diff --git a/DESCRIPTION b/DESCRIPTION
index a533bf8..83752fe 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: leiden
 Type: Package
 Title: R implementation of the Leiden package
-Version: 0.2.0
+Version: 0.2.1
 Date: 2018
 Author: Tom Kelly <tom.kelly@riken.jp>
 Maintainer: Tom Kelly <tom.kelly@riken.jp>
diff --git a/NAMESPACE b/NAMESPACE
index 1156c91..ea8c261 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,4 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
 export(leiden)
-import(reticulate)
+importFrom(reticulate,import)
+importFrom(reticulate,r_to_py)
diff --git a/R/leiden.R b/R/leiden.R
index f048795..fde354b 100644
--- a/R/leiden.R
+++ b/R/leiden.R
@@ -7,23 +7,37 @@
 ##'
 ##' @param adj_mat An adjacency matrix compatible with \code{\link[igraph]{igraph}} object.
 ##' @param partition_type Type of partition to use. Defaults to RBConfigurationVertexPartition. Options include: ModularityVertexPartition, RBERVertexPartition, CPMVertexPartition, MutableVertexPartition, SignificanceVertexPartition, SurpriseVertexPartition (see the Leiden python module documentation for more details)
+##' @param initial_membership,weights,node_sizes Parameters to pass to the Python leidenalg function (defaults initial_membership=None, weights=None).
 ##' @param resolution_parameter A parameter controlling the coarseness of the clusters. Higher values lead to more clusters. (defaults to 1.0 for partition types that accept a resolution parameter)
-##' @param ... Parameters to pass to the Python leidenalg function (defaults initial_membership=None, weights=None).
 ##'
 ##' @keywords graph network igraph mvtnorm simulation
-##' @import reticulate
+##' @importFrom reticulate import r_to_py
 ##' @export
 
-leiden <- function(adj_mat, partition_type = c('RBConfigurationVertexPartition', 'ModularityVertexPartition', 'RBERVertexPartition', 'CPMVertexPartition', 'MutableVertexPartition', 'SignificanceVertexPartition', 'SurpriseVertexPartition'), resolution_parameter = 1, ...){
+leiden <- function(adj_mat,
+     partition_type = c(
+    'RBConfigurationVertexPartition',
+    'ModularityVertexPartition',
+    'RBERVertexPartition',
+    'CPMVertexPartition',
+    'MutableVertexPartition',
+    'SignificanceVertexPartition',
+    'SurpriseVertexPartition'
+),
+initial_membership = NULL,
+weights = NULL,
+node_sizes = NULL,
+resolution_parameter = 1
+) {
     #import python modules with reticulate
-    leidenalg <- reticulate::import("leidenalg", delay_load = TRUE)
-    ig <- reticulate::import("igraph", delay_load = TRUE)
+    leidenalg <- import("leidenalg", delay_load = TRUE)
+    ig <- import("igraph", delay_load = TRUE)
 
     #convert matrix input
     adj_mat <- as.matrix(ceiling(adj_mat))
 
     ##convert to python numpy.ndarray, then a list
-    adj_mat_py <- reticulate::r_to_py(adj_mat)
+    adj_mat_py <- r_to_py(adj_mat)
     adj_mat_py <- adj_mat_py$tolist()
 
     #convert graph structure to a Python compatible object
@@ -31,27 +45,52 @@ leiden <- function(adj_mat, partition_type = c('RBConfigurationVertexPartition',
 
     #compute partitions
     partition_type <- partition_type[1]
-    if(partition_type == "RBConfigurationVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$RBConfigurationVertexPartition, resolution_parameter = resolution_parameter, ...)
-    } else if(partition_type == "ModularityVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$ModularityVertexPartition, ...)
-    } else if(partition_type == "RBERVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$RBERVertexPartition, resolution_parameter = resolution_parameter, ...)
-    } else if(partition_type == "CPMVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$CPMVertexPartition, resolution_parameter = resolution_parameter, ...)
-    } else if(partition_type == "MutableVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$MutableVertexPartition, ...)
-    } else if(partition_type == "SignificanceVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$SignificanceVertexPartition, resolution_parameter = resolution_parameter, ...)
-    } else if(partition_type == "SurpriseVertexPartition"){
-        part <- leidenalg$find_partition(snn_graph, leidenalg$SurpriseVertexPartition, resolution_parameter = resolution_parameter, ...)
-    } else {
+    part <- switch(
+        EXPR = partition_type,
+        'RBConfigurationVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$RBConfigurationVertexPartition,
+            initial_membership = initial_membership, weights = weights,
+            resolution_parameter = resolution_parameter
+        ),
+        'ModularityVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$ModularityVertexPartition,
+            initial_membership = initial_membership, weights = weights
+        ),
+        'RBERVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$RBERVertexPartition,
+            initial_membership = initial_membership, weights = weights, node_sizes = node_sizes,
+            resolution_parameter = resolution_parameter
+        ),
+        'CPMVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$CPMVertexPartition,
+            initial_membership = initial_membership, weights = weights, node_sizes = node_sizes,
+            resolution_parameter = resolution_parameter
+        ),
+        'MutableVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$MutableVertexPartition,
+            initial_membership = initial_membership
+        ),
+        'SignificanceVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$SignificanceVertexPartition,
+            initial_membership = initial_membership, node_sizes = node_sizes,
+            resolution_parameter = resolution_parameter
+        ),
+        'SurpriseVertexPartition' = leidenalg$find_partition(
+            snn_graph,
+            leidenalg$SurpriseVertexPartition,
+            initial_membership = initial_membership, weights = weights, node_sizes = node_sizes
+        ),
         stop("please specify a partition type as a string out of those documented")
-    }
+    )
     return(part$membership+1)
 }
 
-
 # global reference to python modules (will be initialized in .onLoad)
 leidenalg <- NULL
 ig <- NULL
diff --git a/README.md b/README.md
index d258b52..a1c9344 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Leiden Algorithm
 
-## leiden version 0.2.0
+## leiden version 0.2.1
 
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/leiden)](https://cran.r-project.org/package=leiden)
 [![Travis Build Status](https://travis-ci.org/TomKellyGenetics/leiden.svg?branch=master)](https://travis-ci.org/TomKellyGenetics/leiden)
@@ -84,7 +84,7 @@ Please cite this implementation R in if you use it:
 To cite the leiden package in publications use:
 
   S. Thomas Kelly (2018). leiden: R implementation of the Leiden algorithm. R
-  package version 0.1.0 https://github.com/TomKellyGenetics/leiden
+  package version 0.2.1 https://github.com/TomKellyGenetics/leiden
 
 A BibTeX entry for LaTeX users is
 
@@ -92,7 +92,7 @@ A BibTeX entry for LaTeX users is
     title = {leiden: R implementation of the Leiden algorithm},
     author = {S. Thomas Kelly},
     year = {2018},
-    note = {R package version 0.2.0},
+    note = {R package version 0.2.1},
     url = {https://github.com/TomKellyGenetics/leiden},
   }
  ```
diff --git a/man/leiden.Rd b/man/leiden.Rd
index a2403ea..554c729 100644
--- a/man/leiden.Rd
+++ b/man/leiden.Rd
@@ -8,16 +8,17 @@
 leiden(adj_mat, partition_type = c("RBConfigurationVertexPartition",
   "ModularityVertexPartition", "RBERVertexPartition", "CPMVertexPartition",
   "MutableVertexPartition", "SignificanceVertexPartition",
-  "SurpriseVertexPartition"), resolution_parameter = 1, ...)
+  "SurpriseVertexPartition"), initial_membership = NULL,
+  weights = NULL, node_sizes = NULL, resolution_parameter = 1)
 }
 \arguments{
 \item{adj_mat}{An adjacency matrix compatible with \code{\link[igraph]{igraph}} object.}
 
 \item{partition_type}{Type of partition to use. Defaults to RBConfigurationVertexPartition. Options include: ModularityVertexPartition, RBERVertexPartition, CPMVertexPartition, MutableVertexPartition, SignificanceVertexPartition, SurpriseVertexPartition (see the Leiden python module documentation for more details)}
 
-\item{resolution_parameter}{A parameter controlling the coarseness of the clusters. Higher values lead to more clusters. (defaults to 1.0 for partition types that accept a resolution parameter)}
+\item{initial_membership, weights, node_sizes}{Parameters to pass to the Python leidenalg function (defaults initial_membership=None, weights=None).}
 
-\item{...}{Parameters to pass to the Python leidenalg function (defaults initial_membership=None, weights=None).}
+\item{resolution_parameter}{A parameter controlling the coarseness of the clusters. Higher values lead to more clusters. (defaults to 1.0 for partition types that accept a resolution parameter)}
 }
 \description{
 Implements the Leiden clustering algorithm in R using reticulate to run the Python version. Requires the python "leidenalg" and "igraph" modules to be installed. Returns a vector of partition indices.