Package: cpueTargeting 0.4.0

Henning Winker

cpueTargeting: Simulators and Tools for CPUE Targeting and Standardization

Multispecies operating models for exploring spatial and vertical targeting and catch-per-unit-effort standardization. Includes the multi-habitat cluster example of Winker et al. (2014), the four-species bathymetric Run 22 model based on the spatial simulator of Thorson et al. (2017) <doi:10.1139/cjfas-2015-0598> and Ono et al. (2015) <doi:10.1016/j.fishres.2015.05.021>, and its extension to a longline tuna fishery with species vertical distributions and hook depth by hooks-between-floats, following the habitat-based approach of Bigelow et al. (2002). Provides a step-by-step toolbox for real catch and effort data: data formatting, cumulative species curves, fleet and catch composition exploration, habitat-based subsetting (Stephens and MacCall 2004), and targeting covariates from the catch composition (principal components with the Optimal Coordinates test, and clustering into fishing tactics).

Authors:Henning Winker [aut, cre]

cpueTargeting_0.4.0.tar.gz
cpueTargeting_0.4.0.zip(r-4.7-any)cpueTargeting_0.4.0.zip(r-4.6-any)cpueTargeting_0.4.0.zip(r-4.5-any)
cpueTargeting_0.4.0.tgz(r-4.6-any)cpueTargeting_0.4.0.tgz(r-4.5-any)
cpueTargeting_0.4.0.tar.gz(r-4.7-any)cpueTargeting_0.4.0.tar.gz(r-4.6-any)
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cpueTargeting/json (API)

# Install 'cpueTargeting' in R:
install.packages('cpueTargeting', repos = c('https://henning-winker.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/henning-winker/cpuetargeting/issues

On CRAN:

Conda:

3.48 score 1 stars 10 scripts 64 exports 17 dependencies

Last updated from:39090fba78. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develWARNING213
source / vignettesOK331
linux-releaseWARNING202
macos-releaseWARNING199
macos-oldrelWARNING234
windows-develWARNING160
windows-releaseWARNING152
windows-oldrelWARNING167
wasm-releaseOK92

Exports:add_targetingas_set_databathymetric_settingsbind_comparisonscluster_tacticscomposition_pcacompute_occpue_datacpue_diagnosticscpue_indexcpue_matrixexport_indexfit_cpueinfluence_cpuelogbook_settingslogbook_truthnominal_cpueplot_availabilityplot_biplotplot_cluster_2014plot_clustersplot_compositionplot_cpue_mapplot_diagnosticsplot_effectsplot_fleetplot_indexplot_indicesplot_influenceplot_logbook_omplot_nominalplot_ocplot_omplot_pcsplot_replicatesplot_residualsplot_sectionsplot_species_curveplot_stepplot_stephens_maccallplot_targetingplot_thermoclineplot_thermocline_effectplot_tuna_vertical_distributionsplot_vessel_effectsplot_vessel_profileppc_cpueread_replicatesrun_replicatesselect_modelsselect_speciesselect_tacticsselect_vesselssimulate_bathymetricsimulate_clustersimulate_logbooksimulate_tunaspecies_curvestandardize_cpuestandardize_tuna_indicesstephens_maccalltactic_tabletuna_settingsvessel_summary

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Analysing real catch and effort data: a step-by-step guide
Example data | Step 1. Format the data | Step 2. Explore the fleet | Step 3. Choose the species set | Step 4. Nominal CPUE and catch composition | Step 5. Habitat-based subsetting (optional) | Step 6. Targeting covariates from the catch composition | How many PCs? | What do the PCs and clusters represent? | Removing irrelevant tactics (optional) | Step 7. Fit the standardisation models | Step 8. Diagnostics | Model comparison | Indices and steps | Influence | Residuals, effects and vessels | Predictive check of the error distribution | Step 9. The final index | Processing log | Reporting checklist | References
Rendered fromreal-data-guide.Rmdusingknitr::rmarkdown

Last update: 2026-10-14
Started: 2026-10-13

Setting up the logbook simulator: a user guide
1. Settings | 2. Environment | 3. Species and habitats | 4. Tactics | 5. Fleet: specialists, mixed vessels and loyalty | 6. Prices, closure and fleet changes | 7. Reporting: from sets to a logbook | 8. Basic mechanics: what the PCs and clusters capture | PCs against tactic and habitat | PCs against depth and the true position | Mixed trips lie between the clusters | Clusters against the true tactic | Targeting over time | Vessel profiles against the true vessel type | Things to try | References
Rendered fromlogbook-om-guide.Rmdusingknitr::rmarkdown

Last update: 2026-10-14
Started: 2026-10-14

Standardising a realistic logbook: two worked examples
The logbook | Part 1. Preparing the logbook | Step 1. Format the data | Step 2. Explore the fleet | Step 3. Choose the species set | Step 4. Nominal CPUE and catch composition | Step 6. Targeting covariates for the whole fleet | Part 2. Worked example: blackspot seabream | Step 5. Remove the trips that could not catch seabream | Step 6. Targeting covariates of the demersal trips | Step 7. Fit the models | Step 8. Diagnostics | Model comparison, indices and steps | Influence | Residuals | Predictive check of the error distribution | Effects and vessels | Sensitivity to the data selection | Step 9. The index | Part 3. Worked example: dolphinfish | Step 5. Keep the pelagic trips | Step 6. Targeting covariates of the pelagic trips | Influence and residuals | Predictive check by month and by year | Error distribution: Tweedie or delta-lognormal? | Sensitivity | Processing logs | Reporting checklist | Things to try | References
Rendered fromlogbook-example.Rmdusingknitr::rmarkdown

Last update: 2026-10-14
Started: 2026-10-13

Targeting along a continuous gradient: the bathymetric example
The simulated fishery | Standardisation as in the guidelines | What do the PCs capture? | What do the clusters capture? | Replicates | References
Rendered frombathymetric-example.Rmdusingknitr::rmarkdown

Last update: 2026-10-14
Started: 2026-10-13

Targeting in a longline tuna fishery: a step-by-step guide
The operating model in brief | How to control the scenario | Targeting through prices | Species habitat | Longline gear | Environment | Fleet and data | Step 1: define the scenarios | Step 2: simulate | Step 3: the environment | Step 4: why the thermocline matters for HBF | Step 5: how the fishery responds | Gear use | Vertical availability by HBF | Nominal CPUE versus true biomass | Step 6: standardise | Step 7: compare the indices with the truth | Indices | Accuracy | What to look for | Step 8: what do the principal components capture? | Replicates: is the ranking robust? | Optional: with or without space, and at what resolution | Optional: interactions with year | A note on habitat-based standardisation | References
Rendered fromtuna-targeting-scenarios.Rmdusingknitr::rmarkdown

Last update: 2026-10-14
Started: 2026-10-12

Tuna longline targeting: flat versus contrasting true trends
Settings | Nominal CPUE versus the truth | Standardised indices | What do the PCs capture? | What do the clusters capture? | Replicates: flat versus contrasting trends | What to take from this
Rendered fromtuna-flat-vs-contrast.Rmdusingknitr::rmarkdown

Last update: 2026-10-14
Started: 2026-10-13

Readme and manuals

Help Manual

Help pageTopics
Add targeting covariates: PC scores and fishing tacticsadd_targeting
Reshape set-by-species records (long) to one row per setas_set_data
Default settings for the four-species bathymetric scenario (Run 22)bathymetric_settings
Combine several standardisation results into onebind_comparisons
Cluster operations into fishing tacticscluster_tactics
Principal components of the catch compositioncomposition_pca
Optimal Coordinates test for the number of non-trivial componentscompute_oc
Prepare catch and effort data for analysiscpue_data
Compare fitted CPUE modelscpue_diagnostics
Standardised index tablecpue_index
CPUE of each operationcpue_matrix
Write the standardised index to a CSV fileexport_index
Standardise CPUE for one species with mgcvfit_cpue
Influence of each model term on the indexinfluence_cpue
Default settings for the logbook simulatorlogbook_settings
True relative biomass of one species from simulate_logbook()logbook_truth
Nominal CPUE by yearnominal_cpue
Vertical availability by HBF over timeplot_availability
PCA biplot of the catch compositionplot_biplot
Plot the 2014 cluster simulationplot_cluster_2014
What do the catch-composition clusters capture?plot_clusters
Plot the catch composition over timeplot_composition
Map of operations and CPUEplot_cpue_map
Compare the accuracy of standardised indicesplot_diagnostics
Partial effects of the covariatesplot_effects
Overview of the fleet and the recordsplot_fleet
Plot standardised indicesplot_index
Plot standardised indices against the true biomass trendplot_indices
Plot the influence of each model termplot_influence
Diagnostic plots of the logbook simulatorplot_logbook_om
Plot nominal CPUEplot_nominal
Plot the scree and the Optimal Coordinates testplot_oc
Diagnostic plots for an operating-model runplot_om
What do the principal components of the catch composition capture?plot_pcs
Accuracy of standardised indices across replicatesplot_replicates
Residual diagnosticsplot_residuals
Species distributions in three dimensions: sections and depth layersplot_sections
Plot the cumulative species curveplot_species_curve
Step plot of the standardisationplot_step
Diagnostic plots for Stephens and MacCall subsettingplot_stephens_maccall
What the targeting covariates captureplot_targeting
Plot the thermocline anomalyplot_thermocline
Show how a thermocline shift moves species relative to the hooksplot_thermocline_effect
Plot species vertical profiles and hook-depth profiles by HBFplot_tuna_vertical_distributions
Plot the random vessel effectsplot_vessel_effects
Catch profile of each vesselplot_vessel_profile
Predictive check of the error distributionppc_cpue
Read replicate results saved one file per seedread_replicates
Run scenarios over several seeds (replicates)run_replicates
The growth rate 'r' has one value per species and is applied by column. (The original code multiplied 'B' by 'r' directly, which recycles 'r' over cells rather than species.)schaefer_update
Keep only some models of a standardisation resultselect_models
Select the main speciesselect_species
Keep or drop fishing tactics and recompute the targeting covariatesselect_tactics
Select core (indicator) vesselsselect_vessels
Simulate the four-species bathymetric Run 22 scenariosimulate_bathymetric
Simulate the 2014 multi-habitat cluster scenariosimulate_cluster
Simulate a realistic multispecies logbooksimulate_logbook
Simulate a longline fishery for four tuna species with vertical targetingsimulate_tuna
Cumulative species curvespecies_curve
Standardise simulated CPUE with alternative targeting covariatesstandardize_cpue standardize_tuna_indices
Select operations likely to have fished the habitat of a target speciesstephens_maccall
Keep a subset of operationssubset.cpue_data
Species composition of each fishing tactictactic_table
Default settings for the longline tuna scenariotuna_settings
Summary of each vessel's activity and catches of target speciesvessel_summary