diff --git a/R/rucrdtw-package.r b/R/rucrdtw-package.r
index 5a1f55e..0044b28 100644
--- a/R/rucrdtw-package.r
+++ b/R/rucrdtw-package.r
@@ -1,6 +1,6 @@
#' rucrdtw: Fast time series subsequence search in R
#'
-#' @description Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the \strong{dtw} package (Giorgino 2009).
+#' @description Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the \strong{dtw} package (Giorgino 2009).
#'
#' Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The UCR Suite (Rakthanmanon et al. 2012) provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
#'
diff --git a/man/rucrdtw.Rd b/man/rucrdtw.Rd
index d242728..592d434 100644
--- a/man/rucrdtw.Rd
+++ b/man/rucrdtw.Rd
@@ -5,7 +5,7 @@
\alias{rucrdtw}
\title{rucrdtw: Fast time series subsequence search in R}
\description{
-Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the \strong{dtw} package (Giorgino 2009).
+Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the \strong{dtw} package (Giorgino 2009).
Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The UCR Suite (Rakthanmanon et al. 2012) provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
diff --git a/paper.md b/paper.md
index 59bed74..a289200 100644
--- a/paper.md
+++ b/paper.md
@@ -14,7 +14,7 @@ bibliography: vignettes/rucrdtw.bib
---
# Summary
-Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection.
+Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection.
Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The [UCR Suite](http://www.cs.ucr.edu/~eamonn/UCRsuite.html) [@rakthanmanon2012searching] provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
diff --git a/vignettes/using_rucrdtw.Rmd b/vignettes/using_rucrdtw.Rmd
index 7482d81..4bdf344 100644
--- a/vignettes/using_rucrdtw.Rmd
+++ b/vignettes/using_rucrdtw.Rmd
@@ -11,7 +11,7 @@ bibliography: rucrdtw.bib
---
## Introduction
-Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection.
+Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection.
Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The [UCR Suite](http://www.cs.ucr.edu/~eamonn/UCRsuite.html) [@rakthanmanon2012searching] provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
@@ -89,7 +89,7 @@ legend("topright", legend = c("query", "DTW match", "ED match"), col=c("red", "b
```
## Comparison with a naive DTW sub-sequence search
-We can compare the speed-up achived with the UCR algorithm by comparing it to a naive sliding-window comparison with the `dtw` function from the [`dtw` package](https://CRAN.R-project.org/package=dtw) [@giorgino2009computing]. We create another time series and load `dtw`.
+We can compare the speed-up achieved with the UCR algorithm by comparing it to a naive sliding-window comparison with the `dtw` function from the [`dtw` package](https://CRAN.R-project.org/package=dtw) [@giorgino2009computing]. We create another time series and load `dtw`.
```{r dtw-comparison, message=FALSE}
set.seed(123)
diff --git a/vignettes/using_rucrdtw.html b/vignettes/using_rucrdtw.html
index ba62ca3..52833da 100644
--- a/vignettes/using_rucrdtw.html
+++ b/vignettes/using_rucrdtw.html
@@ -310,7 +310,7 @@ 2016-11-06
Introduction
-
Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series (Berndt and Clifford 1994). The remaining cumulative distance between the series after the alignement is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection.
+
Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series (Berndt and Clifford 1994). The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection.
Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The UCR Suite (Rakthanmanon et al. 2012) provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.
A broad suite of DTW algorithms is implemented in R in the dtw package (Giorgino 2009). The rucrdtw R package provides complementary functionality for fast similarity searches by providing R bindings for the UCR Suite via Rcpp (Eddelbuettel and Francois 2011). In addition to queries and data stored in text files, rucrdtw also implements methods for queries and/or data that are held in memory as R objects, as well as a method to do fast similarity searches against reference libraries of time series.
@@ -333,12 +333,12 @@ Examples
Since both query and data are R vectors, we use the vector-vector methods for the search.
## user system elapsed
-## 1.71 0.02 1.72
+## 1.59 0.00 1.61
## [1] TRUE
## user system elapsed
-## 1.72 0.00 1.76
+## 1.61 0.01 1.65
## [1] TRUE
And in a matter of seconds we have searched 10 million data points and rediscovered our query!
@@ -368,7 +368,7 @@ Examples
Comparison with a naive DTW sub-sequence search
-
We can compare the speed-up achived with the UCR algorithm by comparing it to a naive sliding-window comparison with the dtw function from the dtw package (Giorgino 2009). We create another time series and load dtw.
+
We can compare the speed-up achieved with the UCR algorithm by comparing it to a naive sliding-window comparison with the dtw function from the dtw package (Giorgino 2009). We create another time series and load dtw.
## Loading required package: rbenchmark
-
+
The speed-up is approximately 3 orders of magnitude.