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Changelog

pharmr.extra (development version)

  • run_nlme() SAEM fits of nlmixr2 models no longer fail when the model was modified by a pharmpy operation after create_model() (e.g. create_model(...) |> set_initial_estimates(...)). Such operations return a new pharmpy object that drops the SAEM-safe nlmixr_code attribute cached by create_model(); run_nlme_nlmixr() now reapplies the residual-alias rewrite to the fallback model$code so SAEM accepts the residual error formula.

  • create_model(tool = "nlmixr2") no longer aborts with ValueError: datainfo.path is None when data is supplied as a data.frame. clean_modelfit_data() was calling pharmr::load_dataset() after set_dataset(); the reload was redundant (the dataset is already attached) and failed for nlmixr2 models, whose datainfo has no on-disk path.

  • create_model() now writes a data.frame data argument to a CSV in the session tempdir and points the model’s $DATA record at that file, instead of leaving pharmpy’s DUMMYPATH placeholder. This gives the model an on-disk dataset, which is required to use the run_nlme(copy_dataset = FALSE) workflow (NONMEM models only; filename input already has an on-disk dataset).

  • copy_dataset = FALSE can only be honored when the dataset is a file on disk (supplied via data or referenced by the model’s $DATA record). When only an in-memory dataset is available (a passed data frame, model$dataset, or the original dataset), a warning is issued and the dataset is copied into the run folder (with $DATA rewritten) as a fallback.

  • copy_dataset = FALSE now leaves the model’s $DATA record untouched instead of rewriting it to the dataset’s absolute path. Combined with not copying the dataset into the run folder, the model’s original data reference is preserved verbatim. ($DATA is still rewritten when copy_dataset = TRUE, i.e. when the dataset is placed into the run folder as data.csv.)

  • run_nlme(data = NULL, copy_dataset = FALSE) now correctly leaves the dataset in its existing location when the model’s $DATA record points to a real file. Previously the dataset was always copied into the run folder because run_nlme() materialised model$dataset into a tempfile before reaching prepare_run_folder(), and prepare_run_folder() preferred the in-memory dataset over the on-disk $DATA path.

  • update_parameters() now also accepts a raw nlmixr2FitCore / nlmixr2FitData object — useful when fitting outside run_nlme(). Both diagonal and off-diagonal omega elements are extracted and named per pharmpy’s IIV_X / IIV_X_IIV_Y convention, so block-omega covariances are now updated alongside variance terms (previously dropped).

  • run_nlme(), run_sim(), and create_vpc_data() now dispatch on the model engine. Pharmpy nlmixr-format models are routed through nlmixr2 / rxode2 directly (no pharmpy pyreadr dependency). NONMEM models still use the existing nmfe / PsN / pharmpy paths unchanged.

  • New control argument on run_nlme() is forwarded to nlmixr2::nlmixr2() (e.g. foceiControl()); ignored for NONMEM.

  • validate_model() now accepts nlmixr-format pharmpy models.

  • get_advan() / get_obs_compartment() return gracefully for non-NONMEM models instead of erroring on the missing control stream.

  • add_sir() now warns and no-ops for non-NONMEM models instead of silently doing nothing or aborting deep inside the covariance-record reader.

  • call_pharmpy_tool() now forwards esttool = "nlmixr" to pharmpy when a search tool (modelsearch, covsearch, iivsearch, ruvsearch, amd, bootstrap) is invoked with an nlmixr-format model. Pharmpy can drive these searches against nlmixr2 if the Python package pyreadr is installed and the system Rscript that pharmpy spawns has a working nlmixr2 / data.table install.

  • compare_nlme_runs() now detects the engine per run folder and loads nlmixr2 fits from the saved <id>.rds next to the run directory, so compare_nlme_runs() works for nlmixr2 runs too.

  • create_modelfit_info_table() is robust to fit objects reloaded from RDS whose pharmpy model attribute is no longer a live Python reference.

  • create_vpc_data() on an nlmixr fit now uses the fitted model’s parameter estimates (was silently falling back to the pre-fit initial estimates when only a fit was supplied), and restricts obs to observation rows so it matches the simulation row set.

  • run_nlme_nlmixr() now stashes an explicit data argument on the model so saved fits sim/VPC against the right dataset (previously the saved model still referenced the original model$dataset).

  • Initial CRAN submission.