Merge pull request #271 from billdenney/v0.11.0

Prepare for release of version 0.11.0
billdenney · Jun 19, 2024 · e5dede3 · e5dede3
2 parents 3ecb3fc + 27343a2
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diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 0.11.0
+Date: 2024-06-19 12:12:13 UTC
+SHA: d85d13034a8e067409c1566fe0edd7a84ed1455b
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: PKNCA
 Type: Package
 Title: Perform Pharmacokinetic Non-Compartmental Analysis
-Version: 0.10.2.9000
+Version: 0.11.0
 Authors@R: c(
     person("Bill", "Denney", email="[email protected]", role=c("aut", "cre"), comment=c(ORCID="0000-0002-5759-428X")),
     person("Clare", "Buckeridge", email="[email protected]", role="aut"),

diff --git a/NEWS.md b/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.
 
-# PKNCA development version
+# PKNCA 0.11.0
 
 * PKNCA will now indicate the number of observations included in a summary ("n")
   when it is not the same as the number of subjects included in the summary

diff --git a/R/intervals.R b/R/intervals.R
diff --git a/R/sparse.R b/R/sparse.R
@@ -65,7 +65,7 @@ sparse_pk_attribute <- function(sparse_pk, ...) {
 #'
 #' Where:
 #'
-#' \itemize{
+#' \describe{
 #'   \item{\eqn{w_i}{w_i}}{is the weight at time i}
 #'   \item{\eqn{\delta_{time,i-1,i}}{d_time[i-1,i]} and \eqn{\delta_{time,i,i+1}}{d_time[i,i+1]}}{are the changes between time i-1 and i or i and i+1 (zero outside of the time range)}
 #'   \item{\eqn{t_i}{t_i}}{is the time at time i}
@@ -89,7 +89,7 @@ sparse_auc_weight_linear <- function(sparse_pk) {
 #' Choices for the method of calculation (the argument `sparse_mean_method`)
 #' are:
 #'
-#' \itemize{
+#' \describe{
 #'   \item{"arithmetic mean"}{Arithmetic mean (ignoring number of BLQ samples)}
 #'   \item{"arithmetic mean, <=50% BLQ"}{If >= 50% of the measurements are BLQ, zero.  Otherwise, the arithmetic mean of all samples (including the BLQ as zero).}
 #' }
@@ -199,7 +199,7 @@ var_sparse_auc <- function(sparse_pk) {
 #' defined as zero (rather than dividing by zero).
 #'
 #' Where:
-#' \itemize{
+#' \describe{
 #'   \item{\eqn{\hat{\sigma}_{ij}}{sigma_ij}}{The covariance of times i and j}
 #'   \item{\eqn{r_i}{r_i} and \eqn{r_j}{r_j}}{The number of subjects (usually animals) at times i and j, respectively}
 #'   \item{\eqn{r_{ij}{r_ij}}}{The number of subjects (usually animals) at both times i and j}
@@ -292,7 +292,7 @@ sparse_to_dense_pk <- function(sparse_pk) {
 #'
 #' Where:
 #'
-#' \itemize{
+#' \describe{
 #'   \item{\eqn{AUC}{AUC}}{is the estimated area under the concentration-time curve}
 #'   \item{\eqn{w_i}{w_i}}{is the weight applied to the concentration at time i (related to the time which it affects, see [sparse_auc_weight_linear()])}
 #'   \item{\eqn{\bar{C}_i}{Cbar_i}}{is the average concentration at time i}

diff --git a/R/time_calc.R b/R/time_calc.R
@@ -4,7 +4,7 @@
 #' @param time_obs A vector of times for observations
 #' @param units Passed to `base::as.numeric.difftime()`
 #' @returns A data.frame with columns for:
-#' \itemize{
+#' \describe{
 #'   \item{event_number_before}{The index of `time_event` that is the last one before `time_obs` or `NA` if none are before.}
 #'   \item{event_number_after}{The index of `time_event` that is the first one after `time_obs` or `NA` if none are after.}
 #'   \item{time_before}{The minimum time that the current `time_obs` is before a `time_event`, 0 if at least one `time_obs == time_event`.}

diff --git a/inst/WORDLIST b/inst/WORDLIST
@@ -11,6 +11,7 @@ AUMClast
 Analyte
 BIP
 BLQ
+Beal's
 Biometrics
 Biopharmaceutical
 CDISC
@@ -25,6 +26,7 @@ DL
 DOI
 Dixit
 Durations
+Extrap
 Gabrielsson
 Hadley
 Hsuan
@@ -96,7 +98,6 @@ conc
 ctrough
 customizable
 dbplyr
-difftime
 doBy
 doi
 dosetype

diff --git a/man/cov_holder.Rd b/man/cov_holder.Rd
diff --git a/man/pk.calc.sparse_auc.Rd b/man/pk.calc.sparse_auc.Rd
diff --git a/man/sparse_auc_weight_linear.Rd b/man/sparse_auc_weight_linear.Rd
diff --git a/man/sparse_mean.Rd b/man/sparse_mean.Rd
diff --git a/man/time_calc.Rd b/man/time_calc.Rd
diff --git a/vignettes/v06-half-life-calculation-tobit.Rmd b/vignettes/v06-half-life-calculation-tobit.Rmd
@@ -88,7 +88,7 @@ The steps for Tobit regression are:
     2. The $\lambda_z$ value (slope for the half-life line) must be positive;
        in other words, the half-life slope must be decreasing.
 
-# Comparision of Tobit and semi-log regression
+# Comparison of Tobit and semi-log regression
 
 In almost all scenarios, Tobit regression using the algorithm above improves the
 half-life estimate compared to semi-log regression.  In the figure below,