diff --git a/DESCRIPTION b/DESCRIPTION
index 3bddab3b..0e413527 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -21,7 +21,7 @@ Depends:
 Imports:
     dplyr (>= 0.5.0),
     tidyr,
-    stringi
+    snakecase
 License: MIT + file LICENSE
 LazyData: true
 RoxygenNote: 6.0.1
diff --git a/R/clean_names.R b/R/clean_names.R
index 8b7182dd..cc5f933f 100644
--- a/R/clean_names.R
+++ b/R/clean_names.R
@@ -1,12 +1,31 @@
 #' @title Cleans names of a data.frame.
 #'
 #' @description
-#' Resulting names are unique and consist only of the \code{_} character, lowercase letters, and numbers.
+#' Resulting names are unique and consist only of the \code{_} character, numbers and letters regarding the specified \code{case}.
 #' 
 #' Accented characters are
 #' transliterated to ASCII.  For example, an "o" with a german umlaut over it becomes "o", and the Spanish character "enye" becomes "n". 
 #'
 #' @param dat the input data.frame.
+#' @param case The desired target case (default is \code{"snake"}), provided as one of the following:
+#' \itemize{
+#'  \item{snake_case: \code{"snake"}}
+#'  \item{lowerCamel: \code{"lower_camel"} or \code{"small_camel"}}
+#'  \item{UpperCamel: \code{"upper_camel"} or \code{"big_camel"}}
+#'  \item{ALL_CAPS: \code{"screaming_snake"} or \code{"all_caps"}}
+#'  \item{lowerUPPER: \code{"lower_upper"}}
+#'  \item{UPPERlower: \code{"upper_lower"}}
+#'  }
+#'
+#'  There are three "special" cases available:
+#' \itemize{
+#'  \item{\code{"parsed"}: Every substring a string consists of, surrounded by an underscore. No lower or 
+#'  upper case pattern from the input string are changed.}
+#'  \item{\code{"mixed"}: Almost the same as \code{case = "parsed"}. Every letter which is not at the start
+#'  or behind an underscore is turned into lowercase.}
+#'  \item{\code{"none"}: This case is just available as an artifact from the underlying snakecase-pkg, but shouldn't be called within the context of \code{clean_names()}.}
+#'  }
+#'  
 #' @return Returns the data.frame with clean names.
 #' @export
 #' @examples
@@ -20,25 +39,27 @@
 #' # library(readxl)
 #' # readxl("messy_excel_file.xlsx") %>% clean_names()
 
-clean_names <- function(dat){
+clean_names <- function(dat, case = "snake"){
 
   # Takes a data.frame, returns the same data frame with cleaned names
   old_names <- names(dat)
   new_names <- old_names %>%
     gsub("'", "", .) %>% # remove quotation marks
     gsub("\"", "", .) %>% # remove quotation marks
-    gsub("%", "percent", .) %>%
+    gsub("%", ".percent_", .) %>% # starting with "." as a workaround, to make
+    # ".percent" a valid name. The "." will be replaced in the call to to_any_case
+    # via the preprocess argument anyway.
     gsub("^[ ]+", "", .) %>%
     make.names(.) %>%
-    gsub("[.]+", "_", .) %>% # convert 1+ periods to single _
-    gsub("[_]+", "_", .) %>% # fix rare cases of multiple consecutive underscores
-    tolower(.) %>%
-    gsub("_$", "", .) %>% # remove string-final underscores
-    stringi::stri_trans_general("latin-ascii") 
-
+    # Handle dots, multiple underscores, case conversion, string transliteration
+    snakecase::to_any_case(case = case, preprocess = "\\.", 
+                replace_special_characters = c("Latin-ASCII"))
+  
   # Handle duplicated names - they mess up dplyr pipelines
   # This appends the column number to repeated instances of duplicate variable names
-  dupe_count <- sapply(1:length(new_names), function(i) { sum(new_names[i] == new_names[1:i]) })
+  dupe_count <- vapply(1:length(new_names), function(i) { 
+    sum(new_names[i] == new_names[1:i]) }, integer(1))
+  
   new_names[dupe_count > 1] <- paste(new_names[dupe_count > 1],
                                      dupe_count[dupe_count > 1],
                                      sep = "_")
diff --git a/man/clean_names.Rd b/man/clean_names.Rd
index 0a559f5d..ceb9706a 100644
--- a/man/clean_names.Rd
+++ b/man/clean_names.Rd
@@ -4,16 +4,35 @@
 \alias{clean_names}
 \title{Cleans names of a data.frame.}
 \usage{
-clean_names(dat)
+clean_names(dat, case = "snake")
 }
 \arguments{
 \item{dat}{the input data.frame.}
+
+\item{case}{The desired target case (default is \code{"snake"}), provided as one of the following:
+\itemize{
+ \item{snake_case: \code{"snake"}}
+ \item{lowerCamel: \code{"lower_camel"} or \code{"small_camel"}}
+ \item{UpperCamel: \code{"upper_camel"} or \code{"big_camel"}}
+ \item{ALL_CAPS: \code{"screaming_snake"} or \code{"all_caps"}}
+ \item{lowerUPPER: \code{"lower_upper"}}
+ \item{UPPERlower: \code{"upper_lower"}}
+ }
+
+ There are three "special" cases available:
+\itemize{
+ \item{\code{"parsed"}: Every substring a string consists of, surrounded by an underscore. No lower or 
+ upper case pattern from the input string are changed.}
+ \item{\code{"mixed"}: Almost the same as \code{case = "parsed"}. Every letter which is not at the start
+ or behind an underscore is turned into lowercase.}
+ \item{\code{"none"}: This case is just available as an artifact from the underlying snakecase-pkg, but shouldn't be called within the context of \code{clean_names()}.}
+ }}
 }
 \value{
 Returns the data.frame with clean names.
 }
 \description{
-Resulting names are unique and consist only of the \code{_} character, lowercase letters, and numbers.
+Resulting names are unique and consist only of the \code{_} character, numbers and letters regarding the specified \code{case}.
 
 Accented characters are
 transliterated to ASCII.  For example, an "o" with a german umlaut over it becomes "o", and the Spanish character "enye" becomes "n".