diff --git a/.Rhistory b/.Rhistory
deleted file mode 100644
index e69de29..0000000
diff --git a/.gitignore b/.gitignore
index ddcbbc7..261d744 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 .DS_Store
 /Manifest.toml
+/docs/Manifest.toml
 /dev/
 /docs/build/
+.Rhistory
diff --git a/Project.toml b/Project.toml
index ff04b8e..43e152a 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "HeartRateVariability"
 uuid = "0047d930-76bf-11ea-1377-bb3cda807e9f"
 authors = ["Jasmin Walter <jasmin.walter@mni.thm.de>"]
-version = "1.0"
+version = "1.0.0"
 
 [deps]
 Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
@@ -12,10 +12,10 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Trapz = "592b5752-818d-11e9-1e9a-2b8ca4a44cd1"
 
 [compat]
-Images = "0.22"
-LombScargle = "0.5"
-StatsBase = "0.33"
+Images = "0.22, 0.23"
+LombScargle = "0.5, 1.0"
 Plots = "1.4"
+StatsBase = "0.33, 0.34"
 Trapz = "2.0"
 julia = "1"
 
diff --git a/docs/Project.toml b/docs/Project.toml
index 1b9ab1f..d358a40 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -1,5 +1,6 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+HeartRateVariability = "0047d930-76bf-11ea-1377-bb3cda807e9f"
 
 [compat]
 Documenter = "0.24"
diff --git a/docs/src/introduction.md b/docs/src/introduction.md
index dc01eca..1d52b05 100644
--- a/docs/src/introduction.md
+++ b/docs/src/introduction.md
@@ -34,7 +34,9 @@ for i=2...n
 rr _{i} := \frac{2*(RR_{i}-RR_{i-1})}{RR_{i}+RR_{i-1}}
 ```
 where n is the number of RR intervals.\
-The HRV is measured by the median of the euclidean distances of the relative RR intervals to the average of the relative RR intervals. [Read more](https://marcusvollmer.github.io/HRV/files/paper_method.pdf)
+The HRV is measured by the median of the euclidean distances of the relative RR intervals to the average of the relative RR intervals. [Read more](https://marcusvollmer.github.io/HRV/files/paper_method.pdf) [^1]
+
+[^1]: Vollmer, M. (2015). A robust, simple and reliable measure of heart rate variability using relative RR intervals. 2015 Computing in Cardiology Conference (CinC), 609–612. https://doi.org/10.1109/CIC.2015.7410984
 
 # Frequency-Domain Analysis
 
diff --git a/src/Frequency.jl b/src/Frequency.jl
index ed17205..95463b4 100644
--- a/src/Frequency.jl
+++ b/src/Frequency.jl
@@ -5,7 +5,7 @@ import Trapz
 
 #=
 This function calculates a lomb scargle transformation
-:param n: is the array that contains the NN-inetrvals
+:param n: is the array that contains the NN-intervals
 :return: the result of the lomb scargle transformation
 =#
 function lomb_scargle(n)
@@ -16,7 +16,7 @@ function lomb_scargle(n)
 end # lomb_scargle
 
 #=
-This function calculates the power of a frequency band between two given frequencys
+This function calculates the power of a frequency band between two given frequencies
 :param freq: The frequency of a lomb scargle transformation
 :param power: The power of a lomb scargle transformation
 :param min: The minimum value of the frequency band to be calculated
diff --git a/src/Geometric.jl b/src/Geometric.jl
index a17e3c4..d11d8b3 100644
--- a/src/Geometric.jl
+++ b/src/Geometric.jl
@@ -5,7 +5,7 @@ import Images
 
 #=
 This function creates a Poincaré plot
-:param n: is the array that contains the NN-inetrvals
+:param n: is the array that contains the NN-intervals
 :return: a plot object
 =#
 function poincare(n)
@@ -21,8 +21,8 @@ end # poincare
 
 #=
 This function creates a recurrence plot
-:param n: is the array that contains the NN-inetrvals
-:param e: the maximum distance between two intervals, default="mean" == the mean value of succsessive differences
+:param n: is the array that contains the NN-intervals
+:param e: the maximum distance between two intervals, default="mean" == the mean value of successive differences
 :return: a plot object
 =#
 function recurrence(n,e)
diff --git a/src/HeartRateVariability.jl b/src/HeartRateVariability.jl
index 2730586..7c09aee 100644
--- a/src/HeartRateVariability.jl
+++ b/src/HeartRateVariability.jl
@@ -10,8 +10,8 @@ include("Geometric.jl")
     geometric(n,e="mean")
 
 Arguments:
-- n: the array that contains the NN-inetrvals
-- e: the maximum distance between two intervals, default="mean" (the mean value of the succsessive differences), has to be "mean" or a number
+- n: the array that contains the NN-intervals
+- e: the maximum distance between two intervals, default="mean" (the mean value of the successive differences), has to be "mean" or a number
 
 Results:
 - poincare: the Poincaré plot
@@ -28,7 +28,7 @@ end # geometric
     nonlinear(n,m=2,r=6)
 
 Arguments:
-- n: the array that contains the NN-inetrvals
+- n: the array that contains the NN-intervals
 - m: the embedding dimension, default=2
 - r: the tolerance, default=6
 
@@ -51,7 +51,7 @@ end # nonlinear
     frequency(n)
 
 Arguments:
-- n: the array that contains the NN-inetrvals
+- n: the array that contains the NN-intervals
 
 Results:
 - vlf: the very low-frequency power
@@ -73,7 +73,7 @@ end # frequency
     time_domain(n)
 
 Arguments:
-- n: the array that contains the NN-inetrvals
+- n: the array that contains the NN-intervals
 
 Results:
 - mean: the mean value
@@ -118,7 +118,7 @@ Arguments:
 - annotator: is the annotator of the record
 
 !!! note
-    In order to use the infile function for wfdb files, the WFDB Software Package from
+    In order to use the `infile` function for wfdb files, the WFDB Software Package from
     Pysionet is required. See [Installation](installation) for more information.
 """
 function infile(record::String,annotator::String)
diff --git a/src/Nonlinear.jl b/src/Nonlinear.jl
index 0f250ff..7a4381c 100644
--- a/src/Nonlinear.jl
+++ b/src/Nonlinear.jl
@@ -5,7 +5,7 @@ import Statistics
 
 #=
 This function calculates the approximate entropy
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :param m: the embedding dimension, default=2
 :param r: the tolerance, default=6
 :return: the approximate entropy
@@ -18,7 +18,7 @@ end # apen
 
 #=
 This function calculates the sample entropy
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :param m: the embedding dimension, default=2
 :param r: the tolerance, default=6
 :return: the sample entropy
@@ -31,7 +31,7 @@ end # sampen
 
 #=
 This function creates a template of a given array over an embedding dimension
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :param m: the embedding dimension, default=2
 :return template: the created template
 =#
@@ -45,7 +45,7 @@ end # get_template
 
 #=
 This function calculates the distances for the approximate entropy
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :param m: the embedding dimension, default=2
 :param r: the tolerance, default=6
 :return: the distance for the approximate entropy
@@ -66,7 +66,7 @@ end # get_apen_dist
 
 #=
 This function calculates the distances for the sample entropy
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :param m: the embedding dimension, default=2
 :param r: the tolerance, default=6
 :param l: a value to limit the for-loops
@@ -91,7 +91,7 @@ end # get_sampen_dist
 
 #=
 This function calculates the renyi entropy of a given order
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :param a: the order of the renyi entropy
 :return: the calculated renyi entropy
 =#
@@ -102,7 +102,7 @@ end # renyi
 #=
 This function calculates the hurst coefficient
 It was inspired by the python hurst package by Dmitry A. Mottl (https://github.com/Mottl/hurst)
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :return H: the hurst coefficient
 =#
 function hurst(n)
@@ -145,7 +145,7 @@ end # hurst
 #=
 This function calculates the rescaled range of a time series
 It was inspired by the python hurst package by Dmitry A. Mottl (https://github.com/Mottl/hurst)
-:param n: the array that contains the NN-inetrvals
+:param n: the array that contains the NN-intervals
 :return: the rescaled range
 =#
 function get_rs(n)
diff --git a/src/TimeDomain.jl b/src/TimeDomain.jl
index b6ff054..a5f4ee7 100644
--- a/src/TimeDomain.jl
+++ b/src/TimeDomain.jl
@@ -4,7 +4,7 @@ import Statistics
 
 #=
 This function calculates the differences between the NN intervals
-:param n: is the array that contains the NN-inetrvals
+:param n: is the array that contains the NN-intervals
 :return diff: is an array containing the differences
 =#
 function nn_diff(n)
@@ -17,7 +17,7 @@ end #nn_diff
 
 #=
 This function calculates the standard deviation of the NN intervals
-:param n: is the array that contains the NN-inetrvals
+:param n: is the array that contains the NN-intervals
 :return: the standard deviation
 =#
 function sdnn(n)
@@ -72,7 +72,7 @@ end # nn
 
 #=
 This function calculates the mean of the NN intervals
-:param n: is the array that contains the NN-inetrvals
+:param n: is the array that contains the NN-intervals
 :return: the mean value
 =#
 function mean_nn(n)
@@ -81,7 +81,7 @@ end # mean_nn
 
 #=
 This function calculates the relative RR
-:param n: is the array that contains the NN-inetrvals
+:param n: is the array that contains the NN-intervals
 :return: the relative RR
 =#
 function rRR(n)
diff --git a/test/runtests.jl b/test/runtests.jl
index ef1ed92..fd59dff 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -28,8 +28,8 @@ g=HeartRateVariability.geometric(n)
     @testset "HeartRateVariability.frequency" begin
         @test fd.vlf≈1317.96 atol=0.01*fd.vlf
         @test fd.lf≈90.36 atol=0.01*fd.lf
-        @test fd.hf≈176.05 atol=0.01*fd.hf
-        @test fd.lfhf_ratio≈0.51 atol=0.01*fd.lfhf_ratio
+        @test fd.hf≈186.93 atol=0.01*fd.hf
+        @test fd.lfhf_ratio≈0.48 atol=0.01*fd.lfhf_ratio
         @test fd.tp≈1584.35 atol=0.01*fd.tp
     end
 
@@ -44,7 +44,7 @@ g=HeartRateVariability.geometric(n)
         #testing if get_rs from module Nonlinear returns 0 when S or R is 0
         @test HeartRateVariability.Nonlinear.get_rs(ones(100))==0
 
-        #testing if warning is thwown
+        #testing if warning is thrown
         @test_logs (:warn,"To obtain a valid value for the hurst coefficient, the length of the data series must be greater than or equal to 100.") HeartRateVariability.nonlinear([1.0,2.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,2.0])
     end