diff --git a/NEWS.md b/NEWS.md
index 9f20054b9199276760eacdf58106a2ce0c520e12..2dd1570b127f4cf597633b8adbf810e47e82d485 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,6 @@
 # s2dv 0.0.2 (Release date: 2020-)
+- Change Regression() parameter 'time_dim' to 'reg_dim', and enable the inputs to be vectors.
+- Change Trend() parameter 'time_dim' default value from 'sdate' to 'ftime'.
 - Change the default of Season() parameter 'time_dim' from 'sdate' to 'ftime'.
 - Add p-value by ANOVA in Trend().
 - Change MeanDims() na.rm default to FALSE to be in line with mean()
diff --git a/R/Clim.R b/R/Clim.R
index 95701191a5741c36104a4e98491520ba0b24ddf2..d879fc4f4fdba572efeb4308ded28ea1ce41b799 100644
--- a/R/Clim.R
+++ b/R/Clim.R
@@ -335,10 +335,10 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'),
     tmp <- Subset(obs, ftime_dim, 1, drop = 'selected')
     ini_obs <- InsertDim(tmp, pos_ftime, dim_ftime) #only first ftime
     #ini_: [sdate, dat_dim, ftime]
-    tmp_exp <- Regression(datay = exp, datax = ini_exp, time_dim = time_dim,
+    tmp_exp <- Regression(datay = exp, datax = ini_exp, reg_dim = time_dim,
                           na.action = na.omit,
                           pval = FALSE, conf = FALSE)$regression
-    tmp_obs <- Regression(datay = obs, datax = ini_obs, time_dim = time_dim, 
+    tmp_obs <- Regression(datay = obs, datax = ini_obs, reg_dim = time_dim, 
                           na.action = na.omit, 
                           pval = FALSE, conf = FALSE)$regression
     #tmp_: [stats = 2, dat_dim, ftime]
diff --git a/R/Regression.R b/R/Regression.R
index 1e0c79b24c1e34f7d2875bcae03daa170c73d272..8b2bd9c3c73d4b90122f0f6623957882ddd1c410 100644
--- a/R/Regression.R
+++ b/R/Regression.R
@@ -1,7 +1,7 @@
 #'Compute the regression of an array on another along one dimension.
 #'
 #'Compute the regression of the array 'datay' on the array 'datax' along the
-#''time_dim' dimension by least square fitting (default) or self-defined model. 
+#''reg_dim' dimension by least square fitting (default) or self-defined model. 
 #'The function provides the slope of the regression, the intercept, and the
 #'associated p-value and confidence interval. The filtered datay from the 
 #'regression onto datax is also provided.\cr
@@ -12,8 +12,8 @@
 #'  which the regression is computed.
 #'@param datax An numeric array as predictor. The dimension should be identical
 #'  as parameter 'datay'.
-#'@param time_dim A character string indicating the dimension along which to 
-#'  compute the regression.
+#'@param reg_dim A character string indicating the dimension along which to 
+#'  compute the regression. The default value is 'sdate'.
 #'@param formula An object of class "formula" (see function \code{link[stats]{lm}}).
 #'@param pval A logical value indicating whether to retrieve the p-value 
 #'  or not. The default value is TRUE.
@@ -35,14 +35,14 @@
 #'A list containing:
 #'\item{$regression}{
 #'  A numeric array with same dimensions as parameter 'datay' and 'datax' except 
-#'  the 'time_dim' dimension, which is replaced by a 'stats' dimension containing 
+#'  the 'reg_dim' dimension, which is replaced by a 'stats' dimension containing 
 #'  the regression coefficients from the lowest order (i.e., intercept) to 
 #'  the highest degree. The length of the 'stats' dimension should be 
 #'  \code{polydeg + 1}.  
 #'}
 #'\item{$conf.lower}{
 #'  A numeric array with same dimensions as parameter 'daty' and 'datax' except
-#'  the 'time_dim' dimension, which is replaced by a 'stats' dimension containing 
+#'  the 'reg_dim' dimension, which is replaced by a 'stats' dimension containing 
 #'  the lower value of the \code{siglev}\% confidence interval for all
 #'  the regression coefficients with the same order as $regression. The length 
 #'  of 'stats' dimension should be \code{polydeg + 1}. Only present if 
@@ -50,7 +50,7 @@
 #'}
 #'\item{$conf.upper}{
 #'  A numeric array with same dimensions as parameter 'daty' and 'datax' except
-#'  the 'time_dim' dimension, which is replaced by a 'stats' dimension containing 
+#'  the 'reg_dim' dimension, which is replaced by a 'stats' dimension containing 
 #'  the upper value of the \code{siglev}\% confidence interval for all
 #'  the regression coefficients with the same order as $regression. The length 
 #'  of 'stats' dimension should be \code{polydeg + 1}. Only present if 
@@ -58,11 +58,11 @@
 #'}
 #'\item{$p.val}{
 #'  A numeric array with same dimensions as parameter 'daty' and 'datax' except
-#'  the 'time_dim' dimension, The array contains the p-value.
+#'  the 'reg_dim' dimension, The array contains the p-value.
 #'}
 #'\item{$filtered}{
 #'  A numeric array with the same dimension as paramter 'datay' and 'datax', 
-#'  the filtered datay from the regression onto datax along the 'time_dim' 
+#'  the filtered datay from the regression onto datax along the 'reg_dim' 
 #'  dimension.
 #'}
 #'
@@ -79,7 +79,7 @@
 #'@importFrom stats lm na.omit confint
 #'@import multiApply
 #'@export
-Regression <- function(datay, datax, time_dim = 'sdate', formula = y ~ x, 
+Regression <- function(datay, datax, reg_dim = 'sdate', formula = y ~ x, 
                        pval = TRUE, conf = TRUE, conf.lev = 0.95,
                        na.action = na.omit, ncores = NULL) {
 
@@ -91,8 +91,16 @@ Regression <- function(datay, datax, time_dim = 'sdate', formula = y ~ x,
   if (!is.numeric(datay) | !is.numeric(datax)) {
     stop("Parameter 'datay' and 'datax' must be a numeric array.")
   }
-  if (is.null(dim(datay)) | is.null(dim(datax))) {
-    stop("Parameter 'datay' and 'datax' must be at least one dimension 'time_dim'.")
+#  if (is.null(dim(datay)) | is.null(dim(datax))) {
+#    stop("Parameter 'datay' and 'datax' must be at least one dimension 'reg_dim'.")
+#  }
+  if (is.null(dim(datay))) {  #is vector
+    dim(datay) <- c(length(datay))
+    names(dim(datay)) <- reg_dim
+  }
+  if (is.null(dim(datax))) {  #is vector
+    dim(datax) <- c(length(datax))
+    names(dim(datax)) <- reg_dim
   }
   if(any(is.null(names(dim(datay))))| any(nchar(names(dim(datay))) == 0) |
      any(is.null(names(dim(datax))))| any(nchar(names(dim(datax))) == 0)) {
@@ -107,12 +115,12 @@ Regression <- function(datay, datax, time_dim = 'sdate', formula = y ~ x,
   if(!all(dim(datay)[name_datay] == dim(datax)[name_datax])) {
     stop("Parameter 'datay' and 'datax' must have same length of all dimensions.")
   }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) > 1) {
-    stop("Parameter 'time_dim' must be a character string.")
+  ## reg_dim
+  if (!is.character(reg_dim) | length(reg_dim) > 1) {
+    stop("Parameter 'reg_dim' must be a character string.")
   }
-  if (!time_dim %in% names(dim(datay)) | !time_dim %in% names(dim(datax))) {
-    stop("Parameter 'time_dim' is not found in 'datay' or 'datax' dimension.")
+  if (!reg_dim %in% names(dim(datay)) | !reg_dim %in% names(dim(datax))) {
+    stop("Parameter 'reg_dim' is not found in 'datay' or 'datax' dimension.")
   }
   ## formula
   if (class(formula) != 'formula') {
@@ -163,19 +171,19 @@ Regression <- function(datay, datax, time_dim = 'sdate', formula = y ~ x,
   # Calculate Regression
     if (conf & pval) {
     output_dims <- list(regression = 'stats', conf.lower = 'stats',
-                        conf.upper = 'stats', p.val = NULL, filtered = time_dim)
+                        conf.upper = 'stats', p.val = NULL, filtered = reg_dim)
   } else if (conf & !pval) {
     output_dims <- list(regression = 'stats', conf.lower = 'stats',
-                        conf.upper = 'stats', filtered = time_dim)
+                        conf.upper = 'stats', filtered = reg_dim)
   } else if (!conf & pval) {
     output_dims <- list(regression = 'stats',
-                        p.val = NULL, filtered = time_dim)
+                        p.val = NULL, filtered = reg_dim)
   } else if (!conf & !pval) {
-    output_dims <- list(regression = 'stats', filtered = time_dim)
+    output_dims <- list(regression = 'stats', filtered = reg_dim)
   }
   
   res <- Apply(list(datay, datax), 
-               target_dims = time_dim, 
+               target_dims = reg_dim, 
                output_dims = output_dims,
                fun = .Regression, 
                formula = formula, pval = pval, conf = conf,
diff --git a/R/Trend.R b/R/Trend.R
index 0f20efc0cb07ffd5dec036b77835b19c2c81a7d3..2914fb2e980468d1a0b00a2cd126bacf40447f3b 100644
--- a/R/Trend.R
+++ b/R/Trend.R
@@ -10,7 +10,7 @@
 #'@param data An numeric array including the dimension along which the trend 
 #'  is computed.
 #'@param time_dim A character string indicating the dimension along which to 
-#'  compute the trend. The default value is 'sdate'.
+#'  compute the trend. The default value is 'ftime'.
 #'@param interval A positive numeric indicating the unit length between two 
 #' points along 'time_dim' dimension. The default value is 1.
 #'@param polydeg A positive integer indicating the degree of polynomial 
@@ -66,7 +66,7 @@
 #'@rdname Trend
 #'@import multiApply
 #'@export
-Trend <- function(data, time_dim = 'sdate', interval = 1, polydeg = 1,
+Trend <- function(data, time_dim = 'ftime', interval = 1, polydeg = 1,
                   conf = TRUE, conf.lev = 0.95, pval = TRUE, ncores = NULL) {
 
   # Check inputs 
@@ -149,7 +149,7 @@ Trend <- function(data, time_dim = 'sdate', interval = 1, polydeg = 1,
   return(output)
 }
 
-.Trend <- function(x, time_dim = 'sdate', interval = 1, polydeg = 1,
+.Trend <- function(x, time_dim = 'ftime', interval = 1, polydeg = 1,
                    conf = TRUE, conf.lev = 0.95, pval = TRUE) {
 
   mon <- seq(x) * interval
diff --git a/man/Regression.Rd b/man/Regression.Rd
index cbb28759448945db58750f6d3044745e14cb8cde..4faafc1d83c57ab08f6d6fbf523e18593d89a131 100644
--- a/man/Regression.Rd
+++ b/man/Regression.Rd
@@ -4,7 +4,7 @@
 \alias{Regression}
 \title{Compute the regression of an array on another along one dimension.}
 \usage{
-Regression(datay, datax, time_dim = "sdate", formula = y ~ x, pval = TRUE,
+Regression(datay, datax, reg_dim = "sdate", formula = y ~ x, pval = TRUE,
   conf = TRUE, conf.lev = 0.95, na.action = na.omit, ncores = NULL)
 }
 \arguments{
@@ -14,8 +14,8 @@ which the regression is computed.}
 \item{datax}{An numeric array as predictor. The dimension should be identical
 as parameter 'datay'.}
 
-\item{time_dim}{A character string indicating the dimension along which to 
-compute the regression.}
+\item{reg_dim}{A character string indicating the dimension along which to 
+compute the regression. The default value is 'sdate'.}
 
 \item{formula}{An object of class "formula" (see function \code{link[stats]{lm}}).}
 
@@ -41,14 +41,14 @@ computation. Default value is NULL.}
 A list containing:
 \item{$regression}{
  A numeric array with same dimensions as parameter 'datay' and 'datax' except 
- the 'time_dim' dimension, which is replaced by a 'stats' dimension containing 
+ the 'reg_dim' dimension, which is replaced by a 'stats' dimension containing 
  the regression coefficients from the lowest order (i.e., intercept) to 
  the highest degree. The length of the 'stats' dimension should be 
  \code{polydeg + 1}.  
 }
 \item{$conf.lower}{
  A numeric array with same dimensions as parameter 'daty' and 'datax' except
- the 'time_dim' dimension, which is replaced by a 'stats' dimension containing 
+ the 'reg_dim' dimension, which is replaced by a 'stats' dimension containing 
  the lower value of the \code{siglev}\% confidence interval for all
  the regression coefficients with the same order as $regression. The length 
  of 'stats' dimension should be \code{polydeg + 1}. Only present if 
@@ -56,7 +56,7 @@ A list containing:
 }
 \item{$conf.upper}{
  A numeric array with same dimensions as parameter 'daty' and 'datax' except
- the 'time_dim' dimension, which is replaced by a 'stats' dimension containing 
+ the 'reg_dim' dimension, which is replaced by a 'stats' dimension containing 
  the upper value of the \code{siglev}\% confidence interval for all
  the regression coefficients with the same order as $regression. The length 
  of 'stats' dimension should be \code{polydeg + 1}. Only present if 
@@ -64,17 +64,17 @@ A list containing:
 }
 \item{$p.val}{
  A numeric array with same dimensions as parameter 'daty' and 'datax' except
- the 'time_dim' dimension, The array contains the p-value.
+ the 'reg_dim' dimension, The array contains the p-value.
 }
 \item{$filtered}{
  A numeric array with the same dimension as paramter 'datay' and 'datax', 
- the filtered datay from the regression onto datax along the 'time_dim' 
+ the filtered datay from the regression onto datax along the 'reg_dim' 
  dimension.
 }
 }
 \description{
 Compute the regression of the array 'datay' on the array 'datax' along the
-'time_dim' dimension by least square fitting (default) or self-defined model. 
+'reg_dim' dimension by least square fitting (default) or self-defined model. 
 The function provides the slope of the regression, the intercept, and the
 associated p-value and confidence interval. The filtered datay from the 
 regression onto datax is also provided.\cr
diff --git a/man/Trend.Rd b/man/Trend.Rd
index 17076c6e690e8063bb85b0bbf17550b24c9342f4..a641041cd451628b948ccb0545970322e0cf1f81 100644
--- a/man/Trend.Rd
+++ b/man/Trend.Rd
@@ -4,7 +4,7 @@
 \alias{Trend}
 \title{Compute the trend}
 \usage{
-Trend(data, time_dim = "sdate", interval = 1, polydeg = 1, conf = TRUE,
+Trend(data, time_dim = "ftime", interval = 1, polydeg = 1, conf = TRUE,
   conf.lev = 0.95, pval = TRUE, ncores = NULL)
 }
 \arguments{
@@ -12,7 +12,7 @@ Trend(data, time_dim = "sdate", interval = 1, polydeg = 1, conf = TRUE,
 is computed.}
 
 \item{time_dim}{A character string indicating the dimension along which to 
-compute the trend. The default value is 'sdate'.}
+compute the trend. The default value is 'ftime'.}
 
 \item{interval}{A positive numeric indicating the unit length between two 
 points along 'time_dim' dimension. The default value is 1.}
diff --git a/tests/testthat/test-Regression.R b/tests/testthat/test-Regression.R
index 69c4f9edd5eafa7561c565f9df80f3ce50079dc5..85e57822d74f1c861dea5c0952268501ebe8e8f9 100644
--- a/tests/testthat/test-Regression.R
+++ b/tests/testthat/test-Regression.R
@@ -39,10 +39,6 @@ test_that("1. Input checks", {
   "Parameter 'datay' and 'datax' must be a numeric array."
   )
   expect_error(
-  Regression(c(1:10), c(2:4)),
-  "Parameter 'datay' and 'datax' must be at least one dimension 'time_dim'."
-  )
-  expect_error(
   Regression(array(1:10, dim = c(2, 5)), array(1:4, dim = c(2, 2))),
   "Parameter 'datay' and 'datax' must have dimension names."
   )
@@ -55,12 +51,12 @@ test_that("1. Input checks", {
   "Parameter 'datay' and 'datax' must have same length of all dimensions."
   )
   expect_error(
-  Regression(datay1, datax1, time_dim = 1),
-  "Parameter 'time_dim' must be a character string."
+  Regression(datay1, datax1, reg_dim = 1),
+  "Parameter 'reg_dim' must be a character string."
   )
   expect_error(
-  Regression(datay1, datax1, time_dim = 'asd'),
-  "Parameter 'time_dim' is not found in 'datay' or 'datax' dimension."
+  Regression(datay1, datax1, reg_dim = 'asd'),
+  "Parameter 'reg_dim' is not found in 'datay' or 'datax' dimension."
   )
   expect_error(
   Regression(datay1, datax1, na.action = TRUE),
@@ -180,15 +176,15 @@ test_that("3. Output checks: dat2", {
 test_that("4. Output checks: dat3", {
 
   expect_equal(
-    dim(Regression(datay3, datax3, time_dim = 'date')$regression),
+    dim(Regression(datay3, datax3, reg_dim = 'date')$regression),
     c(stats = 2, ftime = 2, lon = 2, lat = 4)
   )
   expect_equal(
-    dim(Regression(datay3, datax3, time_dim = 'date')$conf.lower),
+    dim(Regression(datay3, datax3, reg_dim = 'date')$conf.lower),
     c(stats = 2, ftime = 2, lon = 2, lat = 4)
   )
   expect_equal(
-    dim(Regression(datay3, datax3, time_dim = 'date')$p.val),
+    dim(Regression(datay3, datax3, reg_dim = 'date')$p.val),
     c(ftime = 2, lon = 2, lat = 4)
   )
 
diff --git a/tests/testthat/test-Trend.R b/tests/testthat/test-Trend.R
index 89052a0b119d09c02682f8a0b822caa3f3e722f8..79049b526b5c0b77c86239b9d64da96503aa2209 100644
--- a/tests/testthat/test-Trend.R
+++ b/tests/testthat/test-Trend.R
@@ -3,7 +3,7 @@ context("s2dv::Trend tests")
 ##############################################
   # dat1
   dat1 <- array(c(-5, -7, -10:10, 12, 11, 7, 16), 
-                dim = c(dat = 1, sdate = 13, ftime = 2))
+                dim = c(dat = 1, ftime = 13, sdate = 2))
 
   # dat2
   set.seed(10)
@@ -12,7 +12,7 @@ context("s2dv::Trend tests")
   # dat3
   set.seed(1)
   dat3 <- array(c(1:60) + rnorm(60),
-                dim = c(sdate = 5, lat = 2, lon = 3, lev = 2))
+                dim = c(ftime = 5, lat = 2, lon = 3, lev = 2))
   set.seed(1)
   na <- floor(runif(5, min = 1, max = 60))
   dat3[na] <- NA
@@ -42,7 +42,7 @@ test_that("1. Input checks", {
     "Parameter 'time_dim' is not found in 'data' dimension."
   )
   expect_error(
-    Trend(array(c(1:25), dim = c(dat = 1, date = 5, ftime = 5))),
+    Trend(array(c(1:25), dim = c(dat = 1, date = 5, sdate = 5))),
     "Parameter 'time_dim' is not found in 'data' dimension."
   )
   expect_error(
@@ -89,19 +89,19 @@ test_that("2. Output checks: dat1", {
   expect_equal(
     Trend(dat1)$trend,
     array(c(-9.7692308, 0.6593407, 0.9615385, 0.7967033), 
-          dim = c(stats = 2, dat = 1, ftime = 2)),
+          dim = c(stats = 2, dat = 1, sdate = 2)),
     tolerance = 0.0001
   )
   expect_equal(
     Trend(dat1)$conf.upper,
     array(c(-7.4735367, 0.9485709, 3.0167860, 1.0556402), 
-          dim = c(stats = 2, dat = 1, ftime = 2)),
+          dim = c(stats = 2, dat = 1, sdate = 2)),
     tolerance = 0.0001
   )
   expect_equal(
     Trend(dat1)$p.val,
     array(c(0.0003916111, 3.065828e-05), 
-          dim = c(stats = 1, dat = 1, ftime = 2)),
+          dim = c(stats = 1, dat = 1, sdate = 2)),
     tolerance = 0.0001
   )
   expect_equal(
@@ -122,7 +122,7 @@ test_that("3. Output checks: dat2", {
          conf.upper = array(c(0.953, 1.127), dim = c(stats = 2)),
          p.val = array(2.277774e-06, dim = c(stats = 1)),
          detrended = array(c(0.257, 0.110, -1.021, -0.193, 0.757, 0.909, 
-                             -0.633, 0.267, -0.939, 0.487), dim = c(sdate = 10))),
+                             -0.633, 0.267, -0.939, 0.487), dim = c(ftime = 10))),
     tolerance = 0.001
   )
   expect_equal(
@@ -132,7 +132,7 @@ test_that("3. Output checks: dat2", {
          conf.upper = array(c(0.953, 0.563), dim = c(stats = 2)),
          p.val = array(2.277774e-06, dim = c(stats = 1)),
          detrended = array(c(0.257, 0.110, -1.021, -0.193, 0.757, 0.909,
-                             -0.633, 0.267, -0.939, 0.487), dim = c(sdate = 10))),
+                             -0.633, 0.267, -0.939, 0.487), dim = c(ftime = 10))),
     tolerance = 0.001
   )
   expect_equal(