1.12.0

Release Notes

New features and changes

• New model tree feature: bbr now allows you to visualize any run_log() as
  an interactive model tree diagram via model_tree(). This function allows
  users to easily visualize and track the modeling process for a given project
  and display any information available in a run_log(), such as model summary
  information, configuration options, and more. See the vignette here (#673).

• New run_nmtran() function for running NM-TRAN on a model object to validate
  its control stream for correct coding before submission. The NM-TRAN dataset
  (FDATA) and other NONMEM artifacts can be further inspected by keeping the
  run directory around (#705).

• nm_data() now has a filter argument, allowing the user to filter data based
  on IGNORE LIST or ACCEPT LIST options defined in the $DATA record of a
  NONMEM control stream file (#711).

• setup_bootstrap_run() now has a data argument, allowing users to provide
  their own starting dataset to resample from. This defaults to NULL, which
  will use the output from nm_data(.boot_run, filter = TRUE) (#707, #711).

Bugs addressed

• Bootstrap adjustment: previously setup_bootstrap_run() assumed a NUM column
  was present in the input dataset (the default .join_col in nm_join()).
  nm_join() was being used in the back-end to create the starting dataset to
  resample from; the intention being to only include subjects that entered the
  original problem. Given the nm_join() use, this also required that the original
  model had finished executing before bootstrapping. Rather than using nm_join()
  by default, we now use nm_data(.boot_run, filter = TRUE). This both fixes the
  aforementioned bug and removes the requirement that the base model (model being
  bootstrapped) has been executed. If the model has been executed, however, we will
  check the number of records to ensure the filtering was done correctly (#707, #711).