Release of v3.1.0

.Rbuildignore

@@ -3,3 +3,5 @@
 ^cran-comments\.md$
 ^revdep$
 ^\.travis\.yml$
+^doc$
+^Meta$

.gitignore

@@ -4,3 +4,5 @@
 .Ruserdata
 *.Rproj
 /revdep/.cache.rds
+doc
+Meta

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: SteinerNet
 Type: Package
-Version: 3.0.1
-Date: 2018-08-18
+Version: 3.1.0
+Date: 2020-08-21
 Title: Steiner Tree Approach for Graph Analysis
 Description: A set of functions for finding and analysing Steiner trees. It has applications in
     biological pathway network analysis. Sadeghi (2013) <doi:10.1186/1471-2105-14-144>.
@@ -15,11 +15,13 @@ Imports: grDevices,
     stats,
     utils,
     graphics
-Suggests: knitr,
-    rmarkdown
+Suggests: 
+    knitr,
+    rmarkdown,
+    testthat
 VignetteBuilder: knitr
 License: GPL-3
 Repository: CRAN
 NeedsCompilation: no
 Encoding: UTF-8
-RoxygenNote: 6.1.0
+RoxygenNote: 7.1.1

NEWS.md

@@ -1,5 +1,9 @@
 # News
 
+### v3.1.0
+
+* Added support of R 4.0.0 and higher. Improved the code taking into account that since version 4.0.0 matrix objects also inherit from class "array".
+
 ### v3.0.1
 
 * If vertices of input graph of `steinertree` function has a `name` attribute, vertices of the output graph won't have `realname` attribute. The original names will be still at `name` attribute.

README.md

@@ -15,6 +15,8 @@ The installation process may differ to different versions of package. See inform
 
 ### Versions > 3.0.0
 
+To get the latest version of the package and install it from CRAN run the following command:
+
     install.packages("SteinerNet")
 
 ### Version 2.0

cran-comments.md

@@ -1,11 +1,18 @@
 ## Test environments
 
-* local Linux Ubuntu 18.04 LTS install, R 3.5.0
-* win-builder (devel and release)
+* local macOS 10.14.6, R 4.0.2
+* travis-ci ubuntu 16.04.6 LTS (devel and release)
+* win-builder (devel, release, oldrelease)
 
 ## R CMD check results
 
-0 ERRORs | 0 WARNINGs | 0 NOTES
+0 errors | 0 warnings | 1 note
+
+* checking for non-standard things in the check directory ... NOTE
+  Found the following files/directories:
+    ‘plot1.pdf’
+
+  This is temporary file which is created after running an example.
 
 ## Downstream dependencies
 

vignettes/tutorial.Rmd

@@ -142,7 +142,7 @@ steinertree(type = "SPM", terminals = terminal_nodes[[1]],
 
 ## Optimization of resultant tree
 
-Optimization means returning the minimum spanning tree on resultant graph and removing all non-terminal nodes of degree one. **Note, that in version 2.0 this function was runned by default for several algorithms.** Again it is explicitly available in version 3.0 for the sake of convenience.
+Optimization means returning the minimum spanning tree on resultant graph and removing all non-terminal nodes of degree one. **Note, that in version 2.0 this function was run by default for several algorithms.** Again it is explicitly available in version 3.0 for the sake of convenience.
 
 If you want to reproduce results of the article, you need to specify `optimize = TRUE` for the following algorithms:
 
@@ -151,7 +151,7 @@ If you want to reproduce results of the article, you need to specify `optimize =
 * "RSP"
 * "SPM"
 
-For "EXA" algorithm this option is ignored. **Note, that in version 2.0 for "ASP" algorithm optimization was not avaliable at all.** It means, that experiments was condacted without further optimization.
+For "EXA" algorithm this option is ignored. **Note, that in version 2.0 for "ASP" algorithm optimization was not avaliable at all.** It means, that experiments was conducted without further optimization.
 
 ## Article results reproducibility
 
@@ -172,7 +172,7 @@ repetition <- rep(x = 0.5, 50)
 
 However, simultaneous work with the number of terminals selected and size of sets makes an output of some function too wired. So now you need to specify:
 
-* `ter_number`. Each element indicates the number of terminals to be selected and length of vector indecates the number of terminal sets to be picked.
+* `ter_number`. Each element indicates the number of terminals to be selected and length of vector indicates the number of terminal sets to be picked.
 * `prob`. prob[i] defines a propability with which each next node accepted or rejected while selecting ter_number[i] terminals. Usually this probability is the same for all terminals, so you may write something like this `prob = rep(0.5, #len of ter_number#)`
 
 The main difference is that you can now operate with **only one value** of number of terminal sets. So to reproduce results of the article, firstly, you need, for example, generate 50 terminal sets with 5 terminals in each.