fix(compile): keep loadDLL from unloading live shared objects

dyn.unload() nulls every native symbol pointer resolved so far, and nothing
resolves them again, so a second loadDLL() killed the objective it was meant
to restore. loadDLL() now skips what is already loaded. The batch handle
cached by Xs() holds a pointer of its own that no symbol-cache flush reached;
it keys on the flush count now.

The README example profiles and reports confidence intervals on the data
column; regenerated output and figures come with it.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Lkpck7VrrYcCWPYWiFXQTS

Author: simonbeyer1

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: dMod2
 Type: Package
 Title: Dynamic Modeling and Parameter Estimation in ODE Models
-Version: 0.6.0
-Date: 2026-07-18
+Version: 0.6.1
+Date: 2026-08-31
 Authors@R: c(
     person("Simon", "Beyer", email = "simon.beyer@fdm.uni-freiburg.de",
            role = c("aut", "cre")),

NEWS.md

@@ -1,3 +1,13 @@
+# dMod2 0.6.1
+
+* `loadDLL()` skips shared objects that are already loaded in the current
+  process. Unloading them nulled the native symbol pointers held by the
+  prediction, observation and parameter functions built from them, leaving
+  every later call without a way to resolve them again.
+* `Xs()` caches a prepared batch handle that carries such a pointer of its
+  own, which no symbol-cache flush reached. It is now rebuilt whenever the
+  cache is flushed.
+
 # dMod2 0.6.0
 
 * The core is complete: equations, compiled prediction, observation and

R/compile.R

@@ -100,10 +100,19 @@
   hdr
 }
 
+## Counts the symbol-cache flushes of this session. Callers that hold a
+## resolved address of their own key on it, since resolving again per call
+## costs more than the check is worth.
+.dmodSymbols <- new.env(parent = emptyenv())
+.dmodSymbols$generation <- 0L
+
+.symbolGeneration <- function() .dmodSymbols$generation
+
 ## cppDE caches the addresses of a model's compiled entry points, so loading a
 ## shared object invalidates them. Guarded, to stay loadable against a cppDE
 ## that predates the cache.
 .clearSymbols <- function() {
+  .dmodSymbols$generation <- .dmodSymbols$generation + 1L
   f <- get0("clearNativeSymbols", envir = asNamespace("cppDE"), inherits = FALSE)
   if (is.function(f)) f()
 }
@@ -677,30 +686,47 @@ getLocalDLLs <- function() {
 
 
 
+## Absolute paths of the shared objects loaded in this process.
+.loadedDLLPaths <- function() {
+  dlls <- getLoadedDLLs()
+  if (!length(dlls)) return(character(0))
+  paths <- vapply(dlls, function(d) unclass(d)$path, character(1))
+  normalizePath(paths, winslash = "/", mustWork = FALSE)
+}
+
+
 #' Load shared object for a dMod object
-#' 
+#'
 #' Usually when restarting the R session, although all objects are saved in
 #' the workspace, the dynamic libraries are not linked any more. `loadDLL`
 #' is a wrapper for `dyn.load` that uses the "modelname" attribute of
 #' dMod objects like prediction functions, observation functions, etc. to
 #' load the corresponding shared object.
-#' 
+#'
+#' Shared objects already loaded in the current process are skipped, so
+#' calling `loadDLL` repeatedly is a no-op. Unloading them would null the
+#' native symbol pointers that the already built prediction, observation and
+#' parameter functions hold, and nothing resolves those again.
+#'
 #' @param ... objects of class prdfn, obsfn, parfn, objfn, ...
-#' 
+#'
+#' @return Invisibly, the character vector of files loaded by this call.
+#'
 #' @export
 loadDLL <- function(...) {
-  
+
   .so <- .Platform$dynlib.ext
   models <- modelname(...)
   files <- paste0(outer(models, c("", "_s", "_s2", "_sdcv", "_deriv", "_dfdx", "_dfdp"), paste0), .so)
   files <- files[file.exists(files)]
-  
-  for (f in files) {
-    try(dyn.unload(f), silent = TRUE)
-    dyn.load(f)
-  }
+  files <- files[!normalizePath(files, winslash = "/", mustWork = FALSE) %in%
+                   .loadedDLLPaths()]
+  if (!length(files)) return(invisible(character(0)))
+
+  for (f in files) dyn.load(f)
   .clearSymbols()
   message("The following local files were dynamically loaded: ", paste(files, collapse = ", "))
+  invisible(files)
 }
 
 

R/prediction.R

@@ -352,11 +352,14 @@ Xs.cppDE <- function(odemodel, forcings = NULL, events = NULL, names = NULL, con
              fixed = NULL, forcings = controls$forcings), o)))
     } else if (!is.null(prepFn) && !is.null(solveFn)) {
       # The handle bakes in everything but the numbers, so it is only valid
-      # while shapes and labels stay put.
+      # while shapes and labels stay put. It also holds the resolved native
+      # symbol, which unloading the shared object nulls in place, so the
+      # symbol generation is part of the signature.
       sig <- list(model = as.character(model), times = timesL,
                   deriv = deriv, deriv2 = deriv2,
                   sens = lapply(preps, function(pr) dimnames(pr$sens1ini)),
-                  forcings = controls$forcings, opts = obatch)
+                  forcings = controls$forcings, opts = obatch,
+                  symbols = .symbolGeneration())
       if (!identical(bcache$sig, sig)) {
         bcache$handle <- do.call(prepFn, c(list(model, conditions = mkConds()),
                                            obatch))

README.Rmd

@@ -238,15 +238,15 @@ thr_90 <- profileThreshold(0.90, method = "F", n = nd, p = remlfit$rank)
 profiles <- profile(
   obj, best, names(best), limits = c(-5, 5), cores = length(best), delta = thr_95,
   stepControl = list(stepsize = 1e-4, min = 1e-4, max = Inf,
-                     atol = 1e-3, rtol = 1e-3, limit = 200, stop = "value"),
+                     atol = 1e-3, rtol = 1e-3, limit = 200, stop = "data"),
   algoControl = list(reoptimize = TRUE),
   optControl  = list(rinit = 0.1, rmax = 10, iterlim = 20))
 
 plotProfile(profiles, mode %in% c("data", "prior"), threshold = c("90%" = thr_90, "95%" = thr_95))
 ```
 
 ```{r confint}
-confint(profiles, level = 0.95, val.column = "value",
+confint(profiles, level = 0.95, val.column = "data",
         method = "F", n = nd, p = remlfit$rank)
 ```
 

README.md

@@ -259,15 +259,15 @@ rbind(ML = best[remlfit$errpars], REML = remlfit$argument[remlfit$errpars])
 ```
 
     ##      sd_pSTAT5A_rel sd_pSTAT5B_rel sd_rSTAT5A_rel
-    ## ML        0.5878611      0.8200770      0.4987482
-    ## REML      0.6253905      0.8442257      0.5248830
+    ## ML        0.5876483      0.8200724      0.4987661
+    ## REML      0.6254742      0.8442083      0.5248422
 
 ``` r
 remlfit$dof     # n_g minus the leverage each observable spends
 ```
 
     ## pSTAT5A_rel pSTAT5B_rel rSTAT5A_rel 
-    ##    13.67911    14.22154    14.09935
+    ##    13.67813    14.22147    14.10040
 
 ``` r
 remlfit$rank    # effective number of mean parameters
@@ -284,7 +284,7 @@ tapply(lev$leverage, lev$name, sum)
 ```
 
     ## pSTAT5A_rel pSTAT5B_rel rSTAT5A_rel 
-    ##    2.370236    1.743215    1.886549
+    ##    2.372391    1.742590    1.885019
 
 ### Fit and uncertainty band
 
@@ -349,7 +349,7 @@ thr_90 <- profileThreshold(0.90, method = "F", n = nd, p = remlfit$rank)
 profiles <- profile(
   obj, best, names(best), limits = c(-5, 5), cores = length(best), delta = thr_95,
   stepControl = list(stepsize = 1e-4, min = 1e-4, max = Inf,
-                     atol = 1e-3, rtol = 1e-3, limit = 200, stop = "value"),
+                     atol = 1e-3, rtol = 1e-3, limit = 200, stop = "data"),
   algoControl = list(reoptimize = TRUE),
   optControl  = list(rinit = 0.1, rmax = 10, iterlim = 20))
 
@@ -359,20 +359,20 @@ plotProfile(profiles, mode %in% c("data", "prior"), threshold = c("90%" = thr_90
 <img src="man/figures/README-profiles-1.svg" alt="" width="100%" />
 
 ``` r
-confint(profiles, level = 0.95, val.column = "value",
+confint(profiles, level = 0.95, val.column = "data",
         method = "F", n = nd, p = remlfit$rank)
 ```
 
-    ##                                      name      value        lower      upper
-    ## Epo_degradation_BaF3 Epo_degradation_BaF3 -1.5678382  -1.73695985 -1.3902979
-    ## k_exp_hetero                 k_exp_hetero -4.4194494 -10.34823306 -2.8946270
-    ## k_exp_homo                     k_exp_homo -2.2024766  -2.55028643 -1.8657436
-    ## k_imp_hetero                 k_imp_hetero -1.7874266  -1.91294412 -1.6603002
-    ## k_imp_homo                     k_imp_homo  1.4794785   0.07366071  8.1411569
-    ## k_phos                             k_phos  4.1977304   4.10556883  4.2980496
-    ## sd_pSTAT5A_rel             sd_pSTAT5A_rel  0.5878611   0.41152546  0.7985545
-    ## sd_pSTAT5B_rel             sd_pSTAT5B_rel  0.8200770   0.66585246  1.0147002
-    ## sd_rSTAT5A_rel             sd_rSTAT5A_rel  0.4987482   0.34832821  0.6924861
+    ##                                      name      value      lower      upper
+    ## Epo_degradation_BaF3 Epo_degradation_BaF3 -1.5680325 -1.7398858 -1.3903480
+    ## k_exp_hetero                 k_exp_hetero -4.4562477       -Inf -2.9546299
+    ## k_exp_homo                     k_exp_homo -2.2032225 -2.5569151 -1.8895645
+    ## k_imp_hetero                 k_imp_hetero -1.7872354 -1.9108026 -1.6583018
+    ## k_imp_homo                     k_imp_homo  1.4788926  0.1002196        Inf
+    ## k_phos                             k_phos  4.1976370  4.1064208  4.2964734
+    ## sd_pSTAT5A_rel             sd_pSTAT5A_rel  0.5876483  0.4098707  0.7938722
+    ## sd_pSTAT5B_rel             sd_pSTAT5B_rel  0.8200724  0.6648408  1.0126894
+    ## sd_rSTAT5A_rel             sd_rSTAT5A_rel  0.4987661  0.3488114  0.6910599
 
 The plot separates the two terms, and that is where the two
 non-identifiable directions become visible: for `k_imp_homo` and

dMod2.Rproj

@@ -1,10 +1,17 @@
 Version: 1.0
 ProjectId: dmod2
+
 RestoreWorkspace: No
 SaveWorkspace: No
+AlwaysSaveHistory: Default
+
 EnableCodeIndexing: Yes
 UseSpacesForTab: Yes
 NumSpacesForTab: 2
 Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
 BuildType: Package
 PackageUseDevtools: Yes