version 0.0.2.7

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: clusterlab
 Title: Flexible Gaussian Cluster Simulator
-Version: 0.0.2.6
-Date: 2019-01-22
+Version: 0.0.2.7
+Date: 2019-08-02
 Author: Christopher R John
 Maintainer: Christopher R John <chris.r.john86@gmail.com>
 Depends: R (>= 3.4.0)
@@ -14,6 +14,6 @@ Suggests: knitr
 VignetteBuilder: knitr
 RoxygenNote: 6.0.1
 NeedsCompilation: no
-Packaged: 2019-01-22 10:59:45 UTC; christopher
+Packaged: 2019-08-02 12:59:33 UTC; christopher
 Repository: CRAN
-Date/Publication: 2019-01-22 11:30:03 UTC
+Date/Publication: 2019-08-02 13:30:02 UTC

MD5

@@ -1,9 +1,9 @@
-d782b3aa12b78fe8011de06bfb8b7fa8 *DESCRIPTION
+50827e90fd73d0533f89d3b70773a8d0 *DESCRIPTION
 9a58c4ff185bf282e97413b6f038b776 *NAMESPACE
 c5e3ebaeee6f9f22df58df4183703565 *R/clusterlab.R
-b13957cb44d63c30e2985088f0f89ee0 *build/vignette.rds
+e07ebb9c72391ac8272034e022f8aa74 *build/vignette.rds
 a1193cfcf6c957f54d1f1796bdd9c8d2 *inst/doc/introduction.R
-ddb87136769427766b7d9186030d6999 *inst/doc/introduction.Rmd
-136954856a451594207b1fc6003c3e25 *inst/doc/introduction.pdf
+301eb794bcc587cbc96e5a63d9eff34b *inst/doc/introduction.Rmd
+ba4156088e8b9f1272a4d63830718218 *inst/doc/introduction.pdf
 cd1600ad6fcab759c797a3ca5b610de5 *man/clusterlab.Rd
-ddb87136769427766b7d9186030d6999 *vignettes/introduction.Rmd
+301eb794bcc587cbc96e5a63d9eff34b *vignettes/introduction.Rmd

inst/doc/introduction.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created clusterlab. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers. Clusterlab is highly customizable and well suited to testing class discovery tools across a range of fields.
+Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created clusterlab. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers.
 
 ## Contents
 
@@ -175,7 +175,3 @@ Clusterlab also keeps track of the cluster allocations and gives each sample an
 ```{r,fig.width=4.5,fig.height=4.5}
 head(synthetic$identity_matrix)
 ```
-
-## 14. Closing comments
-
-We have seen how the clusterlab package may generate NXN Gaussian clusters in a flexible manner. Clusterlab was developed for the testing of class discovery algorithms on high dimensional genome wide expression data. For class discovery of this type of data we recommend clusterlab's sister package, M3C which was developed in parallel. M3C has been extensively tested on high dimensional Gaussian clusters. M3C is available on the Bioconductor (https://bioconductor.org/packages/devel/bioc/html/M3C.html). Thanks for using clusterlab.

vignettes/introduction.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created clusterlab. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers. Clusterlab is highly customizable and well suited to testing class discovery tools across a range of fields.
+Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created clusterlab. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers.
 
 ## Contents
 
@@ -175,7 +175,3 @@ Clusterlab also keeps track of the cluster allocations and gives each sample an
 ```{r,fig.width=4.5,fig.height=4.5}
 head(synthetic$identity_matrix)
 ```
-
-## 14. Closing comments
-
-We have seen how the clusterlab package may generate NXN Gaussian clusters in a flexible manner. Clusterlab was developed for the testing of class discovery algorithms on high dimensional genome wide expression data. For class discovery of this type of data we recommend clusterlab's sister package, M3C which was developed in parallel. M3C has been extensively tested on high dimensional Gaussian clusters. M3C is available on the Bioconductor (https://bioconductor.org/packages/devel/bioc/html/M3C.html). Thanks for using clusterlab.