Release preparation for version 0.12.2

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 0.12.1
-Date: 2025-08-19 00:38:37 UTC
-SHA: 68c5803de01dfc6cc8e43cfdf649de69e38b2a85
+Version: 0.12.2
+Date: 2026-03-23 14:03:31 UTC
+SHA: 3c048da92711ddc75d121a3c654073e20878b132

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: PKNCA
 Type: Package
 Title: Perform Pharmacokinetic Non-Compartmental Analysis
-Version: 0.12.1.9000
+Version: 0.12.2
 Authors@R: c(
     person("Bill", "Denney", email="wdenney@humanpredictions.com", role=c("aut", "cre"), comment=c(ORCID="0000-0002-5759-428X")),
     person("Clare", "Buckeridge", email="clare.buckeridge@pfizer.com", role="aut"),

NEWS.md

@@ -4,7 +4,7 @@ will continue until then.  These will be especially noticeable around
 the inclusion of IV NCA parameters and additional specifications of
 the dosing including dose amount and route.
 
-# Development version
+# PKNCA 0.12.2
 
 ## Breaking changes
 

R/half.life.R

@@ -212,18 +212,18 @@ pk.calc.half.life <- function(conc, time, tmax, tlast,
       )
     half_lives_for_selection <-
       half_lives_for_selection[order(-half_lives_for_selection$lambda.z.time.first), ]
+    # Pre-build the two-column data.frame that fit_half_life expects.  The rows
+    # are already in descending-time order (matching half_lives_for_selection).
+    # Subsetting a pre-built data.frame avoids constructing a new data.frame on
+    # every loop iteration.
+    dfK_for_fit <- data.frame(
+      log_conc = half_lives_for_selection$log_conc,
+      time     = half_lives_for_selection$lambda.z.time.first
+    )
     for(i in min.hl.points:nrow(half_lives_for_selection)) {
       # Fit the terminal slopes until the adjusted r-squared value
       # is not improving (or it only gets worse by a small factor).
-      fit <-
-        fit_half_life(
-          data=
-            data.frame(
-              log_conc=half_lives_for_selection$log_conc[1:i],
-              time=half_lives_for_selection$lambda.z.time.first[1:i]
-            ),
-          tlast=ret$tlast
-        )
+      fit <- fit_half_life(data=dfK_for_fit[seq_len(i), , drop=FALSE], tlast=ret$tlast)
       half_lives_for_selection[i,names(fit)] <- fit
     }
     # Find the best model

R/pk.calc.all.R

@@ -196,7 +196,11 @@ pk.nca.intervals <- function(data_conc, data_dose, data_intervals, sparse,
     # No intervals; potentially placebo data
     return(rlang::warning_cnd(class="pknca_no_intervals", message="No intervals for data"))
   }
-  ret <- data.frame()
+  # Hoist the debug check: options is already the fully-merged options object
+  # (merged at the top of pk.nca()), so there is no need to re-query
+  # PKNCA.options() from the environment on every iteration.
+  use_debug <- !is.null(options$debug)
+  ret_list <- list()
   for (i in seq_len(nrow(data_intervals))) {
     current_interval <- data_intervals[i, , drop=FALSE]
     has_calc_sparse_dense <- any_sparse_dense_in_interval(current_interval, sparse=sparse)
@@ -279,7 +283,7 @@ pk.nca.intervals <- function(data_conc, data_dose, data_intervals, sparse,
         stop("Cannot both include and exclude half-life points for the same interval")
       }
       # Try the calculation
-      if (!is.null(PKNCA.options()$debug)) {
+      if (use_debug) {
         # debugging mode does not need coverage
         calculated_interval <- do.call(pk.nca.interval, args) # nocov
       } else {
@@ -304,10 +308,10 @@ pk.nca.intervals <- function(data_conc, data_dose, data_intervals, sparse,
           calculated_interval,
           row.names=NULL
         )
-      ret <- rbind(ret, new_ret)
+      ret_list[[length(ret_list) + 1L]] <- new_ret
     }
   }
-  ret
+  if (length(ret_list) == 0L) data.frame() else dplyr::bind_rows(ret_list)
 }
 
 #' Compute all PK parameters for a single concentration-time data set

cran-comments.md

@@ -0,0 +1,5 @@
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* Fixed URI error noted in prior submission

vignettes/v01-introduction-and-usage.Rmd

@@ -408,7 +408,7 @@ minimum-required number of calculations for the data that are changed.
 To use `update()`, give it your existing results and the new `PKNCAdata` object
 you want to use.
 
-Starting from the theophylline example in the [Quick Start](Quick Start) section above:
+Starting from the theophylline example in the [Quick Start](#quick-start) section above:
 
 ```{r update-example-setup}
 d_conc <- as.data.frame(datasets::Theoph)