diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 960234cd..258c0087 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -2,7 +2,7 @@
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches: [main, master]
+    branches: [main, master, dev]
   pull_request:
     branches: [main, master]
 
diff --git a/DESCRIPTION b/DESCRIPTION
index f023615a..1811680b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: melt
 Title: Multiple Empirical Likelihood Tests
-Version: 1.11.2
+Version: 1.11.3
 Authors@R: c(
     person("Eunseop", "Kim", , "markean@pm.me", role = c("aut", "cph", "cre")),
     person("Steven", "MacEachern", role = c("ctb", "ths")),
diff --git a/NAMESPACE b/NAMESPACE
index 12da1df7..38016fd2 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -29,6 +29,7 @@ exportMethods(sigTests)
 exportMethods(summary)
 exportMethods(weights)
 importFrom(Rcpp,sourceCpp)
+importFrom(checkmate,assert_choice)
 importFrom(checkmate,assert_class)
 importFrom(checkmate,assert_int)
 importFrom(checkmate,assert_logical)
diff --git a/NEWS.md b/NEWS.md
index b5f6efff..1c2aafe1 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,11 @@
+# melt 1.11.3
+## NEW FEATURES
+* New `"ael"` option has been added in the `calibrate` argument of `elt()` for adjusted empirical likelihood calibration.
+
+## MINOR IMPROVEMENTS
+* The package vignette has been updated.
+
+
 # melt 1.11.2
 ## MINOR IMPROVEMENTS
 * The package vignette has been updated.
diff --git a/R/AllClasses.R b/R/AllClasses.R
index 9bcc5311..09ee2bcf 100644
--- a/R/AllClasses.R
+++ b/R/AllClasses.R
@@ -24,6 +24,8 @@
 #' @slot nthreads A single integer for the number of threads for parallel
 #'   computation via OpenMP (if available).
 #' @slot seed A single integer for the seed for random number generation.
+#' @slot an A single numeric representing the scaling factor for adjusted
+#'   empirical likelihood calibration.
 #' @slot b A single integer for the number of bootstrap replicates.
 #' @slot m A single integer for the number of Monte Carlo samples.
 #' @aliases ControlEL
@@ -33,7 +35,7 @@ setClass("ControlEL",
   slots = c(
     maxit = "integer", maxit_l = "integer", tol = "numeric", tol_l = "numeric",
     step = "ANY", th = "ANY", verbose = "logical", keep_data = "logical",
-    nthreads = "integer", seed = "ANY", b = "integer", m = "integer"
+    nthreads = "integer", seed = "ANY", an = "ANY", b = "integer", m = "integer"
   )
 )
 
diff --git a/R/AllGenerics.R b/R/AllGenerics.R
index e405196b..bc251ac3 100644
--- a/R/AllGenerics.R
+++ b/R/AllGenerics.R
@@ -283,7 +283,7 @@ setGeneric("elmt", function(object,
 
 #' Empirical likelihood test
 #'
-#' Tests a linear hypothesis.
+#' Tests a linear hypothesis with various calibration options.
 #'
 #' @param object An object that inherits from \linkS4class{EL}.
 #' @param rhs A numeric vector or a column matrix for the right-hand side of
@@ -295,9 +295,9 @@ setGeneric("elmt", function(object,
 #'   of parameters. Or a character vector with a symbolic description of the
 #'   hypothesis is allowed. Defaults to `NULL`. See ‘Details’.
 #' @param alpha A single numeric for the significance level. Defaults to `0.05`.
-#' @param calibrate A single character for the calibration method. It is
-#'   case-insensitive and must be one of `"chisq"`, `"boot"`, or `"f"`.
-#'   Defaults to `"chisq"`. See ‘Details’.
+#' @param calibrate A single character representing the calibration method. It
+#'   is case-insensitive and must be one of `"ael"`, `"boot"`, `"chisq"`, or
+#'   `"f"`. Defaults to `"chisq"`. See ‘Details’.
 #' @param control An object of class \linkS4class{ControlEL} constructed by
 #'   [el_control()]. Defaults to `NULL` and inherits the `control` slot in
 #'   `object`.
@@ -318,12 +318,13 @@ setGeneric("elmt", function(object,
 #'   1. If `lhs` is `NULL`, \eqn{L} is set to the identity matrix and the
 #'   problem reduces to evaluating at \eqn{r} as \eqn{l(r)}.
 #'
-#'   `calibrate` specifies the calibration method used. Three methods are
-#'   available: `"chisq"` (chi-square calibration), `"boot"` (bootstrap
-#'   calibration), and `"f"` (\eqn{F} calibration). `"boot"` is applicable only
-#'   when `lhs` is `NULL`. The `nthreads`, `seed`, and `B` slots in `control`
-#'   apply to the bootstrap procedure. `"f"` is applicable only to the mean
-#'   parameter when `lhs` is `NULL`.
+#'   `calibrate` specifies the calibration method used. Four methods are
+#'   available: `"ael"` (adjusted empirical likelihood calibration), `"boot"`
+#'   (bootstrap calibration), `"chisq"` (chi-square calibration), and `"f"`
+#'   (\eqn{F} calibration). When `lhs` is not `NULL`, only `"chisq"` is
+#'   available. `"f"` is applicable only to the mean parameter. The `an` slot in
+#'   `control` applies specifically to `"ael"`, while the `nthreads`, `seed`,
+#'   and `B` slots apply to `"boot"`.
 #' @return An object of class of \linkS4class{ELT}. If `lhs` is non-`NULL`, the
 #'   `optim` slot corresponds to that of \linkS4class{CEL}. Otherwise, it
 #'   corresponds to that of \linkS4class{EL}.
@@ -333,6 +334,11 @@ setGeneric("elmt", function(object,
 #'   \emph{Statistical Methods & Applications}, **19**(4), 463--476.
 #'   \doi{10.1007/s10260-010-0137-9}.
 #' @references
+#'   Chen J, Variyath AM, Abraham B (2008).
+#'   ``Adjusted Empirical Likelihood and Its Properties.''
+#'   \emph{Journal of Computational and Graphical Statistics}, **17**(2),
+#'   426--443.
+#' @references
 #'   Kim E, MacEachern SN, Peruggia M (2024).
 #'   ``melt: Multiple Empirical Likelihood Tests in R.''
 #'   \emph{Journal of Statistical Software}, **108**(5), 1--33.
@@ -345,9 +351,15 @@ setGeneric("elmt", function(object,
 #' @seealso \linkS4class{EL}, \linkS4class{ELT}, [elmt()], [el_control()]
 #' @usage NULL
 #' @examples
-#' ## F calibration for the mean
+#' ## Adjusted empirical likelihood calibration
 #' data("precip")
 #' fit <- el_mean(precip, 32)
+#' elt(fit, rhs = 100, calibrate = "ael")
+#'
+#' ## Bootstrap calibration
+#' elt(fit, rhs = 32, calibrate = "boot")
+#'
+#' ## F calibration
 #' elt(fit, rhs = 32, calibrate = "f")
 #'
 #' ## Test of no treatment effect
diff --git a/R/calibrate.R b/R/calibrate.R
index e8f844cf..73339873 100644
--- a/R/calibrate.R
+++ b/R/calibrate.R
@@ -14,7 +14,7 @@
 #' @noRd
 calibrate <- function(calibrate, alpha, statistic, p, par, object, control) {
   switch(calibrate,
-    "chisq" = {
+    "ael" = {
       c(
         cv = qchisq(1 - alpha, df = p),
         pval = pchisq(statistic, df = p, lower.tail = FALSE)
@@ -32,6 +32,12 @@ calibrate <- function(calibrate, alpha, statistic, p, par, object, control) {
         control@maxit_l, control@tol_l, control@th, getWeights(object)
       )
     },
+    "chisq" = {
+      c(
+        cv = qchisq(1 - alpha, df = p),
+        pval = pchisq(statistic, df = p, lower.tail = FALSE)
+      )
+    },
     "f" = {
       n <- nrow(getData(object))
       c(
diff --git a/R/el_control.R b/R/el_control.R
index 98ae6214..5fa4b32a 100644
--- a/R/el_control.R
+++ b/R/el_control.R
@@ -41,6 +41,9 @@
 #'   non-overlapping but reproducible sequence of random numbers. The
 #'   Xoshiro256+ pseudo-random number generator is used internally to work with
 #'   OpenMP.
+#' @param an A single numeric representing the scaling factor for adjusted
+#'   empirical likelihood calibration. It only applies to [elt()] when
+#'   `calibrate` is set to `"ael"`. Defaults to `NULL`.
 #' @param b A single integer for the number of bootstrap replicates. It only
 #'   applies to [elt()] when `calibrate` is set to `"boot"`. Defaults to
 #'   `10000`.
@@ -61,6 +64,7 @@ el_control <- function(maxit = 200L,
                        keep_data = TRUE,
                        nthreads,
                        seed = NULL,
+                       an = NULL,
                        b = 10000L,
                        m = 1e+06L) {
   maxit <- assert_int(maxit, lower = 1L, coerce = TRUE)
@@ -92,11 +96,14 @@ el_control <- function(maxit = 200L,
   if (isFALSE(is.null(seed))) {
     seed <- assert_int(seed, coerce = TRUE)
   }
+  if (isFALSE(is.null(an))) {
+    an <- assert_number(an, lower = .Machine$double.eps, finite = TRUE)
+  }
   b <- assert_int(b, lower = 1L, coerce = TRUE)
   m <- assert_int(m, lower = 1L, coerce = TRUE)
   new("ControlEL",
     maxit = maxit, maxit_l = maxit_l, tol = tol, tol_l = tol_l, step = step,
     th = th, verbose = verbose, keep_data = keep_data, nthreads = nthreads,
-    seed = seed, b = b, m = m
+    seed = seed, an = an, b = b, m = m
   )
 }
diff --git a/R/elt-methods.R b/R/elt-methods.R
index 5115ced4..d4605237 100644
--- a/R/elt-methods.R
+++ b/R/elt-methods.R
@@ -18,8 +18,8 @@ setMethod("elt", "EL", function(object,
   onames <- names(logProb(object))
   pnames <- names(getOptim(object)$par)
   h <- validate_hypothesis(rhs, lhs, getNumPar(object), pnames)
-  alpha <- assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
-  calibrate <- validate_calibrate(calibrate)
+  assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
+  calibrate <- assert_choice(tolower(calibrate), c("ael", "boot", "chisq", "f"))
   method <- getMethodEL(object)
   maxit <- control@maxit
   maxit_l <- control@maxit_l
@@ -27,6 +27,7 @@ setMethod("elt", "EL", function(object,
   tol_l <- control@tol_l
   step <- control@step
   th <- control@th
+  an <- control@an
   w <- getWeights(object)
   if (is.null(lhs)) {
     stopifnot(
@@ -35,6 +36,17 @@ setMethod("elt", "EL", function(object,
     )
     par <- h$r
     out <- compute_EL(method, par, getData(object), maxit_l, tol_l, th, w)
+    if (isTRUE(calibrate == "ael")) {
+      if (is.null(an)) {
+        n <- nobs(object)
+        an <- log(n) / 2
+      }
+      g <- out$g
+      pg <- -an * colMeans(g)
+      g <- rbind(g, pg)
+      out <- compute_generic_EL(g, maxit_l, tol_l, th, w)
+      out$optim <- c(par = list(par), out$optim)
+    }
     optim <- validate_optim(out$optim)
     names(optim$par) <- pnames
     optim$cstr <- FALSE
@@ -50,9 +62,11 @@ setMethod("elt", "EL", function(object,
   }
   # Proceed with chi-square calibration for non-NULL `lhs`
   stopifnot(
-    "Bootstrap calibration is applicable only when `lhs` is NULL." =
+    "AEL calibration is applicable only when `lhs` is `NULL`." =
+      (calibrate != "ael"),
+    "Bootstrap calibration is applicable only when `lhs` is `NULL`." =
       (calibrate != "boot"),
-    "F calibration is applicable only when `lhs` is NULL." =
+    "F calibration is applicable only when `lhs` is `NULL`." =
       (calibrate != "f")
   )
   out <- test_hypothesis(
@@ -95,8 +109,8 @@ setMethod("elt", "QGLM", function(object,
   nm <- names(getOptim(object)$par)
   pnames <- nm[-getNumPar(object)]
   h <- validate_hypothesis(rhs, lhs, getNumPar(object) - 1L, pnames)
-  alpha <- assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
-  calibrate <- validate_calibrate(calibrate)
+  assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
+  assert_choice(tolower(calibrate), c("ael", "boot", "chisq", "f"))
   method <- getMethodEL(object)
   maxit <- control@maxit
   maxit_l <- control@maxit_l
@@ -104,6 +118,7 @@ setMethod("elt", "QGLM", function(object,
   tol_l <- control@tol_l
   step <- control@step
   th <- control@th
+  an <- control@an
   w <- getWeights(object)
   if (is.null(lhs)) {
     stopifnot(
@@ -115,6 +130,17 @@ setMethod("elt", "QGLM", function(object,
       par <- c(h$r, object@dispersion)
     }
     out <- compute_EL(method, par, getData(object), maxit_l, tol_l, th, w)
+    if (isTRUE(calibrate == "ael")) {
+      if (is.null(an)) {
+        n <- nobs(object)
+        an <- log(n) / 2
+      }
+      g <- out$g
+      pg <- -an * colMeans(g)
+      g <- rbind(g, pg)
+      out <- compute_generic_EL(g, maxit_l, tol_l, th, w)
+      out$optim <- c(par = list(par), out$optim)
+    }
     optim <- validate_optim(out$optim)
     names(optim$par) <- nm
     optim$cstr <- TRUE
@@ -124,12 +150,14 @@ setMethod("elt", "QGLM", function(object,
     return(new("ELT",
       optim = optim, logp = setNames(out$logp, onames), logl = out$logl,
       loglr = out$loglr, statistic = out$statistic, df = length(par),
-      pval = unname(cal["pval"]), cv = unname(cal["cv"]), rhs = h$r, lhs = h$l,
+      pval = unname(cal["pval"]), cv = unname(cal["cv"]), rhs = par, lhs = h$l,
       alpha = alpha, calibrate = calibrate, control = control
     ))
   }
   stopifnot(
-    "Bootstrap calibration is applicable only when `lhs` is NULL." =
+    "AEL calibration is applicable only when `lhs` is `NULL`." =
+      (calibrate != "ael"),
+    "Bootstrap calibration is applicable only when `lhs` is `NULL`." =
       (calibrate != "boot"),
     "F calibration is applicable only to the mean." = (calibrate != "f")
   )
@@ -172,11 +200,12 @@ setMethod("elt", "SD", function(object,
   onames <- names(logProb(object))
   pnames <- names(getOptim(object)$par)
   h <- validate_hypothesis(rhs, lhs, getNumPar(object), pnames)
-  alpha <- assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
-  calibrate <- validate_calibrate(calibrate)
+  assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
+  assert_choice(tolower(calibrate), c("ael", "boot", "chisq", "f"))
   maxit_l <- control@maxit_l
   tol_l <- control@tol_l
   th <- control@th
+  an <- control@an
   w <- getWeights(object)
   if (is.null(lhs)) {
     par <- h$r
@@ -188,6 +217,17 @@ setMethod("elt", "SD", function(object,
         (par >= .Machine$double.eps)
     )
     out <- compute_EL("sd", par, getData(object), maxit_l, tol_l, th, w)
+    if (isTRUE(calibrate == "ael")) {
+      if (is.null(an)) {
+        n <- nobs(object)
+        an <- log(n) / 2
+      }
+      g <- out$g
+      pg <- -an * colMeans(g)
+      g <- rbind(g, pg)
+      out <- compute_generic_EL(g, maxit_l, tol_l, th, w)
+      out$optim <- c(par = list(par), out$optim)
+    }
     optim <- validate_optim(out$optim)
     names(optim$par) <- pnames
     optim$cstr <- FALSE
@@ -200,6 +240,8 @@ setMethod("elt", "SD", function(object,
     ))
   }
   stopifnot(
+    "AEL calibration is applicable only when `lhs` is `NULL`." =
+      (calibrate != "ael"),
     "Bootstrap calibration is applicable only when `lhs` is `NULL`." =
       (calibrate != "boot"),
     "F calibration is applicable only when `lhs` is `NULL`." =
@@ -233,8 +275,8 @@ setMethod("elt", "missing", function(object,
                                      alpha = 0.05,
                                      calibrate = "chisq",
                                      control = NULL) {
-  alpha <- assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
-  calibrate <- validate_calibrate(calibrate)
+  assert_number(alpha, lower = 0, upper = 1, finite = TRUE)
+  assert_choice(tolower(calibrate), c("ael", "boot", "chisq", "f"))
   if (is.null(control)) {
     control <- el_control()
   } else {
diff --git a/R/melt-package.R b/R/melt-package.R
index fec3dcfe..fa4cbfd6 100644
--- a/R/melt-package.R
+++ b/R/melt-package.R
@@ -1,4 +1,5 @@
 ## usethis namespace: start
+#' @importFrom checkmate assert_choice
 #' @importFrom checkmate assert_class
 #' @importFrom checkmate assert_int
 #' @importFrom checkmate assert_logical
diff --git a/R/print-methods.R b/R/print-methods.R
index 915a0fd2..513a4cd6 100644
--- a/R/print-methods.R
+++ b/R/print-methods.R
@@ -57,8 +57,9 @@ setMethod("print", "ELT", function(x,
     x@rhs, x@lhs, names(getOptim(x)$par)[seq_len(ncol(x@lhs))], digits
   ), sep = "\n")
   method <- switch(x@calibrate,
-    "chisq" = "Chi-square",
+    "ael" = "Adjusted EL",
     "boot" = "Bootstrap",
+    "chisq" = "Chi-square",
     "f" = "F"
   )
   cat(paste0(
@@ -209,8 +210,9 @@ setMethod(
       x@rhs, x@lhs, names(getOptim(x)$par)[seq_len(ncol(x@lhs))], digits
     ), sep = "\n")
     method <- switch(x@calibrate,
-      "chisq" = "Chi-square",
+      "ael" = "Adjusted EL",
       "boot" = "Bootstrap",
+      "chisq" = "Chi-square",
       "f" = "F"
     )
     cat(paste0(
@@ -265,7 +267,7 @@ setMethod(
       cat("\nLagrange multipliers:\n")
       print.default(getOptim(x)$lambda, digits = digits, ...)
       cat("\nMaximum EL estimates:\n")
-      print.default(coef(x)[,1L], digits = digits, ...)
+      print.default(coef(x)[, 1L], digits = digits, ...)
       cat(paste(
         "\nlogL:", format.default(logL(x), digits = digits, ...),
         ", logLR:", format.default(logLR(x), digits = digits, ...)
@@ -337,7 +339,7 @@ setMethod(
       cat("\nLagrange multipliers:\n")
       print.default(getOptim(x)$lambda, digits = digits, ...)
       cat("\nMaximum EL estimates:\n")
-      print.default(coef(x)[,1L], digits = digits, ...)
+      print.default(coef(x)[, 1L], digits = digits, ...)
       cat(paste(
         "\nlogL:", format.default(logL(x), digits = digits, ...),
         ", logLR:", format.default(logLR(x), digits = digits, ...)
diff --git a/R/validate.R b/R/validate.R
index c94d74a6..fc681ee5 100644
--- a/R/validate.R
+++ b/R/validate.R
@@ -1,23 +1,3 @@
-#' Validate `calibrate`
-#'
-#' Validate `calibrate` in [elt()].
-#'
-#' @param calibrate A single character.
-#' @return A single character.
-#' @noRd
-validate_calibrate <- function(calibrate) {
-  assert_string(calibrate)
-  table <- c("chisq", "boot", "f")
-  calibrate <- table[pmatch(tolower(calibrate), table = table)]
-  if (isTRUE(is.na(calibrate))) {
-    stop(gettextf(
-      "`calibrate` must be one of %s, %s, or %s.",
-      dQuote("chisq"), dQuote("boot"), dQuote("f")
-    ), domain = NA)
-  }
-  calibrate
-}
-
 #' Validate `cv`
 #'
 #' Validate `cv` in [confint()] and [confreg()].
diff --git a/README.Rmd b/README.Rmd
index 5b853ee4..b89610c1 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 [![R-CMD-check](https://github.com/ropensci/melt/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ropensci/melt/actions/workflows/R-CMD-check.yaml)
 [![pkgcheck](https://github.com/ropensci/melt/workflows/pkgcheck/badge.svg)](https://github.com/ropensci/melt/actions?query=workflow%3Apkgcheck)
-[![Codecov test coverage](https://codecov.io/gh/ropensci/melt/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ropensci/melt?branch=master)
+[![Codecov test coverage](https://codecov.io/gh/ropensci/melt/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ropensci/melt?branch=main)
 [![CRAN status](https://www.r-pkg.org/badges/version/melt)](https://CRAN.R-project.org/package=melt)
 [![runiverse](https://ropensci.r-universe.dev/badges/melt)](http://ropensci.r-universe.dev/ui/#package:melt)
 [![ropensci review](https://badges.ropensci.org/550_status.svg)](https://github.com/ropensci/software-review/issues/550)
@@ -67,7 +67,7 @@ melt provides an intuitive API for performing the most common data analysis task
 * `el_glm()` fits a generalized linear model with empirical likelihood.
 * `confint()` computes confidence intervals for model parameters.
 * `confreg()` computes confidence region for model parameters.
-* `elt()` tests a linear hypothesis.
+* `elt()` tests a hypothesis with various calibration options.
 * `elmt()` performs multiple testing simultaneously.
 
 
@@ -78,7 +78,19 @@ set.seed(971112)
 
 ## Test for the mean
 data("precip")
-el_mean(precip, par = 30)
+(fit <- el_mean(precip, par = 30))
+
+
+## Adjusted empirical likelihood calibration
+elt(fit, rhs = 30, calibrate = "ael")
+
+
+## Bootstrap calibration
+elt(fit, rhs = 30, calibrate = "boot")
+
+
+## F calibration
+elt(fit, rhs = 30, calibrate = "f")
 
 
 ## Linear model
diff --git a/README.md b/README.md
index 858d170f..76cb294b 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.re
 [![R-CMD-check](https://github.com/ropensci/melt/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ropensci/melt/actions/workflows/R-CMD-check.yaml)
 [![pkgcheck](https://github.com/ropensci/melt/workflows/pkgcheck/badge.svg)](https://github.com/ropensci/melt/actions?query=workflow%3Apkgcheck)
 [![Codecov test
-coverage](https://codecov.io/gh/ropensci/melt/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ropensci/melt?branch=master)
+coverage](https://codecov.io/gh/ropensci/melt/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ropensci/melt?branch=main)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/melt)](https://CRAN.R-project.org/package=melt)
 [![runiverse](https://ropensci.r-universe.dev/badges/melt)](http://ropensci.r-universe.dev/ui/#package:melt)
@@ -69,7 +69,7 @@ analysis tasks:
 - `el_glm()` fits a generalized linear model with empirical likelihood.
 - `confint()` computes confidence intervals for model parameters.
 - `confreg()` computes confidence region for model parameters.
-- `elt()` tests a linear hypothesis.
+- `elt()` tests a hypothesis with various calibration options.
 - `elmt()` performs multiple testing simultaneously.
 
 ## Usage
@@ -80,7 +80,7 @@ set.seed(971112)
 
 ## Test for the mean
 data("precip")
-el_mean(precip, par = 30)
+(fit <- el_mean(precip, par = 30))
 #> 
 #>  Empirical Likelihood
 #> 
@@ -93,6 +93,51 @@ el_mean(precip, par = 30)
 #> EL evaluation: converged
 
 
+## Adjusted empirical likelihood calibration
+elt(fit, rhs = 30, calibrate = "ael")
+#> 
+#>  Empirical Likelihood Test
+#> 
+#> Hypothesis:
+#> par = 30
+#> 
+#> Significance level: 0.05, Calibration: Adjusted EL 
+#> 
+#> Statistic: 7.744, Critical value: 3.841
+#> p-value: 0.005389 
+#> EL evaluation: converged
+
+
+## Bootstrap calibration
+elt(fit, rhs = 30, calibrate = "boot")
+#> 
+#>  Empirical Likelihood Test
+#> 
+#> Hypothesis:
+#> par = 30
+#> 
+#> Significance level: 0.05, Calibration: Bootstrap 
+#> 
+#> Statistic: 8.285, Critical value: 3.84
+#> p-value: 0.0041 
+#> EL evaluation: converged
+
+
+## F calibration
+elt(fit, rhs = 30, calibrate = "f")
+#> 
+#>  Empirical Likelihood Test
+#> 
+#> Hypothesis:
+#> par = 30
+#> 
+#> Significance level: 0.05, Calibration: F 
+#> 
+#> Statistic: 8.285, Critical value: 3.98
+#> p-value: 0.005318 
+#> EL evaluation: converged
+
+
 ## Linear model
 data("mtcars")
 fit_lm <- el_lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
@@ -226,13 +271,13 @@ summary(fit_glm)
 #> 
 #> Coefficients:
 #>              Estimate   Chisq Pr(>Chisq)    
-#> (Intercept)  -0.10977   0.090   0.763757    
+#> (Intercept)  -0.10977   0.090      0.764    
 #> log(mass)     0.24750 425.859    < 2e-16 ***
-#> fruit         0.04654  11.584   0.000665 ***
+#> fruit         0.04654  29.024   7.15e-08 ***
 #> foliage     -19.40632  65.181   6.83e-16 ***
 #> varGZ        -0.25760  17.308   3.18e-05 ***
-#> trtSpray      0.06724   0.860   0.353820    
-#> trtFurrow    -0.03634   0.217   0.641379    
+#> trtSpray      0.06724   0.860      0.354    
+#> trtFurrow    -0.03634   0.217      0.641    
 #> trtSeed       0.34790  19.271   1.13e-05 ***
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
diff --git a/codemeta.json b/codemeta.json
index ec6e74ac..416a70f0 100644
--- a/codemeta.json
+++ b/codemeta.json
@@ -8,7 +8,7 @@
   "codeRepository": "https://github.com/ropensci/melt",
   "issueTracker": "https://github.com/ropensci/melt/issues",
   "license": "https://spdx.org/licenses/GPL-2.0",
-  "version": "1.11.2",
+  "version": "1.11.3",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -291,7 +291,7 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "300699.916KB",
+  "fileSize": "367508.966KB",
   "citation": [
     {
       "@type": "ScholarlyArticle",
@@ -334,7 +334,7 @@
     }
   ],
   "releaseNotes": "https://github.com/ropensci/melt/blob/master/NEWS.md",
-  "contIntegration": ["https://github.com/ropensci/melt/actions/workflows/R-CMD-check.yaml", "https://github.com/ropensci/melt/actions?query=workflow%3Apkgcheck", "https://app.codecov.io/gh/ropensci/melt?branch=master"],
+  "contIntegration": ["https://github.com/ropensci/melt/actions/workflows/R-CMD-check.yaml", "https://github.com/ropensci/melt/actions?query=workflow%3Apkgcheck", "https://app.codecov.io/gh/ropensci/melt?branch=main"],
   "developmentStatus": "https://www.repostatus.org/#active",
   "review": {
     "@type": "Review",
diff --git a/inst/WORDLIST b/inst/WORDLIST
index c67b68eb..25142869 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -39,7 +39,6 @@ LM
 Lazar
 MELE
 Mroz
-NA's
 Neonicotinoid
 Obregon
 OpenMP
@@ -49,7 +48,6 @@ Pederson
 Poveda
 QGLM
 Qin
-Qu
 Raphson
 Rcpp
 RcppEigen
@@ -59,35 +57,28 @@ R’
 SES
 SPM
 SX
-Signif
 Sinica
 Statistica
 Thiamethoxam
 UScrime
+Variyath
 Wilks
 Xoshiro
 Zhang
 Zhao
 Zhu
 agricolae
-byrow
-calpha
 carData
-cd
-cex
 chisq
 clothianidin
 coef
 coercible
 colMeans
-conf
 confint
 confreg
 conv
-cr
 critVal
 cstr
-ctrl
 cv
 df
 doi
@@ -111,7 +102,6 @@ glht
 glm
 gmm
 hc
-hcyes
 iteratively
 jss
 lfp
@@ -123,55 +113,39 @@ logProb
 logl
 loglr
 logp
-lwd
-lwg
-lwr
 maxit
 misspecification
 momentfit
 multcomp
-ncol
 neonicotinoid
 nonconvex
 npoints
-nrow
 nthreads
 optim
 optimality
 pVal
 parallelization
-parm
-pch
 piecewiseSEM
 pkgVersion
 pkgcheck
 priori
 pval
-quasipoisson
 rOpenSci
 ridgeline
 ropensci
 runiverse
-scb
 sd
 stars’
 straightneck
 studentization
-tck
 th
 thiamethoxam
 tidyverse
 tol
 translocates
 trt
-trtFurrow
-trtSeed
-trtSpray
-upr
-varGZ
 vcov
 wc
-wcyes
 xs
 ys
 µg
diff --git a/man/ControlEL-class.Rd b/man/ControlEL-class.Rd
index b9d45903..5e7f8813 100644
--- a/man/ControlEL-class.Rd
+++ b/man/ControlEL-class.Rd
@@ -43,6 +43,9 @@ computation via OpenMP (if available).}
 
 \item{\code{seed}}{A single integer for the seed for random number generation.}
 
+\item{\code{an}}{A single numeric representing the scaling factor for adjusted
+empirical likelihood calibration.}
+
 \item{\code{b}}{A single integer for the number of bootstrap replicates.}
 
 \item{\code{m}}{A single integer for the number of Monte Carlo samples.}
diff --git a/man/el_control.Rd b/man/el_control.Rd
index b9ca6712..c076619f 100644
--- a/man/el_control.Rd
+++ b/man/el_control.Rd
@@ -15,6 +15,7 @@ el_control(
   keep_data = TRUE,
   nthreads,
   seed = NULL,
+  an = NULL,
   b = 10000L,
   m = 1000000L
 )
@@ -69,6 +70,10 @@ non-overlapping but reproducible sequence of random numbers. The
 Xoshiro256+ pseudo-random number generator is used internally to work with
 OpenMP.}
 
+\item{an}{A single numeric representing the scaling factor for adjusted
+empirical likelihood calibration. It only applies to \code{\link[=elt]{elt()}} when
+\code{calibrate} is set to \code{"ael"}. Defaults to \code{NULL}.}
+
 \item{b}{A single integer for the number of bootstrap replicates. It only
 applies to \code{\link[=elt]{elt()}} when \code{calibrate} is set to \code{"boot"}. Defaults to
 \code{10000}.}
diff --git a/man/elt.Rd b/man/elt.Rd
index e7516df5..55685c5c 100644
--- a/man/elt.Rd
+++ b/man/elt.Rd
@@ -32,9 +32,9 @@ hypothesis is allowed. Defaults to \code{NULL}. See ‘Details’.}
 
 \item{alpha}{A single numeric for the significance level. Defaults to \code{0.05}.}
 
-\item{calibrate}{A single character for the calibration method. It is
-case-insensitive and must be one of \code{"chisq"}, \code{"boot"}, or \code{"f"}.
-Defaults to \code{"chisq"}. See ‘Details’.}
+\item{calibrate}{A single character representing the calibration method. It
+is case-insensitive and must be one of \code{"ael"}, \code{"boot"}, \code{"chisq"}, or
+\code{"f"}. Defaults to \code{"chisq"}. See ‘Details’.}
 
 \item{control}{An object of class \linkS4class{ControlEL} constructed by
 \code{\link[=el_control]{el_control()}}. Defaults to \code{NULL} and inherits the \code{control} slot in
@@ -46,7 +46,7 @@ An object of class of \linkS4class{ELT}. If \code{lhs} is non-\code{NULL}, the
 corresponds to that of \linkS4class{EL}.
 }
 \description{
-Tests a linear hypothesis.
+Tests a linear hypothesis with various calibration options.
 }
 \details{
 \code{\link[=elt]{elt()}} performs the constrained minimization of \eqn{l(\theta)}
@@ -68,17 +68,24 @@ is performed with the right-hand side \eqn{r} and the left-hand side
 problem reduces to evaluating at \eqn{r} as \eqn{l(r)}.
 }
 
-\code{calibrate} specifies the calibration method used. Three methods are
-available: \code{"chisq"} (chi-square calibration), \code{"boot"} (bootstrap
-calibration), and \code{"f"} (\eqn{F} calibration). \code{"boot"} is applicable only
-when \code{lhs} is \code{NULL}. The \code{nthreads}, \code{seed}, and \code{B} slots in \code{control}
-apply to the bootstrap procedure. \code{"f"} is applicable only to the mean
-parameter when \code{lhs} is \code{NULL}.
+\code{calibrate} specifies the calibration method used. Four methods are
+available: \code{"ael"} (adjusted empirical likelihood calibration), \code{"boot"}
+(bootstrap calibration), \code{"chisq"} (chi-square calibration), and \code{"f"}
+(\eqn{F} calibration). When \code{lhs} is not \code{NULL}, only \code{"chisq"} is
+available. \code{"f"} is applicable only to the mean parameter. The \code{an} slot in
+\code{control} applies specifically to \code{"ael"}, while the \code{nthreads}, \code{seed},
+and \code{B} slots apply to \code{"boot"}.
 }
 \examples{
-## F calibration for the mean
+## Adjusted empirical likelihood calibration
 data("precip")
 fit <- el_mean(precip, 32)
+elt(fit, rhs = 100, calibrate = "ael")
+
+## Bootstrap calibration
+elt(fit, rhs = 32, calibrate = "boot")
+
+## F calibration
 elt(fit, rhs = 32, calibrate = "f")
 
 ## Test of no treatment effect
@@ -104,6 +111,11 @@ Adimari G, Guolo A (2010).
 \emph{Statistical Methods & Applications}, \strong{19}(4), 463--476.
 \doi{10.1007/s10260-010-0137-9}.
 
+Chen J, Variyath AM, Abraham B (2008).
+``Adjusted Empirical Likelihood and Its Properties.''
+\emph{Journal of Computational and Graphical Statistics}, \strong{17}(2),
+426--443.
+
 Kim E, MacEachern SN, Peruggia M (2024).
 ``melt: Multiple Empirical Likelihood Tests in R.''
 \emph{Journal of Statistical Software}, \strong{108}(5), 1--33.
diff --git a/src/EL.cpp b/src/EL.cpp
index 4ca055f0..4ad540e8 100644
--- a/src/EL.cpp
+++ b/src/EL.cpp
@@ -18,17 +18,18 @@ EL::EL(const std::string method,
        const double tol_l,
        const double th,
        const Eigen::Ref<const Eigen::ArrayXd> &wt)
-    : par{par0},
-      l{Eigen::VectorXd::Zero(par0.size())},
-      mele_fn{set_mele_fn(method)},
-      w{wt},
-      maxit_l{maxit_l},
+    : maxit_l{maxit_l},
       tol_l{tol_l},
       th{th},
       n{static_cast<int>(x.rows())},
-      g_fn{EL::set_g_fn(method)}
+      g_fn{EL::set_g_fn(method)},
+      par{par0},
+      l{Eigen::VectorXd::Zero(par0.size())},
+      mele_fn{set_mele_fn(method)},
+      w{wt},
+      g{g_fn(x, par)}
 {
-  set_el(g_fn(x, par), wt);
+  set_el(g, wt);
 }
 
 EL::EL(const Eigen::Ref<const Eigen::MatrixXd> &g,
@@ -36,15 +37,16 @@ EL::EL(const Eigen::Ref<const Eigen::MatrixXd> &g,
        const double tol_l,
        const double th,
        const Eigen::Ref<const Eigen::ArrayXd> &wt)
-    : par{},
-      l{Eigen::VectorXd::Zero(g.cols())},
-      mele_fn{},
-      w{wt},
-      maxit_l{maxit_l},
+    : maxit_l{maxit_l},
       tol_l{tol_l},
       th{th},
       n{static_cast<int>(g.rows())},
-      g_fn{}
+      g_fn{},
+      par{},
+      l{Eigen::VectorXd::Zero(g.cols())},
+      mele_fn{},
+      w{wt},
+      g{g}
 {
   set_el(g, wt);
 }
diff --git a/src/EL.h b/src/EL.h
index e961ed3e..edfe05d9 100644
--- a/src/EL.h
+++ b/src/EL.h
@@ -7,6 +7,18 @@
 
 class EL
 {
+private:
+  // members
+  const int maxit_l;  // maximum number of iterations
+  const double tol_l; // relative convergence tolerance
+  const double th;    // threshold value for negative log-likelihood ratio
+  const int n;        // sample size
+  // estimating function
+  const std::function<Eigen::MatrixXd(
+      const Eigen::Ref<const Eigen::MatrixXd> &,
+      const Eigen::Ref<const Eigen::VectorXd> &)>
+    g_fn;
+
 public:
   // members
   const Eigen::VectorXd par; // parameter value specified
@@ -18,6 +30,7 @@ class EL
   int iter{0};            // iterations performed in optimization
   bool conv{false};       // convergence status
   const Eigen::ArrayXd w; // weights
+  const Eigen::MatrixXd g;
 
   // constructors
   EL(const std::string method,
@@ -50,17 +63,7 @@ class EL
   // log-likelihood
   double loglik() const;
 
-private:
-  // members
-  const int maxit_l;  // maximum number of iterations
-  const double tol_l; // relative convergence tolerance
-  const double th;    // threshold value for negative log-likelihood ratio
-  const int n;        // sample size
-  // estimating function
-  const std::function<Eigen::MatrixXd(
-      const Eigen::Ref<const Eigen::MatrixXd> &,
-      const Eigen::Ref<const Eigen::VectorXd> &)>
-      g_fn;
+
 };
 
 class CEL
diff --git a/src/compute_EL.cpp b/src/compute_EL.cpp
index 57d9cdb0..2bca2718 100644
--- a/src/compute_EL.cpp
+++ b/src/compute_EL.cpp
@@ -21,6 +21,7 @@ Rcpp::List compute_EL(const std::string method,
             Rcpp::Named("lambda") = el.l,
             Rcpp::Named("iterations") = el.iter,
             Rcpp::Named("convergence") = el.conv),
+        Rcpp::Named("g") = el.g,
         Rcpp::Named("logp") = el.logp(x),
         Rcpp::Named("logl") = el.loglik(),
         Rcpp::Named("loglr") = -el.nllr,
diff --git a/tests/testthat/test-el_control.R b/tests/testthat/test-el_control.R
index 667d0f65..29061ef3 100644
--- a/tests/testthat/test-el_control.R
+++ b/tests/testthat/test-el_control.R
@@ -16,4 +16,5 @@ test_that("Invalid `control`.", {
   expect_error(el_control(nthreads = c(10, 20)))
   expect_error(el_control(nthreads = 0))
   expect_warning(el_control(nthreads = .Machine$integer.max))
+  expect_error(el_control(an = 0))
 })
diff --git a/tests/testthat/test-elt.R b/tests/testthat/test-elt.R
index d693007d..a899378a 100644
--- a/tests/testthat/test-elt.R
+++ b/tests/testthat/test-elt.R
@@ -72,6 +72,9 @@ test_that("When elt == evaluation.", {
   rhs <- c(0, 0)
   wfit4 <- elt(wfit3, rhs = rhs, lhs = lhs)
   expect_equal(getOptim(wfit3)$lambda, getOptim(wfit4)$lambda)
+  expect_error(elt(fit, lhs = c("par"), rhs = 10, calibrate = "ael"))
+  out <- elt(fit, rhs = 10, calibrate = "ael")
+  expect_s4_class(out, "ELT")
 })
 
 test_that("`conv()` method and calibration.", {
@@ -81,8 +84,8 @@ test_that("`conv()` method and calibration.", {
 
 test_that("Probabilities add up to 1.", {
   fit <- el_mean(precip, par = 60)
-  elt <- elt(fit, rhs = 65)
-  expect_equal(sum(exp(logProb(elt))), 1, tolerance = 1e-07)
+  out <- elt(fit, rhs = 65)
+  expect_equal(sum(exp(logProb(out))), 1, tolerance = 1e-07)
 })
 
 test_that("Vector `lhs`.", {
@@ -130,11 +133,14 @@ test_that("`SD` class.", {
   expect_error(elt(fit, rhs = -1))
   expect_error(elt(fit, rhs = rhs, lhs = lhs, calibrate = "boot"))
   expect_error(elt(fit, rhs = rhs, lhs = lhs, calibrate = "f"))
+  expect_error(elt(fit, rhs = -1, lhs = 1, calibrate = "ael"))
   expect_error(elt(fit, rhs = -1, lhs = 1, calibrate = "f"))
   out <- elt(fit, rhs = 1)
   out2 <- elt(fit, rhs = 1, lhs = 2)
+  out3 <- elt(fit, rhs = 1, calibrate = "ael")
   expect_s4_class(out, "ELT")
   expect_s4_class(out2, "ELT")
+  expect_s4_class(out3, "ELT")
 })
 
 test_that("`QGLM` class.", {
@@ -142,7 +148,9 @@ test_that("`QGLM` class.", {
     family = quasipoisson("log"), data = mtcars
   )
   out <- elt(fit, rhs = coef(fit))
-  expect_s4_class(out, "ELT")
   out2 <- elt(fit, lhs = c("mpg + cyl"))
+  out3 <- elt(fit, rhs = c(11, 0, 7), calibrate = "ael")
+  expect_s4_class(out, "ELT")
   expect_s4_class(out2, "ELT")
+  expect_s4_class(out3, "ELT")
 })
diff --git a/vignettes/article.Rnw b/vignettes/article.Rnw
index c127cd66..251eddbb 100644
--- a/vignettes/article.Rnw
+++ b/vignettes/article.Rnw
@@ -769,16 +769,16 @@ specify the problem in Equation~\ref{eq:constraint}.
 %
 \begin{itemize}
 \item \fct{elt}: Tests a linear hypothesis with EL. It returns an object of
-  class \class{ELT} that contains the test statistic, the critical value, and
-    the level of the test.
-Several calibration options discussed in Section~\ref{sec:2.2} are available,
-    and the \(p\)~value is computed by the calibration method chosen.
+class \class{ELT} that contains the test statistic, the critical value, and the
+level of the test. Various calibration options are available, including some of
+those discussed in Section~\ref{sec:2.2} and the adjusted empirical likelihood
+calibration method proposed by \citet{chen2008adjusted}. The \(p\)~value is
+computed based on the chosen calibration method.
 \item \fct{elmt}: Tests multiple linear hypotheses simultaneously with EL.
 Each test can be considered as one instance of \fct{elt}, where the marginal
-    test statistic has an asymptotic chi-square distribution under the
-    corresponding null hypothesis.
-It returns an object of class \class{ELMT} with slots similar to those in
-    \class{ELT}.
+test statistic has an asymptotic chi-square distribution under the corresponding
+null hypothesis. It returns an object of class \class{ELMT} with slots similar
+to those in \class{ELT}.
 \end{itemize}
 An \class{ELT} object also has the \code{optim} slot, which does not necessarily
 correspond to the EL optimization. The user can supply an arbitrary parameter
@@ -832,7 +832,7 @@ The default values are shown below.
 \begin{Code}
 el_control(maxit = 200L, maxit_l = 25L, tol = 1e-06, tol_l = 1e-06,
   step = NULL, th = NULL, verbose = FALSE, keep_data = TRUE, nthreads,
-  seed = NULL, b = 10000L, m = 1000000L)
+  seed = NULL, an = NULL, b = 10000L, m = 1000000L)
 \end{Code}
 Specifically, \code{nthreads} specifies the number of threads for parallel
 computation via \pkg{OpenMP} (if available). By default, it is set to half the
diff --git a/vignettes/references.bib b/vignettes/references.bib
index dc079e98..e3abd63d 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -59,6 +59,17 @@ @Article{chaudhuri2017hamiltonian
   year = {2017},
 }
 
+@Article{chen2008adjusted,
+  title = {Adjusted Empirical Likelihood and its Properties},
+  author = {Jiahua Chen and Asokan Mulayath Variyath and Bovas Abraham},
+  journal = {Journal of Computational and Graphical Statistics},
+  volume = {17},
+  number = {2},
+  pages = {426--443},
+  year = {2008},
+  doi = {10.1198/106186008X321068},
+}
+
 @Article{chen2009effects,
   author = {Song Xi Chen and Liang Peng and Ying-Li Qin},
   title = {Effects of Data Dimension on Empirical Likelihood},
@@ -723,7 +734,7 @@ @Manual{melt
   title = {\pkg{melt}: Multiple Empirical Likelihood Tests},
   author = {Eunseop Kim},
   year = {2024},
-  note = {\proglang{R} package version 1.11.1},
+  note = {\proglang{R} package version 1.11.3},
   url = {https://CRAN.R-project.org/package=melt},
 }