diff --git a/DESCRIPTION b/DESCRIPTION
index 7272d09..787049f 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -27,7 +27,7 @@ Depends:
     tern (>= 0.9.5)
 Imports:
     checkmate (>= 2.1.0),
-    emmeans (>= 1.4.5),
+    emmeans (>= 1.10.4),
     formatters (>= 0.5.8),
     geeasy,
     geepack,
diff --git a/NEWS.md b/NEWS.md
index b17ef98..912e67c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
 # tern.gee 0.1.4.9004
 
+* Up version `emmeans` package version to 1.10.4.
+
 # tern.gee 0.1.4
 
 * Change package maintainer to Joe Zhu.
diff --git a/R/lsmeans.R b/R/lsmeans.R
index 0fef130..18e57f2 100644
--- a/R/lsmeans.R
+++ b/R/lsmeans.R
@@ -4,6 +4,7 @@
 #' @param conf_level (`proportion`)\cr confidence level
 #' @param weights (`string`)\cr type of weights to be used for the least square means,
 #'   see [emmeans::emmeans()] for details.
+#' @param specs (`string` or `formula`) specifications passed to [emmeans::emmeans()]
 #' @param ... additional arguments for methods
 #'
 #' @return A `data.frame` with least-square means and contrasts. Additional
@@ -21,6 +22,7 @@
 lsmeans <- function(object,
                     conf_level = 0.95,
                     weights = "proportional",
+                    specs = object$vars$arm,
                     ...) {
   UseMethod("lsmeans", object)
 }
@@ -30,8 +32,8 @@ lsmeans <- function(object,
 lsmeans.tern_gee_logistic <- function(object,
                                       conf_level = 0.95,
                                       weights = "proportional",
+                                      specs = object$vars$arm,
                                       ...) {
-  specs <- as.formula(paste("~", object$vars$arm))
   prop_emm <- emmeans::emmeans(
     object = object,
     specs = specs,
@@ -41,7 +43,7 @@ lsmeans.tern_gee_logistic <- function(object,
   )
 
   prop_df <- cbind(
-    data.frame(stats::confint(prop_emm))[, c(object$vars$arm, "prob", "SE", "asymp.LCL", "asymp.UCL")],
+    data.frame(stats::confint(prop_emm))[, c(object$vars$arm, "prob", "SE", "lower.CL", "upper.CL")],
     n = as.list(prop_emm)$extras[, ".wgt."]
   )
   names(prop_df) <- c(object$vars$arm, "prop_est", "prop_est_se", "prop_lower_cl", "prop_upper_cl", "n")
@@ -57,7 +59,7 @@ lsmeans.tern_gee_logistic <- function(object,
   or_df <- or_df[or_df$comparator == ref_level, ]
   or_df <- rbind(NA, or_df)
   or_df[1L, object$vars$arm] <- ref_level
-  or_df <- or_df[, c(object$vars$arm, "odds.ratio", "asymp.LCL", "asymp.UCL")]
+  or_df <- or_df[, c(object$vars$arm, "odds.ratio", "lower.CL", "upper.CL")]
   or_df <- cbind(or_df, log(or_df[, -1L]), conf_level)
   names(or_df) <- c(
     object$vars$arm,
diff --git a/man/lsmeans.Rd b/man/lsmeans.Rd
index 210a3df..bf22a95 100644
--- a/man/lsmeans.Rd
+++ b/man/lsmeans.Rd
@@ -5,9 +5,21 @@
 \alias{lsmeans.tern_gee_logistic}
 \title{Extract Least Square Means from a GEE Model}
 \usage{
-lsmeans(object, conf_level = 0.95, weights = "proportional", ...)
+lsmeans(
+  object,
+  conf_level = 0.95,
+  weights = "proportional",
+  specs = object$vars$arm,
+  ...
+)
 
-\method{lsmeans}{tern_gee_logistic}(object, conf_level = 0.95, weights = "proportional", ...)
+\method{lsmeans}{tern_gee_logistic}(
+  object,
+  conf_level = 0.95,
+  weights = "proportional",
+  specs = object$vars$arm,
+  ...
+)
 }
 \arguments{
 \item{object}{(\code{tern_gee})\cr result of \code{\link[=fit_gee]{fit_gee()}}.}
@@ -17,6 +29,8 @@ lsmeans(object, conf_level = 0.95, weights = "proportional", ...)
 \item{weights}{(\code{string})\cr type of weights to be used for the least square means,
 see \code{\link[emmeans:emmeans]{emmeans::emmeans()}} for details.}
 
+\item{specs}{(\code{string} or \code{formula}) specifications passed to \code{\link[emmeans:emmeans]{emmeans::emmeans()}}}
+
 \item{...}{additional arguments for methods}
 }
 \value{