fix: #102 deprecated arg in derive_param_computed (#103)

* fix: #102 deprecated arg in derive_param_computed

* chore: #102 spelling

* chore: #102 links

inst/WORDLIST.txt

@@ -511,3 +511,7 @@ pageID
 QHuA
 thome
 traceRedir
+pKobZqjlXChj
+si
+vehIoJgdA
+youtu

posts/2023-06-27__hackathon_app/index.qmd

@@ -23,7 +23,7 @@ long_slug <- "2023-06-19_shiny_run_script"
 
 
 We recently created a shiny application for the [`admiral` hackathon](https://cosa.cdisc.org/events/Admiral) in February 2023.
-The [`admiral` hackathon](https://cosa.cdisc.org/events/Admiral) was an event designed to make statistical programmers from the pharmaceutical industry more comfortable with the [`admiral`](https://pharmaverse.github.io/admiral/cran-release/) R package which allows users to efficiently transform data from one data standard (SDTM) to another (ADaM).
+The [`admiral` hackathon](https://cosa.cdisc.org/events/Admiral) was an event designed to make statistical programmers from the pharmaceutical industry more comfortable with the [`admiral`](https://pharmaverse.github.io/admiral/) R package which allows users to efficiently transform data from one data standard (SDTM) to another (ADaM).
 
 Hackathon participants formed groups of up to five people and were then tasked to create R-scripts that map the SDTM data to ADaM according to specifics defined in the metadata.
 
@@ -32,7 +32,7 @@ The purpose of the shiny app was threefold:
 1.  Allow participants to easily register as a team.
 
 
-2.  Let participants upload their R-scripts, so that the [`admiral` team](https://github.com/pharmaverse/admiral/graphs/contributors) can see how the [`admiral`](https://pharmaverse.github.io/admiral/cran-release/) package is being used -- and how the functions and the documentation could be improved.
+2.  Let participants upload their R-scripts, so that the [`admiral` team](https://github.com/pharmaverse/admiral/graphs/contributors) can see how the [`admiral`](https://pharmaverse.github.io/admiral/) package is being used -- and how the functions and the documentation could be improved.
 
 
 3.  Run participants R-scripts on the cloud and compare the produced file to a solution file.

posts/2023-06-27_admiral/valuelevel/derive_param_computed.qmd

@@ -29,7 +29,7 @@ options(width = narrow)
 
 When creating ADaM Basic Data Structure (BDS) datasets, we often encounter deriving a new parameter based on the analysis values (e.g., AVAL) of other parameters.
 
-The [`admiral`](https://pharmaverse.github.io/admiral/cran-release/) function [`derive_param_computed()`](https://pharmaverse.github.io/admiral/cran-release/reference/derive_param_computed.html) adds a parameter computed from the analysis value of other parameters.
+The [`admiral`](https://pharmaverse.github.io/admiral/) function [`derive_param_computed()`](https://pharmaverse.github.io/admiral/reference/derive_param_computed.html) adds a parameter computed from the analysis value of other parameters.
 
 It works like a calculator to derive new records without worrying about merging and combining datasets, all you need is a derivation formula, which also improves the readability of the code.
 
@@ -69,10 +69,10 @@ adex_admiral <- derive_param_computed(
   adex,
   by_vars = exprs(USUBJID),
   parameters = c("TOTDOSE", "NUMCYC"),
-  analysis_value = AVAL.TOTDOSE / AVAL.NUMCYC,
   set_values_to = exprs(
     PARAMCD = "ADPCYMG",
-    PARAM = "Actual Dose per Cycle (mg)"
+    PARAM = "Actual Dose per Cycle (mg)",
+    AVAL = AVAL.TOTDOSE / AVAL.NUMCYC
   )
 )
 ```

posts/2023-07-10_blanks_and_nas/blanks_and_nas.qmd

@@ -28,7 +28,7 @@ to bring in your source data to construct your ADaMs or SDTMs.
 
 Unfortunately, while using [haven](https://haven.tidyverse.org/) the character blanks (missing data) found in a typical SAS-based dataset are left as blanks. These blanks will typically prove problematic while using functions like `is.na` in combination with `dplyr::filter()` to subset data. Check out [Bayer's SAS2R catalog: handling-of-missing-values](https://bayer-group.github.io/sas2r/r-and-sas-syntax.html#handling-of-missing-values) for more discussion on missing values and `NAs`.
 
-In the [admiral](https://pharmaverse.github.io/admiral/cran-release/) package, we have built a simple function called `convert_blanks_to_na()` to help us quickly remedy this problem. You can supply an entire dataframe to this function and it will convert any character blanks to `NA_character_`
+In the [admiral](https://pharmaverse.github.io/admiral/) package, we have built a simple function called `convert_blanks_to_na()` to help us quickly remedy this problem. You can supply an entire dataframe to this function and it will convert any character blanks to `NA_character_`
 
 ## Loading Packages and Making Dummy Data
 
@@ -64,7 +64,7 @@ df_na %>% filter(is.na(RFICDTC))
 
 ## That's it!
 
-A simple call to this function can make your derivation life so much easier while working in R if working with SAS-based datasets. In [admiral](https://pharmaverse.github.io/admiral/cran-release/), we make use of this function at the start of all ADaM templates for common ADaM datasets. You can use the function `use_ad_template()` to get the full R script for the below ADaMs.
+A simple call to this function can make your derivation life so much easier while working in R if working with SAS-based datasets. In [admiral](https://pharmaverse.github.io/admiral/), we make use of this function at the start of all ADaM templates for common ADaM datasets. You can use the function `use_ad_template()` to get the full R script for the below ADaMs.
 
 ```{r}
 list_all_templates()

posts/2023-07-14_code_sections/code_sections.qmd

@@ -23,7 +23,7 @@ long_slug <- "2023-07-14_code_sections"
 
 ## Introduction
 
-The [`admiral`](https://pharmaverse.github.io/admiral/cran-release/) package embraces a modular style of programming, where blocks of code are pieced together in sequence to create an ADaM dataset. However, with the well-documented advantages of the modular approach comes the recognition that scripts will on average be longer. As such, astute programmers working in RStudio are constantly on the lookout for quick ways to effectively navigate their scripts. Enter **code sections**!
+The [`admiral`](https://pharmaverse.github.io/admiral/) package embraces a modular style of programming, where blocks of code are pieced together in sequence to create an ADaM dataset. However, with the well-documented advantages of the modular approach comes the recognition that scripts will on average be longer. As such, astute programmers working in RStudio are constantly on the lookout for quick ways to effectively navigate their scripts. Enter **code sections**!
 
 ## So, what are code sections and why are they useful?
 
@@ -73,7 +73,7 @@ For a complete list of Code Sections shortcuts, and for further information, see
 
 # Code Sections on `admiral`
 
-Within the [`admiral`](https://pharmaverse.github.io/admiral/cran-release/) package, heavy use of code sections is made in scripts such as our template ADaM programs. This ensures that they are more traversable and also encourages good commenting practices throughout the program. Here is an example using the ADAE template program:
+Within the [`admiral`](https://pharmaverse.github.io/admiral/) package, heavy use of code sections is made in scripts such as our template ADaM programs. This ensures that they are more traversable and also encourages good commenting practices throughout the program. Here is an example using the ADAE template program:
 
 ```{r, eval = FALSE, message = FALSE}
 library(admiral)
@@ -87,7 +87,7 @@ knitr::include_graphics("adae_code_sections.png")
 ```
 
 ## Conclusion
-Code sections are an easy way to navigate long scripts and foster good commenting practices. They are used extensively in the [`admiral`](https://pharmaverse.github.io/admiral/cran-release/) package, but there is no reason that you cannot start using them yourself in your day-to-day R programming!
+Code sections are an easy way to navigate long scripts and foster good commenting practices. They are used extensively in the [`admiral`](https://pharmaverse.github.io/admiral/) package, but there is no reason that you cannot start using them yourself in your day-to-day R programming!
 
 <!--------------- appendices go here ----------------->
 

posts/2023-08-08_study_day/study_day.qmd

@@ -21,7 +21,7 @@ long_slug <- "2023-08-08_study_day"
 
 <!--------------- post begins here -----------------> 
 
-Creating `--DY` variables for your ADaMs is super easy using  [`derive_vars_dy()`](https://pharmaverse.github.io/admiral/v0.11.1/reference/derive_vars_dy.html) from the [admiral package](https://pharmaverse.github.io/admiral/cran-release/).
+Creating `--DY` variables for your ADaMs is super easy using  [`derive_vars_dy()`](https://pharmaverse.github.io/admiral/v0.11.1/reference/derive_vars_dy.html) from the [admiral package](https://pharmaverse.github.io/admiral/).
 
 
 Let's build some dummy data with 4 subjects, a start date/time for treatment (`TRTSDTM`), an analysis start date/time variable (`ASTDTM`) and an analysis end date variable (`AENDT`).
@@ -73,7 +73,7 @@ derive_vars_dy(
 ```
 
 
-If you want to get `--DT` or `--DTM` variables using `admiral` then check out [`derive_vars_dt()`](https://pharmaverse.github.io/admiral/cran-release/reference/derive_vars_dt.html) and [`derive_vars_dtm()`](https://pharmaverse.github.io/admiral/cran-release/reference/derive_vars_dtm.html). If things are messy in your data, e.g. partial dates, both functions have great imputation abilities, which we will cover in an upcoming blog post!
+If you want to get `--DT` or `--DTM` variables using `admiral` then check out [`derive_vars_dt()`](https://pharmaverse.github.io/admiral/reference/derive_vars_dt.html) and [`derive_vars_dtm()`](https://pharmaverse.github.io/admiral/reference/derive_vars_dtm.html). If things are messy in your data, e.g. partial dates, both functions have great imputation abilities, which we will cover in an upcoming blog post!
 
 
 <!--------------- appendices go here ----------------->

posts/2023-09-26_date_functions_and_imputation/date_functions_and_imputation.qmd

@@ -36,19 +36,19 @@ In this article, we first examine the arsenal of functions provided by`{admiral}
 `{admiral}` provides the following functions for date/datetime imputation:
 
 - Derivations for adding variables
-  - [derive_vars_dt()](https://pharmaverse.github.io/admiral/cran-release/reference/derive_vars_dt.html): Adds a date variable and a date imputation flag variable (optional) based on a --DTC variable and imputation rules.
-  - [derive_vars_dtm()](https://pharmaverse.github.io/admiral/cran-release/reference/derive_vars_dtm.html): Adds a datetime variable, a date imputation flag variable, and a time imputation flag variable (both optional) based on a --DTC variable and imputation rules.
+  - [derive_vars_dt()](https://pharmaverse.github.io/admiral/reference/derive_vars_dt.html): Adds a date variable and a date imputation flag variable (optional) based on a --DTC variable and imputation rules.
+  - [derive_vars_dtm()](https://pharmaverse.github.io/admiral/reference/derive_vars_dtm.html): Adds a datetime variable, a date imputation flag variable, and a time imputation flag variable (both optional) based on a --DTC variable and imputation rules.
 - Computation functions
-  - [impute_dtc_dtm()](https://pharmaverse.github.io/admiral/cran-release/reference/impute_dtc_dtm.html): Returns a complete ISO 8601 datetime or `NA` based on a partial ISO 8601 datetime and imputation rules.
-  - [impute_dtc_dt()](https://pharmaverse.github.io/admiral/cran-release/reference/impute_dtc_dt.html): Returns a complete ISO 8601 date (without time) or `NA` based on a partial ISO 8601 date(time) and imputation rules.
-  - [convert_dtc_to_dt()](https://pharmaverse.github.io/admiral/cran-release/reference/convert_dtc_to_dt.html): Returns a date if the input ISO 8601 date is complete. Otherwise, `NA` is returned.
-  - [convert_dtc_to_dtm()](https://pharmaverse.github.io/admiral/cran-release/reference/convert_dtc_to_dtm.html): Returns a datetime if the input ISO 8601 date is complete (with missing time replaced by `"00:00:00"` as default). Otherwise, NA is returned.
-  - [compute_dtf()](https://pharmaverse.github.io/admiral/cran-release/reference/compute_dtf.html): Returns the date imputation flag.
-  - [compute_tmf()](https://pharmaverse.github.io/admiral/cran-release/reference/compute_tmf.html): Returns the time imputation flag.
+  - [impute_dtc_dtm()](https://pharmaverse.github.io/admiral/reference/impute_dtc_dtm.html): Returns a complete ISO 8601 datetime or `NA` based on a partial ISO 8601 datetime and imputation rules.
+  - [impute_dtc_dt()](https://pharmaverse.github.io/admiral/reference/impute_dtc_dt.html): Returns a complete ISO 8601 date (without time) or `NA` based on a partial ISO 8601 date(time) and imputation rules.
+  - [convert_dtc_to_dt()](https://pharmaverse.github.io/admiral/reference/convert_dtc_to_dt.html): Returns a date if the input ISO 8601 date is complete. Otherwise, `NA` is returned.
+  - [convert_dtc_to_dtm()](https://pharmaverse.github.io/admiral/reference/convert_dtc_to_dtm.html): Returns a datetime if the input ISO 8601 date is complete (with missing time replaced by `"00:00:00"` as default). Otherwise, NA is returned.
+  - [compute_dtf()](https://pharmaverse.github.io/admiral/reference/compute_dtf.html): Returns the date imputation flag.
+  - [compute_tmf()](https://pharmaverse.github.io/admiral/reference/compute_tmf.html): Returns the time imputation flag.
 
 From the point of view of a typical ADaM programmer, the functions `impute_*`, `convert_*` and `compute_*` above can be viewed as utilities for treating dates and/or imputation within any custom code. In contrast, their `derive_*` find their use in directly deriving new timing variables and/or carrying out imputation at an ADaM dataset scale.
 
-For a detailed look at the Imputation rules applied by these `{admiral}` functions, please visit [this vignette](https://pharmaverse.github.io/admiral/cran-release/articles/imputation.html#imputation-rules) on the documentation website.
+For a detailed look at the Imputation rules applied by these `{admiral}` functions, please visit [this vignette](https://pharmaverse.github.io/admiral/articles/imputation.html#imputation-rules) on the documentation website.
 
 # Simple Examples with Vectors
 
@@ -313,7 +313,7 @@ dataset_vignette(vs)
 
 Deriving timing variables and carrying out imputations is tricky at the best of times, but hopefully this blog post can shed some light on how make this all easier using the `{admiral}` package! As `{admiral}` developers we are always interested in knowing how users are employing the package for their ADaM needs, so if you have any comments or feedback related to this topic, don't be afraid to leave a comment on our [Slack channel](https://app.slack.com/client/T028PB489D3/C02M8KN8269) or on the [Github repository](https://github.com/pharmaverse/admiral/), either as an issue or as a discussion.
 
-For an even more detailed treatment of this topic, users are once again invited to read the corresponding [vignette](https://pharmaverse.github.io/admiral/cran-release/articles/imputation.html) on the documentation website, from which this article was adapted.
+For an even more detailed treatment of this topic, users are once again invited to read the corresponding [vignette](https://pharmaverse.github.io/admiral/articles/imputation.html) on the documentation website, from which this article was adapted.
 
 <!--------------- appendices go here ----------------->