Switching to canonical CRAN URLs

README.md

@@ -74,7 +74,7 @@ msdata <- grabMSdata(files = msdata_files[2:4], grab_what = c("BPC", "MS1"))
 
 Some additional examples can be found below, but a more thorough
 introduction can be found in [the
-vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Intro-to-RaMS.html)
+vignette](https://cran.r-project.org/package=RaMS/vignettes/Intro-to-RaMS.html)
 or by typing `vignette("Intro-to-RaMS", package = "RaMS")` in the R
 console after installation.
 
@@ -265,19 +265,19 @@ knitr::kable(head(chromsdata$chroms, 3))
 
 As of version 1.1.0, `RaMS` has functions that allow irrelevant data to
 be removed from the file to reduce file sizes. See the
-[vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html)
+[vignette](https://cran.r-project.org/package=RaMS/vignettes/Minifying-files-with-RaMS.html)
 for more details.
 
 #### tmzML documents
 
 Version 1.2.0 of RaMS introduced a new file type, the “transposed mzML”
 or “tmzML” file to resolve the large memory requirement when working
 with many files. See [the
-vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Intro-to-tmzML.html)
+vignette](https://cran.r-project.org/package=RaMS/vignettes/Intro-to-tmzML.html)
 for more details, though note that I’ve largely deprecated this file
 type in favor of proper database solutions as in the [speed & size
 comparison
-vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html).
+vignette](https://cran.r-project.org/package=RaMS/vignettes/speed_size_comparison.html).
 
 ## File types
 
@@ -315,7 +315,7 @@ msdata$metadata
 For an analysis of how RaMS compares to other methods of MS data access
 and alternative file types, consider browsing the [speed & size
 comparison
-vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html).
+vignette](https://cran.r-project.org/package=RaMS/vignettes/speed_size_comparison.html).
 
 ## Contact
 
@@ -324,4 +324,4 @@ Issues page](https://github.com/wkumler/RaMS/issues).
 
 ------------------------------------------------------------------------
 
-README last built on 2024-09-17
+README last built on 2024-09-19

vignettes/Intro-to-RaMS.Rmd

@@ -46,7 +46,7 @@ If you have your own data, feel free to load it here. If not, there's a couple
 small example files you're welcome to use in the "extdata" folder. The first of 
 these contains DDA data from a pooled sample, while the others are individual 
 samples. I'll be using these throughout. For more details on the origin of these
-files, see the [minification vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html).
+files, see the [minification vignette](https://cran.r-project.org/package=RaMS/vignettes/Minifying-files-with-RaMS.html).
 
 ```{r findfiles, message=FALSE}
 library(RaMS)
@@ -309,7 +309,7 @@ with `RaMS` and are looking to optimize speed, reduce memory requirements, or
 are otherwise interested in the details of what `RaMS` does under the hood. If
 you're just getting started, I strongly recommend applying `RaMS` to your own
 data before you read on. For a more detailed analysis of the first two sections
-on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html)
+on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://cran.r-project.org/package=RaMS/vignettes/speed_size_comparison.html)
 
 ### Saving space: EICs and rtrange
 

vignettes/Intro-to-tmzML.Rmd

@@ -28,7 +28,7 @@ knitr::opts_chunk$set(
 
 This vignette deals with a feature still undergoing active development. While I
 can't promise to update it immediately with changes, I will do my best to keep
-you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html) for more stable alternatives.
+you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://cran.r-project.org/package=RaMS/vignettes/speed_size_comparison.html) for more stable alternatives.
 
 **Table of contents:**
 

vignettes/speed_size_comparison.Rmd

@@ -58,7 +58,7 @@ ms_files <- list.files("vignettes/figures/ssc_vignette_renders/Sample/", full.na
 
 ## Intro
 
-As noted in the [RaMS and friends vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
+As noted in the [RaMS and friends vignette](https://cran.r-project.org/package=RaMS/vignettes/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
 
 The general constraints I'm interested in are speed and space, with a minor consideration for simplicity afterward. My question of interest is how costly in these areas access to the MS data is, a process that can be broken down into two relevant steps - the transformation of the MS data from mz(X)ML format into a queryable format, and then the actual query itself. The query itself is relatively straightforward and can be measured in just the amount of time it takes, but the transformation has a caveat that it can be stored either in memory (and therefore must be recreated every time R is restarted) or it can be stored on disk (which is robust across restarts). So I'm interested in