergence: singularity
#'   indicates an issue with the "true" best estimate, i.e. whether the maximum
#'   likelihood estimation for the variance-covariance matrix of the random effects
#'   is positive definite or only semi-definite. Convergence is a question of
#'   whether we can assume that the numerical optimization has worked correctly
#'   or not.
#'   }
#'
#' @examples
#' if (require("lme4")) {
#'   data(cbpp)
#'   set.seed(1)
#'   cbpp$x <- rnorm(nrow(cbpp))
#'   cbpp$x2 <- runif(nrow(cbpp))
#'
#'   model <- glmer(
#'     cbind(incidence, size - incidence) ~ period + x + x2 + (1 + x | herd),
#'     data = cbpp,
#'     family = binomial()
#'   )
#'
#'   check_convergence(model)
#' }
#'
#' \dontrun{
#' if (require("glmmTMB")) {
#'   model <- glmmTMB(
#'     Sepal.Length ~ poly(Petal.Width, 4) * poly(Petal.Length, 4) +
#'       (1 + poly(Petal.Width, 4) | Species),
#'     data = iris
#'   )
#'   check_convergence(model)
#' }
#' }
#' @export
check_convergence <- function(x, tolerance = 0.001, ...) {
  UseMethod("check_convergence")
}


#' @export
check_convergence.default <- function(x, tolerance = 0.001, ...) {
  # check for valid input
  .is_model_valid(x)
  message(sprintf("`check_convergence()` does not work for models of class '%s'.", class(x)[1]))
}


#' @export
check_convergence.merMod <- function(x, tolerance = 0.001, ...) {
  insight::check_if_installed("Matrix")

  relgrad <- with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))

  # copy logical value, TRUE if convergence is OK
  retval <- max(abs(relgrad)) < tolerance
  # copy convergence value
  attr(retval, "gradient") <- max(abs(relgrad))

  # return result
  retval
}


#' @export
check_convergence.glmmTMB <- function(x, ...) {
  # https://github.com/glmmTMB/glmmTMB/issues/275
  isTRUE(x$sdr$pdHess)
}
