Apply suggestions from code review (2)

Co-authored-by: Craig Gower-Page <[email protected]>
Signed-off-by: Alessandro Noci <[email protected]>

NEWS.md

@@ -1,5 +1,5 @@
 
-# rbmi 1.2.7
+# rbmi (development version)
 
 * Include vignette on how to obtain frequentist and information-anchored inference with conditional mean imputation using `rbmi`
 

vignettes/CondMean_Inference.Rmd

@@ -1,6 +1,6 @@
 ---
 title: "rbmi: Frequentist and information-anchored inference for reference-based conditional mean imputation"
-author: Craig Gower-Page, Alessandro Noci, and Marcel Wolbers
+author: Alessandro Noci, Marcel Wolbers, Craig Gower-Page
 output: 
   bookdown::html_document2:
     toc: true
@@ -179,9 +179,9 @@ get_delta_match_refBased <- function(draws, data_ice, references) {
   
   # Impute according to `data_ice`
   imputeObj <- impute(
-  draws = drawObj,
-  update_strategy = data_ice,
-  references = references
+    draws = drawObj,
+    update_strategy = data_ice,
+    references = references
   )
   
   vars <- imputeObj$data$vars

vignettes/stat_specs.Rmd

@@ -438,7 +438,7 @@ Treatment effects based on conditional mean imputation are deterministic. All ot
 
 ### Standard errors of the treatment effect 
 
-All approaches provide frequentist consistent estimates of the standard error for imputation under a MAR assumption. 
+All approaches for imputation under a MAR assumption provide consistent estimates of the frequentist standard error. 
 
 For reference-based imputation methods, the situation is more complicated and two different types of variance estimators have been proposed in the statistical literature (@Bartlett2021). 
 The first is the frequentist variance which describes the actual repeated sampling variability of the estimator.