diff --git a/DESCRIPTION b/DESCRIPTION
index 9a873f1..3d9a714 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: SimDesign
 Title: Structure for Organizing Monte Carlo Simulation Designs
-Version: 2.18.3
+Version: 2.18.4
 Authors@R: c(person("Phil", "Chalmers", email = "rphilip.chalmers@gmail.com", role = c("aut", "cre"),
                     comment = c(ORCID="0000-0001-5332-2810")),
              person("Matthew", "Sigal", role = c("ctb")),
diff --git a/NEWS.md b/NEWS.md
index 8ed07ab..cae08b2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,10 @@
 # NEWS file for SimDesign
 
+## Changes in SimDesign 2.19
+
+- Multiple parameter `bias()` returned incorrect behavior/errors
+  for `relative` and `abs_relative` types (see #60)
+
 ## Changes in SimDesign 2.18
 
 - Objects built by `createDesign()` gain `[]` and `rbind()` S3 functions for 
diff --git a/R/summary_functions.R b/R/summary_functions.R
index 96ed743..738f6bf 100644
--- a/R/summary_functions.R
+++ b/R/summary_functions.R
@@ -80,6 +80,8 @@
 #' # different parameter associated with each column
 #' mat <- cbind(M1=rnorm(1000, 2, sd = 0.25), M2 = rnorm(1000, 3, sd = .25))
 #' bias(mat, parameter = c(2,3))
+#' bias(mat, parameter = c(2,3), type='relative')
+#' bias(mat, parameter = c(2,3), type='standardized')
 #'
 #' # same, but with data.frame
 #' df <- data.frame(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
@@ -128,8 +130,8 @@ bias <- function(estimate, parameter = NULL, type = 'bias', abs = FALSE,
     if(!equal_len)
         stopifnot(ncol(estimate) == length(parameter))
     diff <- t(t(estimate) - parameter)
-    ret <- if(type == 'relative') colMeans(diff / parameter)
-        else if(type == 'abs_relative') colMeans(diff / abs(parameter))
+    ret <- if(type == 'relative') rowMeans(t(diff) / parameter)
+        else if(type == 'abs_relative') rowMeans(t(diff) / abs(parameter))
         else if(type == 'standardized') colMeans(diff) / colSDs(estimate)
         else colMeans(diff)
     if(abs) ret <- abs(ret)
diff --git a/SimDesign.Rproj b/SimDesign.Rproj
index 081c716..11f8443 100644
--- a/SimDesign.Rproj
+++ b/SimDesign.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: fe769c1e-d77f-4cb0-8cc9-a5d4a1fc8243
 
 RestoreWorkspace: No
 SaveWorkspace: No
diff --git a/man/SimFunctions.Rd b/man/SimFunctions.Rd
index b663b63..a277860 100644
--- a/man/SimFunctions.Rd
+++ b/man/SimFunctions.Rd
@@ -28,10 +28,10 @@ is generally the recommended approach when beginning to write a Monte Carlo simu
 \item{save_structure}{character indicating the number of files to break the simulation code into
 when \code{filename} is included (default is 'single' for one file). When \code{save_structure = 'double'} the
 output is saved to two separate files containing the functions and design definitions,
-and when \code{save_structure = 'all'} the generate, analyse, summarise, and execution code area all saved into
-separate files. The purpose for this structure is because multiple structured files
-often makes organization and debugging slightly easier larger Monte Carlo simulations, though in principle
-all files could be stored into a single R script}
+and when \code{save_structure = 'all'} the generate, analyse, summarise, and execution code are all saved into
+separate files. The purpose of this structure is because multiple structured files
+often makes organization and debugging slightly easier for larger Monte Carlo simulations, though, in principle,
+all files could be stored in a single R script}
 
 \item{extra_file}{logical; should an extra file be saved containing user-defined functions or objects?
 Default is \code{FALSE}}
diff --git a/man/bias.Rd b/man/bias.Rd
index bf03ca0..7749ab4 100644
--- a/man/bias.Rd
+++ b/man/bias.Rd
@@ -79,6 +79,8 @@ bias(mat, parameter = 2, type = 'standardized')
 # different parameter associated with each column
 mat <- cbind(M1=rnorm(1000, 2, sd = 0.25), M2 = rnorm(1000, 3, sd = .25))
 bias(mat, parameter = c(2,3))
+bias(mat, parameter = c(2,3), type='relative')
+bias(mat, parameter = c(2,3), type='standardized')
 
 # same, but with data.frame
 df <- data.frame(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))