URL fixes

R/srr-stats-standards.R

@@ -107,7 +107,7 @@
 #'   Done for all functions.
 #' @srrstats {TS1.2} *Time Series Software should implement validation routines to confirm that inputs are of acceptable classes (or represented in otherwise appropriate ways for software which does not use class systems).*
 #'   Done for all functions.
-#' @srrstats {TS1.3} *Time Series Software should implement a single pre-processing routine to validate input data, and to appropriately transform it to a single uniform type to be passed to all subsequent data-processing functions (the [`tsbox` package](https://www.tsbox.help/) provides one convenient approach for this).*
+#' @srrstats {TS1.3} *Time Series Software should implement a single pre-processing routine to validate input data, and to appropriately transform it to a single uniform type to be passed to all subsequent data-processing functions (the [`tsbox` package](https://docs.ropensci.org/tsbox/) provides one convenient approach for this).*
 #'   This is at the core of tsbox: Everything is converted to a 'dts' object, operations are performed on theser, then they are converted back.
 #' @srrstats {TS1.4} *The pre-processing function described above should maintain all time- or date-based components or attributes of input data.*
 #'   All ts-boxable classes as well as 'dts' objects keep this information intact.
@@ -121,7 +121,7 @@
 #' @srrstats {TS2.5} *Incorporate a system to ensure that both row and column orders follow the same ordering as the underlying time series data. This may, for example, be done by including the `index` attribute of the time series data as an attribute of the covariance matrix.*
 #'   All output keeps column order. Row order is re-arranged by time if needed, in line with {TS1.5}. ts_default() reorders columns to id columns, `time` and `value`.
 #' @srrstats {TS4.0} *Return values should either:*
-#' @srrstats {TS4.0a} *Be in same class as input data, for example by using the [`tsbox` package](https://www.tsbox.help/) to re-convert from standard internal format (see 1.4, above); or*
+#' @srrstats {TS4.0a} *Be in same class as input data, for example by using the [`tsbox` package](https://docs.ropensci.org/tsbox/) to re-convert from standard internal format (see 1.4, above); or*
 #'   The core of this package.
 #' @srrstats {TS4.1} *Any units included as attributes of input data should also be included within return values.*
 #'   Units definied via the `units` package stay alive as long as one works with data frame like objects (data table, tibble). ts objects currently loose the unit, as they are not supported by `units`.

R/ts_.R

@@ -32,7 +32,7 @@ load_suggested <- function(pkg) {
 #' @return A function that accepts ts-boxable time series as an input.
 #' @seealso [ts_examples], for a few useful examples of functions generated by
 #'   `ts_`.
-#' @seealso [Vignette](https://www.tsbox.help/articles/ts-functions.html) on how
+#' @seealso [Vignette](https://docs.ropensci.org/tsbox/articles/ts-functions.html) on how
 #'   to make arbitrary functions ts-boxable.
 #'
 #' @export

R/ts_examples.R

@@ -13,7 +13,7 @@
 #' @param ... further arguments, passed to the underlying function. For help,
 #'   consider these functions, e.g., [stats::prcomp].
 #'
-#' @seealso [Vignette](https://www.tsbox.help/articles/ts-functions.html) on how
+#' @seealso [Vignette](https://docs.ropensci.org/tsbox/articles/ts-functions.html) on how
 #'   to make arbitrary functions ts-boxable.
 #'
 #' @examples

R/tsbox-package.R

@@ -17,7 +17,7 @@
 #' use the same tsbox-command for any time series classes.
 #'
 #' The best way to start is to check out the package
-#' [website](https://www.tsbox.help).
+#' [website](https://docs.ropensci.org/tsbox/).
 #'
 #' In the *ropensci* classification, this package is *An improvement on other
 #' implementations of similar algorithms in **R***. Many time series packages,

README.Rmd

@@ -29,7 +29,7 @@ data frames, thus making it easy to deal with time series in a
 [dplyr](https://CRAN.R-project.org/package=dplyr) or
 [data.table](https://CRAN.R-project.org/package=data.table) workflow.
 
-See [tsbox.help](https://www.tsbox.help/) for the full documentation of
+See [tsbox.help](https://docs.ropensci.org/tsbox/) for the full documentation of
 the package.
 
 To install the stable version from CRAN:

README.md

@@ -21,7 +21,7 @@ it easy to deal with time series in a
 [dplyr](https://CRAN.R-project.org/package=dplyr) or
 [data.table](https://CRAN.R-project.org/package=data.table) workflow.
 
-See [tsbox.help](https://www.tsbox.help/) for the full documentation of
+See [tsbox.help](https://docs.ropensci.org/tsbox/) for the full documentation of
 the package.
 
 To install the stable version from CRAN:

codemeta.json

@@ -352,7 +352,7 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "557.419KB",
+  "fileSize": "557.561KB",
   "citation": [
     {
       "@type": "SoftwareSourceCode",
@@ -366,7 +366,7 @@
         }
       ],
       "name": "tsbox: Class-Agnostic Time Series in in {R}",
-      "url": "https://www.tsbox.help",
+      "url": "https://docs.ropensci.org/tsbox",
       "description": "{R package}"
     }
   ],

inst/CITATION

@@ -5,7 +5,7 @@ bibentry(
                           family = "Sax",
                           email = "[email protected]")),
   note         = "{R package}",
-  url          = "https://www.tsbox.help",
+  url          = "https://docs.ropensci.org/tsbox",
   year         = 2021,
 
   header       = "To cite tsbox in publications use:"