diff --git a/R/lav_options.R b/R/lav_options.R
index 55d496a1..04aee226 100644
--- a/R/lav_options.R
+++ b/R/lav_options.R
@@ -880,6 +880,16 @@ lav_options_set <- function(opt = NULL) {                     # nolint
     "browne.residual.adf.model",
     "bollen.stine"
   ))
+
+  fmgs = c()
+  for (j in seq_along(wrong.idx)) {
+    fmg_parsed = lav_fmg_parse_input(opt$test[wrong.idx[j]])
+    if (!is.null(fmg_parsed)) {
+      fmgs = c(fmgs, opt$test[wrong.idx[j]])
+      wrong.idx = wrong.idx[-j]
+    }
+  }
+
   if (length(wrong.idx) > 0L) {
     lav_msg_stop(gettextf(
     "invalid option(s) for test argument: %1$s. Possible options are: %2$s.",
@@ -889,7 +899,7 @@ lav_options_set <- function(opt = NULL) {                     # nolint
                     "browne.residual.nt.model", "satorra.bentler",
                     "yuan.bentler", "yuan.bentler.mplus",
                     "mean.var.adjusted", "scaled.shifted",
-                    "bollen.stine"), log.sep = "or")))
+                    "bollen.stine", "fmg", "fmgols"), log.sep = "or")))
   }
 
   # bounds
diff --git a/R/lav_test.R b/R/lav_test.R
index e2ca95f0..f3a37569 100644
--- a/R/lav_test.R
+++ b/R/lav_test.R
@@ -168,24 +168,33 @@ lav_test_rename <- function(test, check = FALSE) {
     test[target.idx] <- "browne.residual.nt.model"
   }
 
+  # report unknown values
+  bad.idx <- which(!test %in% c(
+    "standard", "none", "default",
+    "satorra.bentler",
+    "yuan.bentler",
+    "yuan.bentler.mplus",
+    "mean.adjusted",
+    "mean.var.adjusted",
+    "scaled.shifted",
+    "bollen.stine",
+    "browne.residual.nt",
+    "browne.residual.nt.model",
+    "browne.residual.adf",
+    "browne.residual.adf.model"
+  ))
+
+  fmgs = c()
+  for (j in seq_along(bad.idx)) {
+    fmg_parsed = lav_fmg_parse_input(test[bad.idx[j]])
+    if (!is.null(fmg_parsed)) {
+      fmgs = c(fmgs, test[bad.idx[j]])
+      bad.idx = bad.idx[-j]
+    }
+  }
 
   # check?
   if (check) {
-    # report unknown values
-    bad.idx <- which(!test %in% c(
-      "standard", "none", "default",
-      "satorra.bentler",
-      "yuan.bentler",
-      "yuan.bentler.mplus",
-      "mean.adjusted",
-      "mean.var.adjusted",
-      "scaled.shifted",
-      "bollen.stine",
-      "browne.residual.nt",
-      "browne.residual.nt.model",
-      "browne.residual.adf",
-      "browne.residual.adf.model"
-    ))
     if (length(bad.idx) > 0L) {
       lav_msg_stop(sprintf(
         ngettext(
@@ -212,7 +221,7 @@ lav_test_rename <- function(test, check = FALSE) {
     }
   }
 
-  # reorder: first nonscaled, then scaled
+  # reorder: first nonscaled, then scaled, then fmg.
   nonscaled.idx <- which(test %in% c(
     "standard", "none", "default",
     "bollen.stine",
@@ -229,7 +238,9 @@ lav_test_rename <- function(test, check = FALSE) {
     "mean.var.adjusted",
     "scaled.shifted"
   ))
-  test <- c(test[nonscaled.idx], test[scaled.idx])
+
+  scaled <- c(test[scaled.idx], sort(fmgs))
+  test <- c(test[nonscaled.idx], scaled)
 
   test
 }
@@ -555,6 +566,9 @@ lav_model_test <- function(lavobject = NULL,
       # which test statistic shall we scale?
       unscaled.TEST <- TEST[[1]]
       if (lavoptions$scaled.test != "standard") {
+        print(test.orig)
+        print(lavoptions$scaled.test)
+        print(TEST)
         idx <- which(test.orig == lavoptions$scaled.test)
         if (length(idx) > 0L) {
           unscaled.TEST <- TEST[[idx[1]]]
@@ -684,6 +698,8 @@ lav_model_test <- function(lavobject = NULL,
         boot.larger = boot.larger,
         boot.length = boot.length
       )
+    } else if (!is.null(input <- lav_fmg_parse_input(this.test))) {
+      TEST[[this.test]] <- lav_test_fmg(lavobject, input)
     }
   } # additional tests
 
diff --git a/R/lav_test_fmg.R b/R/lav_test_fmg.R
new file mode 100644
index 00000000..05edd6f8
--- /dev/null
+++ b/R/lav_test_fmg.R
@@ -0,0 +1,235 @@
+lav_rls <- \(object) {
+  s_inv <- chol2inv(chol(lavaan::lavInspect(object, "sigma.hat")))
+  residuals <- lavaan::lavInspect(object, "residuals")
+  mm <- residuals[[1]] %*% s_inv
+  n <- lavaan::lavInspect(object, "nobs")
+  .5 * n * sum(mm * t(mm))
+}
+
+lav_test_fmg <- \(object, input) {
+  make_chisqs <- \(chisq) {
+    if (chisq == "ml") lavaan::fitmeasures(object, "chisq") else rls(object)
+  }
+
+  name <- input$name
+  param <- input$param
+  #unbiased <- input$unbiased
+  #chisq <- if (input$chisq == "ml") lavaan::fitmeasures(object, "chisq") else lav_rls(object)
+  chisq <- fitmeasures(object, "chisq")
+  df <- fitmeasures(object, "df")
+  ug <- lav_object_inspect_UGamma(object) # Only for one group.
+  #ug <- lav_ugamma_no_groups(object, unbiased)
+  lambdas <- Re(eigen(ug)$values)[seq(df)]
+
+  print(chisq)
+  print(lambdas)
+  print(param)
+  list(
+    test = lav_fmg_reconstruct_label(input),
+    pvalue = if (name == "fmg") {
+    lav_fmg(chisq, lambdas, param)
+  } else if (name == "fmgols") {
+    lav_fmgols(chisq, lambdas, param)
+  },
+  label = lav_fmg_reconstruct_label(input))
+}
+
+lav_fmg_parse_input <- \(string) {
+  na_to_null <- \(x) if (is.na(x)) NULL else x
+  default <- \(x) if (x != "") as.numeric(x) else 4
+
+  tryCatch ({
+    string <- tolower(string)
+    splitted <- strsplit(string, "_")[[1]]
+    name <- na_to_null(splitted[1])
+    out <- NULL
+
+    if (name != "fmg" && name != "fmgols") {
+      return(NULL)
+    }
+
+    param <- if (length(splitted) == 2) {
+      as.numeric(splitted[2])
+    } else if (name == "fmg") {
+      4
+    } else {
+      0.5
+    }
+
+    list(
+      param = param,
+      name = name
+    )
+  }, error = \(e){
+    cat(e)
+  })
+}
+
+lav_fmg_reconstruct_label <- \(input) paste0(input$name, "_", input$param)
+
+lav_fmg_construct_label <- \(input) {
+  if (input$name == "fmg") {
+    paste0("FMG with ", input$param, " blocks.")
+  } else {
+    paste0("FMGOLS with weighting parameter ", input$param, ".")
+  }
+}
+
+lav_fmg <- \(chisq, lambdas, j) {
+  m <- length(lambdas)
+  k <- ceiling(m / j)
+  eig <- lambdas
+  eig <- c(eig, rep(NA, k * j - length(eig)))
+  dim(eig) <- c(k, j)
+  eig_means <- colMeans(eig, na.rm = TRUE)
+  eig_mean <- mean(lambdas)
+  repeated <- rep(eig_means, each = k)[seq(m)]
+  lav_fmg_imhof(chisq, (repeated + eig_mean) / 2)
+}
+
+lav_fmgols <- \(chisq, lambdas, gamma) {
+  x <- seq_along(lambdas)
+  beta1_hat <- 1 / gamma * stats::cov(x, lambdas) / stats::var(x)
+  beta0_hat <- mean(lambdas) - beta1_hat * mean(x)
+  lambda_hat <- pmax(beta0_hat + beta1_hat * x, 0)
+  lav_fmg_imhof(chisq, lambda_hat)
+}
+
+lav_fmg_imhof <- \(x, lambda) {
+  theta <- \(u, x, lambda) 0.5 * (sum(atan(lambda * u)) - x * u)
+  rho <- \(u, lambda) exp(1 / 4 * sum(log(1 + lambda^2 * u^2)))
+  integrand <- Vectorize(\(u) {
+    sin(theta(u, x, lambda)) / (u * rho(u, lambda))
+  })
+  z <- tryCatch({
+    integrate(integrand, lower = 0, upper = Inf)$value
+  },
+  error = \(e) {
+    integrate(integrand, lower = 0, upper = 1000)$value
+  })
+  0.5 + z / pi
+}
+
+
+#' Calculate non-nested ugamma for multiple groups.
+#' @param object A `lavaan` object.
+#' @param unbiased If `TRUE`, uses the unbiased gamma estimate.
+#' @keywords internal
+#' @return Ugamma for non-nested object.
+ugamma_non_nested <- \(object) {
+  lavmodel <- object@Model
+
+  ceq_idx <- attr(lavmodel@con.jac, "ceq.idx")
+  if (length(ceq_idx) > 0L) {
+    stop("Testing of models with groups and equality constraints not supported.")
+  }
+
+  test <- list()
+  lavsamplestats <- object@SampleStats
+  lavmodel <- object@Model
+  lavoptions <- object@Options
+  lavimplied <- object@implied
+  lavdata <- object@Data
+  test$standard <- object@test[[1]]
+
+  if (test$standard$df == 0L || test$standard$df < 0) {
+    stop("Df must be > 0.")
+  }
+
+  e <- lavaan:::lav_model_information(
+    lavmodel = lavmodel,
+    lavimplied = lavimplied,
+    lavsamplestats = lavsamplestats,
+    lavdata = lavdata,
+    lavoptions = lavoptions,
+    extra = TRUE
+  )
+
+  delta <- attr(e, "Delta")
+  wls_v <- attr(e, "WLS.V")
+
+  gamma <- lavaan:::lav_object_gamma(object)
+  if (is.null(gamma[[1]])) {
+    gamma <- lapply(lavaan::lavInspect(object, "gamma"), \(x) {
+      class(x) <- "matrix"
+      x
+    })
+  }
+
+  gamma_global <- as.matrix(Matrix::bdiag(gamma))
+  delta_global <- do.call(rbind, delta)
+  v_global <- as.matrix(Matrix::bdiag(wls_v))
+  x <- v_global %*% delta_global
+  u_global <- v_global - crossprod(t(x), solve(t(delta_global) %*% x, t(x)))
+  u_global %*% gamma_global
+}
+
+#' Calculate nested ugamma.
+#'
+#' This can also be used with restrictions.
+#'
+#' @param m0,m1 Two nested `lavaan` objects.
+#' @param a The `A` matrix. If if `NULL`, gets calculated by
+#'    `lavaan:::lav_test_diff_A` with `method = method`.
+#' @param method Method passed to `lavaan:::lav_test_diff_A`.
+#' @param unbiased If `TRUE`, uses the unbiased gamma estimate.
+#' @keywords internal
+#' @return Ugamma for nested object.
+lav_ugamma_nested <- \(m0, m1, a = NULL, method = "delta", unbiased = FALSE) {
+  m0@Options$gamma.unbiased <- unbiased
+  m1@Options$gamma.unbiased <- unbiased
+
+  # extract information from m1 and m2
+  t1 <- m1@test[[1]]$stat
+  r1 <- m1@test[[1]]$df
+
+  t0 <- m0@test[[1]]$stat
+  r0 <- m0@test[[1]]$df
+
+  # m = difference between the df's
+  m <- r0 - r1
+
+  # check for identical df setting
+  if (m == 0L) {
+    return(list(
+      T.delta = (t0 - t1), scaling.factor = as.numeric(NA),
+      df.delta = m, a = as.numeric(NA), b = as.numeric(NA)
+    ))
+  }
+
+  gamma <- lavaan:::lav_object_gamma(m0) # the same for m1 and m0
+
+  if (is.null(gamma)) {
+    stop("lavaan error: Can not compute gamma matrix; perhaps missing \"ml\"?")
+  }
+
+  wls_v <- lavaan::lavTech(m1, "WLS.V")
+  pi <- lavaan::lavInspect(m1, "delta")
+
+  p_inv <- lavaan::lavInspect(m1, what = "inverted.information")
+
+  if (is.null(a)) {
+    a <- lavaan:::lav_test_diff_A(m1, m0, method = method, reference = "H1")
+    if (m1@Model@eq.constraints) {
+      a <- a %*% t(m1@Model@eq.constraints.K)
+    }
+  }
+
+  paapaap <- p_inv %*% t(a) %*% MASS::ginv(a %*% p_inv %*% t(a)) %*% a %*% p_inv
+
+  # compute scaling factor
+  fg <- unlist(m1@SampleStats@nobs) / m1@SampleStats@ntotal
+
+  # We need the global gamma, cf. eq.~(10) in Satorra (2000).
+  gamma_rescaled <- gamma
+  for (i in (seq_along(gamma))) {
+    gamma_rescaled[[i]] <- fg[i] * gamma_rescaled[[i]]
+  }
+  gamma_global <- as.matrix(Matrix::bdiag(gamma_rescaled))
+  # Also the global V:
+  v_global <- as.matrix(Matrix::bdiag(wls_v))
+  pi_global <- do.call(rbind, pi)
+  # U global version, eq.~(22) in Satorra (2000).
+  u_global <- v_global %*% pi_global %*% paapaap %*% t(pi_global) %*% v_global
+  return(u_global %*% gamma_global)
+}
diff --git a/lav_fmg_greier.r b/lav_fmg_greier.r
new file mode 100644
index 00000000..7f8f74ec
--- /dev/null
+++ b/lav_fmg_greier.r
@@ -0,0 +1,77 @@
+n = 50
+data = psych::bfi
+
+model = "A =~ A1+a*A2+A3+A4+A5;
+         C =~ C1+a*C2+C3+C4+C5
+         "
+
+object <- sem(model, data[1:n, ], group = "gender", test = "sb")
+lavTest(object, test = "fmg_1")
+lavTest(object, test = "sb")
+
+
+sum(diag(ugamma_non_nested(object))) / 34
+
+dim(lavaan::lavInspect(object, "delta"))
+
+
+
+model = "A =~ A1+A2+A3+A4+A5;
+         C =~ C1+C2+C3+C4+C5
+         "
+object <- sem(model, data[1:n, ], test = "sb")
+dim(lavaan::lavInspect(object, "delta"))
+
+object_chisq <- sem(model, data[1:n, ], test = "sb")
+
+#lavGamma(object_chisq, gamma.unbiased = TRUE)
+gamma_biased <- lav_samplestats_Gamma(data[1:n, 1:10])
+gamma_yves <- lav_samplestats_Gamma(data[1:n, 1:10], unbiased = TRUE)
+gamma_fmg <- lav_fmg_gamma_unbiased(object_chisq, gamma_biased)
+sum(abs(gamma_yves-gamma_fmg))
+
+cov_yves = object_chisq@SampleStats@cov[[1]]
+cov_fmg = cov(data[1:n, 1:10], use = "pairwise") * (n-1) / n
+sum(abs(cov_yves-cov_fmg))
+
+
+#object_rls <- lavaan::sem(model, psych::bfi[1:n, 1:10], test = "sb", scaled.test = "Browne.residual.nt")
+
+
+## UNBIASED
+
+GammaNT.cov <- 2 * lav_matrix_duplication_ginv_pre_post(COV %x% COV)
+
+Gamma.u <- (N * (N - 1) / (N - 2) / (N - 3) * Gamma.cov -
+              N / (N - 2) / (N - 3) * (GammaNT.cov -
+                                         2 / (N - 1) * tcrossprod(cov.vech)))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+lavaan::lavTest(object_chisq, test = "sb", scaled.test = "Browne.residual.nt")
+
+
+lavTest(object_chisq, scaled.test = "browne.residual.nt", test = "sb")
+
+lav_model_test(lavobject = object_chisq)
+lav_model_test(lavobject = object_rls)
+
+test.orig <- object_chisq@Options$test
diff --git a/steps.txt b/steps.txt
new file mode 100644
index 00000000..2b281bc2
--- /dev/null
+++ b/steps.txt
@@ -0,0 +1,2 @@
+1. [ ] Sjekk ugamma for nested.
+2. [ ] Verifiser ugamma for groups med constraints.