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t3
1.0.0
Reference
Articles
Model initialisation and data selection
Level 1 process
Level 2 process
Level 3 process
Running processes examples
Changelog
Changelog
Source:
NEWS.md
T3 1.0.0 - 2022-04-21
Added
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 intilisation function and integrating it into the processes
Changed
Fix minor bugs and optimisation of the code and regarding the overall process
Update of the level 3 process
T3 0.9.1 - 2020-09-21
Added
Implementation for the import of annual dataset from outputs of the level 1 and 2 (period length customizable)
Implementation of boostrap 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)
Changed
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
Removed
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
T3 0.9.0 - 2020-06-05
Added
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
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