Standardise rbmi References (#450)

Closes #448

NEWS.md

@@ -23,7 +23,7 @@
 
 # rbmi 1.2.3
 
-* Minor internal tweaks to ensure compatibility with the packages rbmi depends on
+* Minor internal tweaks to ensure compatibility with the packages `rbmi` depends on
 
 # rbmi 1.2.1
 

R/analyse.R

@@ -96,7 +96,7 @@
 #'
 #' @section Parallelisation:
 #' To speed up the evaluation of `analyse()` you can use the `ncores` argument to enable parallelisation.
-#' Simply providing an integer will get rbmi to automatically spawn that many background processes
+#' Simply providing an integer will get `rbmi` to automatically spawn that many background processes
 #' to parallelise across. If you are using a custom analysis function then you need to ensure
 #' that any libraries or global objects required by your function are available in the
 #' sub-processes. To do this you need to use the [`make_rbmi_cluster()`] function for example:
@@ -127,8 +127,8 @@
 #' a temporary file on the local disk from which it is then read into each sub-process. This is
 #' done to avoid the overhead of transferring the data over the network. Our assumption is that
 #' if you are at the stage where you need to be parallelising your analysis over a remote cluster
-#' then you would likely be better off parallelising across multiple rbmi runs rather than within
-#' a single rbmi run.
+#' then you would likely be better off parallelising across multiple `rbmi` runs rather than within
+#' a single `rbmi` run.
 #'
 #' Finally, if you are doing a tipping point analysis you can get a reasonable performance
 #' improvement by re-using the cluster between each call to `analyse()` e.g.

R/lsmeans.R

@@ -19,7 +19,7 @@
 #' ```
 #' emmeans::emmeans(model, specs = "<treatment>", counterfactual = "<treatment>")
 #' ```
-#' Note that to ensure backwards compatibility with previous versions of rbmi
+#' Note that to ensure backwards compatibility with previous versions of `rbmi`
 #' `weights = "proportional"` is an alias for `weights = "counterfactual"`.
 #' To get results consistent with `emmeans`'s `weights = "proportional"`
 #' please use `weights = "proportional_em"`.

R/parallel.R

@@ -1,18 +1,18 @@
 
-#' Create a rbmi ready cluster
+#' Create a `rbmi` ready cluster
 #'
 #' @param ncores Number of parallel processes to use or an existing cluster to make use of
 #' @param objects a named list of objects to export into the sub-processes
 #' @param packages a character vector of libraries to load in the sub-processes
 #'
 #' This function is a wrapper around `parallel::makePSOCKcluster()` but takes
-#' care of configuring rbmi to be used in the sub-processes as well as loading
+#' care of configuring `rbmi` to be used in the sub-processes as well as loading
 #' user defined objects and libraries and setting the seed for reproducibility.
 #'
 #' If `ncores` is `1` this function will return `NULL`.
 #'
 #' If `ncores` is a cluster created via `parallel::makeCluster()` then this function
-#' just takes care of inserting the relevant rbmi objects into the existing cluster.
+#' just takes care of inserting the relevant `rbmi` objects into the existing cluster.
 #'
 #' @examples
 #' \dontrun{
@@ -54,7 +54,7 @@ make_rbmi_cluster <- function(ncores = 1, objects = NULL, packages = NULL) {
 
     # Load user defined packages
     packages <- c(packages, "assertthat")
-    # Remove attempts to load rbmi as this will be covered later
+    # Remove attempts to load `rbmi` as this will be covered later
     packages <- setdiff(packages, "rbmi")
     devnull <- parallel::clusterCall(
         cl,
@@ -65,7 +65,7 @@ make_rbmi_cluster <- function(ncores = 1, objects = NULL, packages = NULL) {
     # Ensure reproducibility
     parallel::clusterSetRNGStream(cl, sample.int(1))
 
-    # If user has previously configured rbmi sub-processes then early exit
+    # If user has previously configured `rbmi` sub-processes then early exit
     exported_rbmi <- unlist(parallel::clusterEvalQ(cl, exists("..exported..parallel..rbmi")))
     if (all(exported_rbmi)) {
         return(cl)
@@ -87,7 +87,7 @@ make_rbmi_cluster <- function(ncores = 1, objects = NULL, packages = NULL) {
         )
     }
 
-    # Set variable to signify rbmi has been configured
+    # Set variable to signify `rbmi` has been configured
     devnull <- parallel::clusterEvalQ(cl, {
         ..exported..parallel..rbmi <- TRUE
     })

R/rbmi.R

@@ -1,7 +1,7 @@
 
 #' @description
 #'
-#' The rbmi package is used to perform reference based multiple imputation. The package
+#' The `rbmi` package is used to perform reference based multiple imputation. The package
 #' provides implementations for common, patient-specific imputation strategies whilst allowing the user to
 #' select between various standard Bayesian and frequentist approaches.
 #'
@@ -11,7 +11,7 @@
 #' - [analyse()] - Analyses multiple datasets
 #' - [pool()] - Pools multiple results into a single statistic
 #'
-#' To learn more about rbmi, please see the quickstart vignette:
+#' To learn more about `rbmi`, please see the quickstart vignette:
 #'
 #' `vignette(topic= "quickstart", package = "rbmi")`
 #'

README.md

@@ -9,7 +9,7 @@ status](https://www.r-pkg.org/badges/version/rbmi)](https://cran.r-project.org/p
 
 ## Overview
 
-rbmi is a R package for imputation of missing data in clinical trials with continuous multivariate normal longitudinal outcomes. 
+The `rbmi` package is used for the imputation of missing data in clinical trials with continuous multivariate normal longitudinal outcomes. 
 It supports imputation under a missing at random (MAR) assumption, reference-based imputation methods, 
 and delta adjustments (as required for sensitivity analysis such as tipping point analyses). The package implements both Bayesian and 
 approximate Bayesian multiple imputation combined with Rubin's rules for inference, and frequentist conditional mean imputation combined with 
@@ -46,7 +46,7 @@ vignette(topic = "quickstart", package = "rbmi")
 
 ## Validation
 
-For clarification on the current validation status of rbmi please see the FAQ vignette.
+For clarification on the current validation status of `rbmi` please see the FAQ vignette.
 
 
 ## Support

misc/do_not_delete.txt

@@ -1,4 +1,4 @@
 DO NOT DELETE
 
-This is a flag file whose existance indicates to rbmi that it is running in development mode.
+This is a flag file whose existance indicates to `rbmi` that it is running in development mode.
 Relevant code can be found in R/parallel.R

vignettes/CondMean_Inference.Rmd

@@ -147,7 +147,7 @@ Once the datasets have been imputed, we can call the `analyse()` function to app
 
 ```{r}
 
-# Set analysis variables using rbmi function "set_vars"
+# Set analysis variables using `rbmi` function "set_vars"
 vars_an <- set_vars(
   group = vars$group,
   visit = vars$visit,
@@ -237,7 +237,7 @@ get_delta_match_refBased <- function(draws, data_ice, references) {
   cmi <- cmi[, c(vars$subjid, vars$visit, vars$outcome)]
   colnames(cmi)[colnames(cmi) == vars$outcome] <- "y_imp"
   
-  # Map back original patients id since rbmi re-code ids to ensure id uniqueness
+  # Map back original patients id since `rbmi` re-code ids to ensure id uniqueness
   
   cmi[[vars$subjid]] <- idmap[match(cmi[[vars$subjid]], names(idmap))]
   
@@ -269,7 +269,7 @@ get_delta_match_refBased <- function(draws, data_ice, references) {
   cmi_MAR <- cmi_MAR[, c(vars$subjid, vars$visit, vars$outcome)]
   colnames(cmi_MAR)[colnames(cmi_MAR) == vars$outcome] <- "y_MAR"
   
-  # Map back original patients id since rbmi re-code ids to ensure id uniqueness
+  # Map back original patients id since `rbmi` re-code ids to ensure id uniqueness
   cmi_MAR[[vars$subjid]] <- idmap[match(cmi_MAR[[vars$subjid]], names(idmap))]
   
   # Derive delta adjustment "aligned with ref-based imputation",
@@ -300,7 +300,7 @@ The argument `delta` can be used to add a delta-adjustment prior to the analysis
 
 ```{r}
 
-# Set analysis variables using rbmi function "set_vars"
+# Set analysis variables using `rbmi` function "set_vars"
 vars_an <- set_vars(
   group = vars$group,
   visit = vars$visit,

vignettes/CondMean_Inference.html

@@ -506,7 +506,7 @@ <h2><span class="header-section-number">3.2</span> Impute</h2>
 <h2><span class="header-section-number">3.3</span> Analyse</h2>
 <p>Once the datasets have been imputed, we can call the <code>analyse()</code> function to apply the complete-data analysis model (here ANCOVA) to each imputed dataset.</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a></span>
-<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co"># Set analysis variables using rbmi function &quot;set_vars&quot;</span></span>
+<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co"># Set analysis variables using `rbmi` function &quot;set_vars&quot;</span></span>
 <span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a>vars_an <span class="ot">&lt;-</span> <span class="fu">set_vars</span>(</span>
 <span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a>  <span class="at">group =</span> vars<span class="sc">$</span>group,</span>
 <span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a>  <span class="at">visit =</span> vars<span class="sc">$</span>visit,</span>
@@ -682,7 +682,7 @@ <h2><span class="header-section-number">4.2</span> Imputation step including cal
 <span id="cb6-31"><a href="#cb6-31" tabindex="-1"></a>  cmi <span class="ot">&lt;-</span> cmi[, <span class="fu">c</span>(vars<span class="sc">$</span>subjid, vars<span class="sc">$</span>visit, vars<span class="sc">$</span>outcome)]</span>
 <span id="cb6-32"><a href="#cb6-32" tabindex="-1"></a>  <span class="fu">colnames</span>(cmi)[<span class="fu">colnames</span>(cmi) <span class="sc">==</span> vars<span class="sc">$</span>outcome] <span class="ot">&lt;-</span> <span class="st">&quot;y_imp&quot;</span></span>
 <span id="cb6-33"><a href="#cb6-33" tabindex="-1"></a>  </span>
-<span id="cb6-34"><a href="#cb6-34" tabindex="-1"></a>  <span class="co"># Map back original patients id since rbmi re-code ids to ensure id uniqueness</span></span>
+<span id="cb6-34"><a href="#cb6-34" tabindex="-1"></a>  <span class="co"># Map back original patients id since `rbmi` re-code ids to ensure id uniqueness</span></span>
 <span id="cb6-35"><a href="#cb6-35" tabindex="-1"></a>  </span>
 <span id="cb6-36"><a href="#cb6-36" tabindex="-1"></a>  cmi[[vars<span class="sc">$</span>subjid]] <span class="ot">&lt;-</span> idmap[<span class="fu">match</span>(cmi[[vars<span class="sc">$</span>subjid]], <span class="fu">names</span>(idmap))]</span>
 <span id="cb6-37"><a href="#cb6-37" tabindex="-1"></a>  </span>
@@ -714,7 +714,7 @@ <h2><span class="header-section-number">4.2</span> Imputation step including cal
 <span id="cb6-63"><a href="#cb6-63" tabindex="-1"></a>  cmi_MAR <span class="ot">&lt;-</span> cmi_MAR[, <span class="fu">c</span>(vars<span class="sc">$</span>subjid, vars<span class="sc">$</span>visit, vars<span class="sc">$</span>outcome)]</span>
 <span id="cb6-64"><a href="#cb6-64" tabindex="-1"></a>  <span class="fu">colnames</span>(cmi_MAR)[<span class="fu">colnames</span>(cmi_MAR) <span class="sc">==</span> vars<span class="sc">$</span>outcome] <span class="ot">&lt;-</span> <span class="st">&quot;y_MAR&quot;</span></span>
 <span id="cb6-65"><a href="#cb6-65" tabindex="-1"></a>  </span>
-<span id="cb6-66"><a href="#cb6-66" tabindex="-1"></a>  <span class="co"># Map back original patients id since rbmi re-code ids to ensure id uniqueness</span></span>
+<span id="cb6-66"><a href="#cb6-66" tabindex="-1"></a>  <span class="co"># Map back original patients id since `rbmi` re-code ids to ensure id uniqueness</span></span>
 <span id="cb6-67"><a href="#cb6-67" tabindex="-1"></a>  cmi_MAR[[vars<span class="sc">$</span>subjid]] <span class="ot">&lt;-</span> idmap[<span class="fu">match</span>(cmi_MAR[[vars<span class="sc">$</span>subjid]], <span class="fu">names</span>(idmap))]</span>
 <span id="cb6-68"><a href="#cb6-68" tabindex="-1"></a>  </span>
 <span id="cb6-69"><a href="#cb6-69" tabindex="-1"></a>  <span class="co"># Derive delta adjustment &quot;aligned with ref-based imputation&quot;,</span></span>
@@ -741,7 +741,7 @@ <h2><span class="header-section-number">4.3</span> Analyse</h2>
 <code>analyse()</code> will take as the input argument <code>imputations = res_delta_adjust$imputeObj_MAR</code>, i.e. the imputation object corresponding to the MAR imputation (and not the JR imputation).
 The argument <code>delta</code> can be used to add a delta-adjustment prior to the analysis and we set this to the delta-adjustment obtained in the previous step: <code>delta = res_delta_adjust$delta_adjust</code>.</p>
 <div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a></span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co"># Set analysis variables using rbmi function &quot;set_vars&quot;</span></span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co"># Set analysis variables using `rbmi` function &quot;set_vars&quot;</span></span>
 <span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>vars_an <span class="ot">&lt;-</span> <span class="fu">set_vars</span>(</span>
 <span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>  <span class="at">group =</span> vars<span class="sc">$</span>group,</span>
 <span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>  <span class="at">visit =</span> vars<span class="sc">$</span>visit,</span>

vignettes/FAQ.Rmd

@@ -34,7 +34,7 @@ It is intended to be read after the `rbmi: Quickstart` vignette.
 <br>
 
 
-## Is rbmi validated? 
+## Is `rbmi` validated? 
 
 With regards to software in the pharmaceutical industry, validation is the act of ensuring that the software meets the needs and
 requirements of users given the conditions of actual use. The FDA provides general principles and
@@ -44,12 +44,12 @@ validation depends on the entire software stack and the specific processes of ea
 
 That being said, some of the core components of any validation process are the design specification
 (what is the software supposed to do) as well as the testing / test results that demonstrate that
-the design specification has been met. For rbmi, the design specification is documented extensively,
+the design specification has been met. For `rbmi`, the design specification is documented extensively,
 both at a macro level in vignettes and literature publications, and at a micro level in detailed
 function manuals. This is supported by our extensive suite of unit and integration tests, which
 ensure the software consistently produces correct output across a wide range of input scenarios. 
 
-This documentation and test coverage enable rbmi to be easily installed and integrated into any
+This documentation and test coverage enable `rbmi` to be easily installed and integrated into any
 R system, in alignment with the system's broader validation process.