Changelog • deepSTRAPP

deepSTRAPP 1.1.1

convert_BSM_to_simmap() and convert_BSMs_to_simmaps() now repair paths to phylo and tip ranges objects used in the example eel_biogeo_data dataset.

CRAN release: 2026-09-28

Handle uncertainty in trait estimates. See the ‘uncertainty_strategy’ argument in run_deepSTRAPP_*() functions.
Add functions to load results from external BAMM analyses. See the dedicated vignette/tutorial “import_external_analyses”.

STRAPP tests can now be run across a posterior sample of trait histories rather than a single reconstruction. The uncertainty_strategy argument of run_deepSTRAPP_for_focal_time(), run_deepSTRAPP_over_time() and compute_STRAPP_test_for_focal_time() selects how trait and rate uncertainty are combined: "rates_only" (BAMM posterior samples with a single trait reconstruction), "paired" (each stochastic map paired with one BAMM sample), or "full" (all stochastic maps crossed with all BAMM samples).
Stochastic maps can be supplied directly through the new contMaps and simmaps arguments, or simulated from posterior probabilities with nb_simulations.
trait_maps_vs_BAMM_samples_list records, and lets you impose, which stochastic map is paired with which BAMM sample, so that the same pairing is used at every time step of a trajectory.
See the new vignette handle_uncertainty.

New functions to bring analyses run outside deepSTRAPP into the workflow: build_BAMM_object(), subset_BAMM_object() and prune_BAMM_object() for BAMM output; convert_BSM_to_simmap() and convert_BSMs_to_simmaps() for BioGeoBEARS biogeographic stochastic maps; convert_contsimmap_to_contMaps() for contsimmap output; and convert_simmaps_to_densityMaps() to summarise stochastic maps as posterior densities.
aggregate_contMaps() summarises a list of continuous stochastic maps into a single mean or median contMap.
See the new vignette import_external_analyses.

run_deepSTRAPP_over_time() now selects the statistical method once, before any test is run, from all states/ranges described in the complete trait mapping, and applies it at every time step. The method previously depended on the states/ranges still present at each time step, so a trajectory could mix Kruskal-Wallis and Mann-Whitney U p-values on a single curve when a state/range was absent from the deeper time steps. This is not the case anymore, and all p-values across time-steps are prodcued by the same type of test.
The states/ranges actually observed are now reported: $states_observed and $nb_states_observed per time step, and $states_observed_overall, $states_observed_per_time_steps and $nb_states_observed_per_time_steps in the output of run_deepSTRAPP_over_time().

New extract_all_trait_values_for_focal_time() to extract trait data from every stochastic map at a given time, and extract_trait_data_melted_df_for_focal_time() to return it in long dataframe format.
New cut_contMaps_for_focal_time(), cut_simmap_for_focal_time() and cut_simmaps_for_focal_time() to cut lists of stochastic maps at a focal time.
run_deepSTRAPP_for_focal_time() and run_deepSTRAPP_over_time() gain return_updated_Maps to return the mappings cut at each focal time, and run_deepSTRAPP_for_focal_time() gains extract_trait_data_melted_df to return the underlying trait data in long dataframe format.

CRAN release: 2026-01-19