Package: tidycreel 8.0.0.9000

What is tidycreel.connect? | Installation | How tidycreel.connect relates to tidycreel | Exported functions | Connection management | Data loading | Survey discovery (API connections only) | CSV back-end | More information

The map | Effort follows the survey type | The catch rate follows the interview type | Totals follow from the two | Seeing which estimator ran | Overriding the choice | References

Introduction | Scientific Rationale | Roving-Access Design Theory | Why Complete Trips Are Preferred | When Incomplete Trips Might Be Considered | Mean-of-Ratios vs. Ratio-of-Means | Recommended field protocol | Default Workflow: Complete Trips Only | Sample Size Requirements | Never pool complete and incomplete trips | Pooling and substitution are different | What NOT to Do | Package Design Prevents Auto-Pooling | What the default does on a roving design | Step-by-Step Validation Workflow | Step 1: Load Data with Both Trip Types | Step 2: Create Design and Attach Data | Step 3: Run TOST Equivalence Testing | Step 4: Interpret Results | Step 5: View Validation Plot | Step 6: Make Decision | Example: Validation Passes | Example: Validation Fails | Using Diagnostic Comparison Mode | When to Use Diagnostic Mode | Interpretation Guidance | Advanced: Grouped Validation | Technical Details | TOST Equivalence Testing | Equivalence Threshold Configuration | Trip Truncation | Mean-of-Ratios Variance | Summary and Recommendations | Decision Tree: Should I Use Incomplete Trips? | Default Recommendations | Function Reference | Further Reading

What this design is | Design and counts | Effort | Access interviews | Roving interviews | Results as data | Plot | References

What the rate is for | Two estimators, and what each one estimates | Access interviews: ratio of means | Roving interviews: mean of ratios | What tidycreel does | From rates to a total: stratum by stratum | The standard error of a product | The independence assumption | Which part of the error dominates | References

Zero is a measurement | Unknown is not zero | A stratum with no sampled days | A stratum with no catch rate | Other unknowns that are refused | NULL, NA and zero in a standard error | One exception: se_within with one count per day | Kinds of refusal | Why refuse rather than warn

Why there is no default unit | The count side | The interview side | How units combine | What to do with an NA unit | References

The sampling unit is the day | From a count to a day's effort | From sampled days to the stratum total | The standard error: variation between days | Why there is no finite-population correction here | One count per day: se_within is 0, and what that means | Several counts per day: two-stage variance | More days or more counts per day? | Progressive counts | Summary | References

Introduction | The season | Survey design | Counts | Effort | Interviews | Catch rates | Total catch and harvest | Using results as data | Plotting | Variance methods | Next steps

What all three estimate | Same season, three methods | When they can disagree | One sampled day in a stratum | References

Introduction | $v$ is the reciprocal of the published ratio | The correction is estimated, so it carries uncertainty | Example Data | Design Construction | Adding Count Data and Estimating Effort | Visibility Correction | Interview-Based Catch Estimation | Results as Data | Summary | References

What is a Bus-Route Survey? | Key Concepts | Inclusion Probability (πᵢ) | Enumeration Counts | Horvitz-Thompson Estimator | A Note on Existing Implementations | Step-by-Step Example | Step 1: Define the Sampling Design | Step 2: Attach Interview Data | Step 3: Estimate Effort | Step 4: Estimate Catch and Harvest | Step 5: Results as Data | Checking the Arithmetic | References

Overview | Example Data | Counter Mode | Build a Survey Calendar | Design Construction | Handling Informative Gaps | Effort Estimation | Ingress-Egress Mode | Preprocess Timestamps | Build the Design and Estimate Effort | Catch Estimation | Results as Data | Summary | References

When to Use the GLMM Estimator | Example Data | Building the Aerial Design | GLMM Effort Estimation | Variance and Confidence Intervals | Downstream Estimation | Comparison: Simple vs. GLMM Estimator | Estimating Within Strata | Results as Data | Custom Formula | References

Overview | Effort type distinction | Shelter mode stratification | Example Data | Design Construction | Attaching Interview Data | Effort Estimation | Shelter Mode Stratification | Catch Rate Estimation | Total Catch Estimation | Results as Data | References

Survey design terms | Creel survey | Design | Calendar | Stratum / strata | PSU (Primary Sampling Unit) | Survey type | Data layer terms | Count data | Interview data | Catch data | Length data | Section | Effort and catch terms | Effort | Angler-hours | Catch rate | Harvest rate | Release rate | CPUE / HPUE | Total catch / total harvest / total release | Trip-status and interview terms | Complete trip | Incomplete trip | Refusal | Species sought | Estimation terms | Design-based inference | Weighted estimate | Unextrapolated summary | Extrapolated estimate | Variance | Standard error (SE) | Confidence interval (CI) | Relative standard error (RSE) | Planning terms | Sample size planning | Power | Design comparison | Hybrid interviews | Hybrid design | Survey-type terms | Instantaneous count survey | Progressive count survey | Bus-route survey | Camera survey | Aerial survey | Where to go next

Introduction | Build the Design | Interview Participation | Interview Composition | Day Type | Angler Type | Fishing Method | Species Sought | Successful Parties | Trip Length Distribution | Catch-While-Sought (CWS) and Harvest-While-Sought (HWS) Rates | CWS Rates by Species Sought | CWS Rates Collapsed Across All Groupings | HWS Rates | Length Frequency Distributions | Harvest Lengths | Release Lengths | All-Catch Lengths | Extrapolated Species-Level Estimates | Catch Per Unit Effort by Species | Total Catch by Species | Total Harvest by Species | Release Rate and Total Releases by Species | Summary

Introduction | Survey Design and Count Data | Adding Interview Data | Estimating CPUE | Estimating Total Catch | Estimating Harvest | Grouped Estimation | Variance Methods | Age Structure Estimation | Complete Workflow Example | Working with Incomplete Trips | Next Steps

1 Inspect the design with plot_design() | Before attaching counts | After attaching counts | 2 Review the survey calendar with autoplot() | 3 Visualise estimates with autoplot() | Ungrouped effort estimate | Grouped effort estimate | CPUE estimate | Weighted Length Distributions | 4 Combining plots | 5 Customising Appearance with theme_creel() | Using the theme = "creel" argument | Manual Customisation | Summary

1 Pre-season sample-size planning with power_creel() | Required sampling days for total effort precision | Required interviews for CPUE precision | Power to detect a management-relevant CPUE change | 2 Comparing finished designs with compare_designs() | 3 Combining disjoint count frames with as_hybrid_svydesign() | Summary

Introduction | Building a Sectioned Design | Per-Section Effort Estimation | Per-Section Catch Rate | Per-Section and Lake-Wide Total Catch | Variance Aggregation — Why method = "correlated" Is the Default | Missing Section Warning

Introduction | Angler Population Size | Chapman estimator (default) | Petersen estimator | Schnabel estimator (multi-occasion) | Schumacher-Eschmeyer estimator (multi-occasion regression) | Total Harvest from Mark-Recapture | Exploitation Rate | Unstratified | Stratified (T-weighted) | Reporting rate adjustment | Choosing an Estimator | Assumptions | References | See Also

Introduction | Data Setup | Part 1 — Raw survey Package Workflow | 1a. Survey Design from a Stratified Calendar | 1b. Effort Estimation via svytotal | 1c. Catch Rate via svyratio and Total Catch via the Delta Method | Part 2 — tidycreel Equivalent | 2a. creel_design() + add_counts() | 2b. estimate_effort() | 2c. estimate_catch_rate() + add_interviews() | 2d. estimate_total_catch() | Mapping Table | When to Use Each Approach

Overview | 1. Inclusion Probability (πᵢ) | 2. Enumeration Expansion | 3. Effort Estimator (Jones & Pollock Eq. 19.4) | 4. Expanded Effort per Interview (eᵢ) | 5. Harvest Estimator (Jones & Pollock Eq. 19.5) | 5a. Harvest Rate (HPUE) as a Ratio of HT Totals | 5b. Incomplete trips: truncated mean of ratios | 6. Variance Estimation | 7. Summary Traceability Table | 8. Why πᵢ Matters: A Quantitative Example | References

Overview | Instantaneous vs. Progressive Counts | When to Use Progressive Counts | Conditions for an Unbiased Estimate | Data Requirements | A Complete Example | Survey Setup | Scheduling Circuit Start Times | Count Data | Attaching Progressive Counts | Effort Estimation | Adding Interviews | Catch Rate and Total Catch | Harvest Rate and Total Harvest | Pope et al. Worked Example | Multiple Periods per Day | Assemble a Summary Report | References

Overview | How Design-Based Extrapolation Works | Building a Season-Long Dataset | Season-Total Effort | Monthly Effort Totals | Season Total from Monthly Estimates | Monthly Catch Totals | Season-Total Catch | Assembling a Summary Report | Annual Totals Across Multiple Seasons | Exporting Results | Practical reminders | References

Overview | The Count Period Framework | Building a Basic Schedule | Visualising Sampled vs. Unsampled Days | Scheduling High-Use or Other Special Periods | Special Strata vs. Post-Stratification | Three-Period Schedules | Reproducibility with seed | Saving and Sharing the Schedule | Linking the Schedule to a Design | Count Period Scheduling vs. Interview Scheduling | Bus-Route Scheduling | Sampling Rate Guidance | Within-Day Count Time Scheduling | Random Strategy | Systematic Strategy | Fixed Strategy | Progressive Count Strategy | Exporting Count Time Schedules | Combining the Daily Schedule with Count Time Windows | Validating the Design Before the Season | Checking Data Completeness After the Season | Assembling the Season Summary | References