Resolving devtools::check() warnings

R/HierPoolPrev.R

@@ -107,8 +107,6 @@
 #' you can use: \code{list(individual_sd = TRUE)}, which puts a prior on each
 #' the standard deviations of each of group-level effects separately, but
 #' doesn't change the priors used.
-#'
-
 
 HierPoolPrev <- function(data,result,poolSize,hierarchy,...,
                          prior = NULL,
@@ -186,7 +184,7 @@ HierPoolPrev <- function(data,result,poolSize,hierarchy,...,
                             cores = cores,
                             control = control)
     #return(sfit)
-    sfit <- extract(sfit) %>% as_tibble() %>% rowwise()
+    sfit <- extract(sfit) %>% dplyr::as_tibble() %>% rowwise()
 
     prevICC <- sfit %>%
       transmute(prev = meanlinknormal(Intercept,
@@ -209,7 +207,7 @@ HierPoolPrev <- function(data,result,poolSize,hierarchy,...,
       estimate.type = 'consistent'
     }
 
-    out <- tibble::tibble(PrevBayes =
+    out <- dplyr::tibble(PrevBayes =
                             switch(estimate.type,
                                    consistent = mean(prev),
                                    zero = 0)
@@ -225,7 +223,7 @@ HierPoolPrev <- function(data,result,poolSize,hierarchy,...,
     out$NumberOfPools <- sdata$N
     out$NumberPositive <- sum(sdata$Result)
 
-    out$ICC <- ICC %>% apply(2, median) %>% t()
+    out$ICC <- ICC %>% apply(2, stats::median) %>% t()
     out$ICC_CrILow  <- ICC %>% apply(2, stats::quantile, probs = (1-level)/2) %>% t()
     out$ICC_CrIHigh <- ICC %>% apply(2, stats::quantile, probs = (1+level)/2) %>% t()
 
@@ -300,4 +298,3 @@ print.HierPoolPrevOutput <- function(x, ...) {
   print(formatted_output)
   return(invisible(x))
 }
-

R/PoolPrev.R

@@ -79,14 +79,11 @@
 #'   prevalence, \code{CLow} is the lower confidence/credible interval and 
 #'   \code{CHigh} is the upper confidence/credible interval. When printed, the
 #'   prevalence estimate is represented as a percentage (i.e., per 100 units)
-#'   
 #'
 #' @seealso \code{\link{HierPoolPrev}}, \code{\link{getPrevalence}}
 #'
 #' @example examples/Prevalence.R
 
-
-
 PoolPrev <- function(data,result,poolSize,...,
                      bayesian = TRUE, prior = NULL,
                      level = 0.95, reproduce.poolscreen = FALSE,
@@ -170,7 +167,7 @@ PoolPrev <- function(data,result,poolSize,...,
     }
 
     #initialise output object
-    out <- tibble::tibble(NumberOfPools = sdata$N,
+    out <- dplyr::tibble(NumberOfPools = sdata$N,
                           NumberPositive = sum(sdata$Result))
 
     #if there is at least one positive and one negative result

R/utils.R

@@ -12,9 +12,12 @@ extract_matrix_column_ICC <- function(cluster_var, x){
     # Extract only the columns for this clustering variable
     matrix_cols <- x %>%
       select(grep("ICC", names(x), value = T))
-    cluster_cols <- as_tibble(lapply(names(matrix_cols),
-                                     function(x){matrix_cols[[x]][ , which(all_cluster_vars == cluster_var)]}),
-                              .name_repair = "minimal")
+    cluster_cols <- tibble::as_tibble(
+      lapply(
+        names(matrix_cols),
+        function(x){matrix_cols[[x]][ , which(all_cluster_vars == cluster_var)]}
+      ),
+      .name_repair = "minimal")
     names(cluster_cols) <- paste0(cluster_var, ".", names(matrix_cols))
     formatted_cluster_cols <- pretty_format_ICC_column(cluster_cols) # Pretty print
     return(formatted_cluster_cols)

cran-comments.md

@@ -13,7 +13,7 @@ There may be 4 NOTEs depending on platform (and psuedo-random number generation)
 * ```checking dependencies in R code ... NOTE
   Namespace in Imports field not imported from: 'RcppParallel' 'rstantools'
   All declared Imports should be used.```
-  PoolTestR *does* use both of these pacakges, so this NOTE is erroneous.
+  PoolTestR *does* use both of these packages, so this NOTE is erroneous.
 * ```installed package size ... NOTE```
   The large files appear to be in ```libs```, but the size appears to be highly platform dependent. On my local build this is only ~3 MB but in others it is >40 MB. I'm not sure how to avoid this issue and I am guessing it is largely outside of the scope of my package.