Changelog

Change the type of the flag_codes parameter of object_model_data() function from integer to character (three-letter FAO code(s) for the country(ies)).

Add data source observe_database.
Add weight category + 60kg (code 14).
Add functionality for querying multiple databases, for example the main and acquisition observe databases to simultaneously import and process ‘recent’ data from acquisition database, not yet imported into the main database, and older data from the main database.
Add activity code referential to allocate time at sea and fishing time and set duration.

Update vignettes.
Update unit tests.
Use of codama for checking arguments.
Update referential tables : set_duration_ref, length_step and length_weight_relationship.

Implementation of unit tests
Implementation of the documentation for the level 3
Development of output extraction functions for process 1.1
Adding an output directory initialization function and integrating it into the processes

Implementation for the import of annual dataset from outputs of the level 1 and 2 (period length customizable)
Implementation of bootstrap method as base for all confidence intervals
Computation of confidence interval (bootstrap interval) for the nominal catches by species and by fishing mode
Implementation of outputs for the he nominal catches by species and by fishing mode
Fix minor bugs in all different processes (P1, P2, P3)

Improve modelling configuration (addition of new parameters)
Rewrite several figures for the model checking
Improve time computing using faster implementation of random forests for the species composition modeling in the level3

Disabling task and effort computation while waiting for confident interval implementation
Remove the documentation from the package. Stay available at https://ob7-ird.github.io/t3/index.html

Implentation of the model layer in R6class
Implementation of all logbooks standardization sub-processes
Implementation of all samples standardization sub-processes
Implementation of sub-processes for assessing species composition in relation to non-sampled assemblages
Strict selection of samples used in the model training according to several criteria on sets and wells (school type, location, date, sample quality).
Classification of unknown school type (no data available) by clustering according to major tuna species composition.
Development of a statistical model accounting for:
- Spatial and temporal structure of the fishery (location and date of the catch)
- Vessel specificity which impacts the species composition of the catch
- Crew reporting information which enables more flexibility in the model when a marginal composition is caught
- Several years of fishing in the training to increase the robustness of the estimates
Model checking to ensure reliability of the results
Computation of a confidence interval (bootstrap method) which give the trust level on catch estimations

t3 2.0.1 - 2025-01-21