fixes requested by CRAN

Author: levenc

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: posologyr
 Title: Individual Dose Optimization using Population Pharmacokinetics
-Version: 1.2.0
+Version: 1.2.1
 Authors@R: c(
     person("Cyril","Leven",role = c("aut", "cre","cph"),
            email = "cyril.leven@chu-brest.fr",
@@ -12,8 +12,9 @@ Authors@R: c(
 Description: Determine individual pharmacokinetic (and 
     pharmacokinetic-pharmacodynamic) profiles and use them to personalise drug 
     regimens. You provide the data and a population pharmacokinetic model, 
-    `posologyr` provides the individual a posteriori estimate and allows you to 
-    determine the optimal dosing.
+    'posologyr' provides the individual a posteriori estimate and allows you to 
+    determine the optimal dosing. The empirical Bayes estimates are computed as 
+    described in Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.
 License: AGPL-3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

NEWS.md

@@ -1,3 +1,8 @@
+# posologyr v1.2.1
+* Add a reference to Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97> in the DESCRIPTION (as requested by CRAN)
+* Fix messages to the console in the internal function `posologyr()` (as requested by CRAN)
+* Fix assignment to parent environment in dose optim functions, using `parent.frame()` (as requested by CRAN)
+
 # posologyr v1.2.0
 
 ## Additional features

R/dosing_optim.R

@@ -253,7 +253,7 @@ poso_time_cmin <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
                          cmin_ppk_model[,endpoint] < target_cmin,
                          ]$time) - time_last_dose
     #here cmin_distribution is a point estimate of the endpoint
-    cmin_distribution <<-
+    cmin_distribution <-
       cmin_ppk_model[cmin_ppk_model$time == (time_to_target +
                                                   time_last_dose),endpoint]
   }
@@ -386,6 +386,9 @@ poso_dose_auc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
 
   object <- posologyr(prior_model,dat,nocb)
 
+  #initialization of conc_distribution to avoid a global variable
+  auc_distribution <- 0
+
   if(tdm){ #using TDM data
     #input validation
     if (is.null(time_dose)){
@@ -448,13 +451,12 @@ poso_dose_auc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
                                            ifelse(nocb,"nocb","locf"))
 
       auc_proposal     <- max(auc_ppk_model$AUC)-min(auc_ppk_model$AUC)
-      auc_distribution <<- auc_proposal #to parent environment
+      p <- parent.frame()
+      p$auc_distribution <- auc_proposal
       #return the difference between the computed auc and the target
       delta_auc <- (target_auc - auc_proposal)^2
       return(delta_auc)
     }
-    #initialization of conc_distribution to avoid a global variable
-    auc_distribution <- 0
 
     optim_dose_auc <- stats::optim(starting_dose,err_dose_tdm,
                                     time_dose=time_dose,target_auc=target_auc,
@@ -525,7 +527,8 @@ poso_dose_auc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
         auc_index      <- ceiling(p * n_auc)
 
         # assign the distribution of auc to the parent environment
-        auc_distribution <<- sorted_auc
+        p <- parent.frame()
+        p$auc_distribution <- sorted_auc
 
         if (greater_than){
           auc_proposal <- sorted_auc[n_auc - auc_index]
@@ -534,7 +537,8 @@ poso_dose_auc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
         }
       } else {
         auc_proposal  <- max(auc_ppk_model$AUC)-min(auc_ppk_model$AUC)
-        auc_distribution <<- auc_proposal
+        p <- parent.frame()
+        p$auc_distribution <- auc_proposal
       }
 
       #return the difference between the computed AUC and the target
@@ -739,7 +743,8 @@ poso_dose_conc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
                                            ifelse(nocb,"nocb","locf"))
 
       conc_proposal     <- ctime_ppk_model[,endpoint]
-      conc_distribution <<- conc_proposal #to parent environment
+      p <- parent.frame()
+      p$conc_distribution <- conc_proposal
       #return the difference between the computed ctime and the target
       delta_conc <- (target_conc - conc_proposal)^2
       return(delta_conc)
@@ -803,7 +808,8 @@ poso_dose_conc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
         conc_index      <- ceiling(p * n_conc)
 
         # assign the distribution of concentrations to the parent environment
-        conc_distribution <<- sorted_conc
+        p <- parent.frame()
+        p$conc_distribution <- sorted_conc
 
         if (greater_than){
           conc_proposal <- sorted_conc[n_conc - conc_index]
@@ -812,7 +818,8 @@ poso_dose_conc <- function(dat=NULL,prior_model=NULL,tdm=FALSE,
         }
       } else {
         conc_proposal     <- ctime_ppk_model[,endpoint]
-        conc_distribution <<- conc_proposal
+        p <- parent.frame()
+        p$conc_distribution <- conc_proposal
       }
 
       #return the difference between the computed ctime and the target
@@ -979,7 +986,8 @@ poso_inter_cmin <- function(dat=NULL,prior_model=NULL,dose,target_cmin,
       cmin_index      <- ceiling(p * n_cmin)
 
       # assign the distribution of cmin to the parent environment
-      cmin_distribution <<- sorted_cmin
+      p <- parent.frame()
+      p$cmin_distribution <- sorted_cmin
 
       if (greater_than){
         cmin_proposal <- sorted_cmin[n_cmin - cmin_index]
@@ -988,7 +996,8 @@ poso_inter_cmin <- function(dat=NULL,prior_model=NULL,dose,target_cmin,
       }
     } else {
       cmin_proposal     <-  cmin_ppk_model[,endpoint]
-      cmin_distribution <<- cmin_proposal
+      p <- parent.frame()
+      p$cmin_distribution <- cmin_proposal
     }
 
     #return the difference between the computed cmin and the target,

R/posologyr.R

@@ -72,11 +72,11 @@ posologyr <- function(prior_model=NULL,dat=NULL,nocb=FALSE){
   # check the validity of the compiled model and call rxode2::rxode
   # on invalid models
     if (!prior_model$ppk_model$isValid()){
-      cat("Invalid rxode2 model, trying to recompile...")
+      warning("Invalid rxode2 model, trying to recompile...")
       prior_model$ppk_model <- try(rxode2::rxode(prior_model$ppk_model),
                                    silent=TRUE)
       if (prior_model$ppk_model$isValid()){
-        cat("Success","\n")
+        message("Success","\n")
        } else {
          stop("Failed. The rxode2 model is still invalid. Aborting",
               call. = FALSE)