fix URL redirections for CRAN

README.md

@@ -18,7 +18,7 @@ This document explains the purpose of this package and includes a
 tutorial on how to use it. It should take about 20 minutes to go through
 the entire document. A peer-reviewed publication describing this tool is
 available in
-[F1000Research](https://f1000research.com/articles/5-1411/).
+[F1000Research](https://f1000research.com/articles/5-1411).
 
 Table of contents
 =================
@@ -230,7 +230,7 @@ thresholds are manually set, and the other example uses the automated
 analysis. Explanation will follow, these are just here as a teaser.
 
 > Note how `ddpcr` is designed to play nicely with the [magrittr
-> pipe](https://github.com/smbache/magrittr) `%>%` for easier pipeline
+> pipe](https://github.com/tidyverse/magrittr) `%>%` for easier pipeline
 > workflows.
 
     library(ddpcr)

inst/shiny/text/about.md

@@ -18,7 +18,7 @@ Whenever a new dataset is loaded or the settings of an existing dataset are chan
 
 This tool is part of a package developed in the R programming language. For more information, <a target="_blank" href="https://github.com/daattali/ddpcr#readme" >see the complete README</a>.
 
-A peer-reviewed publication describing this tool is available in <a target="_blank" href="https://f1000research.com/articles/5-1411/">F1000Research</a>.
+A peer-reviewed publication describing this tool is available in <a target="_blank" href="https://f1000research.com/articles/5-1411">F1000Research</a>.
 
 The methods were originally developed for a paper <a target="_blank" href="https://jmd.amjpathol.org/article/S1525-1578(15)00262-7/">"Quantitative Detection and Resolution of BRAF V600 Status in Colorectal Cancer Using Droplet Digital PCR and a Novel Wild-Type Negative Assay"</a> by Roza Bidshahri, Dean Attali, et al. The sample data is also from the same project. 
 

vignettes/overview.Rmd

@@ -23,7 +23,7 @@ options(ddpcr.verbose = TRUE)
 
 This package provides an interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. It also includes an interactive web application with a visual user interface to facilitate analysis for anyone who is not comfortable with using R.  The app is [available online](https://daattali.com/shiny/ddpcr/) or it can be [run locally](#r-interactive).
 
-This document explains the purpose of this package and includes a tutorial on how to use it. It should take about 20 minutes to go through the entire document. A peer-reviewed publication describing this tool is available in [F1000Research](https://f1000research.com/articles/5-1411/).
+This document explains the purpose of this package and includes a tutorial on how to use it. It should take about 20 minutes to go through the entire document. A peer-reviewed publication describing this tool is available in [F1000Research](https://f1000research.com/articles/5-1411).
 
 # Table of contents
 
@@ -127,7 +127,7 @@ Even if you do know R, using the interactive application can be easier and more
 
 Here are two basic examples of how to use `ddpcr` to analyze and plot your ddPCR data. One example shows an analysis where the gating thresholds are manually set, and the other example uses the automated analysis. Explanation will follow, these are just here as a teaser.
 
-> Note how `ddpcr` is designed to play nicely with the [magrittr pipe](https://github.com/smbache/magrittr) `%>%` for easier pipeline workflows.
+> Note how `ddpcr` is designed to play nicely with the [magrittr pipe](https://github.com/tidyverse/magrittr) `%>%` for easier pipeline workflows.
 
 ```{r quickstart, message = FALSE, fig.show='hold', out.width='50%', fig.retina=FALSE}
 library(ddpcr)