Package: Rceattle 5.66.1

1. Model dimensions and operating mode | msmMode, single-species vs. multi-species | initMode, initial age structure | The initial equilibrium catch | estimateMode, what does fit_mod() actually do? | Random-effects toggles | 2. Predation: suitMode (per predator) | 3. Selectivity (per fleet) | Sharing a selectivity between fleets | Sex structure and relative selectivity | Which forms can hold a sex difference in level | 4. Catchability (per fleet) | Index likelihood (Index_distribution) | Which fleets get a catchability | How an index is timed against mortality | A snapshot-timed index on a fishery's selectivity | 5. Composition likelihoods | Ageing error, per fleet | 6. Recruitment / stock-recruit (recFun = build_srr()) | 7. Natural mortality (M1Fun = build_M1()) | M1_model, fixed-effects structure of M1 | M1_re, random-effects structure on M1 | 8. Growth and weight-at-age (growthFun = build_growth()) | growth_model, fixed-effects structure of growth | Growth variability, plus group, length bins and maturity | 9. Harvest control rules (HCR = build_hcr()) | 10. Projection and MSE | 11. Diagnostics and inference | 12. Plotting | References

Setup and plotting data | S3 Methods | Convergence diagnostics | Naming the parameter, not just the block | Fit plots | Composition data | Survey indices | Catch | One-step-ahead (OSA) residuals | Choosing the one-step-ahead method | With random effects, the choice reverses for the aggregate series | Fleets with tail accumulation | Process residuals | Retrospective analysis | Jitter testing | Self-test (simulation–estimation) | Redrawing process error | Likelihood profile | Moving a set of cells together | Catchability | Where the data disagree | Comparing runs | Collecting it into report tables | Model average

In progress! | Population dynamics | Predation sub-model | Suitability | MSVPA Type 2 based suitability | Predation mortality | Single-species mode | MSVPA Type 2 based predation mortality | MSVPA Type 3 based predation mortality | Consumption | Bioenergetics based ration | Input ration-at-age | Fishery and survey observation model | Selectivity parameterizations | Selectivity penalty weights | Fixed selectivity | Non-parametric (Type 1) | Logistic | Time-varying logistic | Double logistic | Time-varying double logistic 1 (ascending and descending time-varying parameters) | Time-varying double logistic 2 (random walk ascending-time varying parameters) | Descending logistic | Time-varying descending logistic | Non-parametric (Type 2) | Time-varying non-parametric (Type 2 for hake) | Catchability parameterizations | Linear time-invariant catchability formulations | Time-varying catchability formulations

Overview | A first example: temperature on K | The linkage_spec() API | Priors | Putting a prior on the parameter itself | Growth SD endpoints | Species- and (species, sex)-specific priors | Per-species formulas | Per-sex formulas | Polynomial and basis-expansion formulas | Time blocks | How the pieces fit together | Natural mortality | Recruitment | Priors and fixed values on R0, alpha and beta | Catchability | A prior on the survey catchability | Selectivity | Data weighting (Dirichlet-multinomial) | A multi-process example | Inspecting and debugging | Behind the scenes: automatic base-parameter handling | init and the base parameter | Stratification | Single-sex models and by = ~ ... + sex | Random effects | Integrated or penalized | Current limitations | See also

Overview | 1. Installation | 2. Data | 3. Fit models | 4. Reading the fitted object | 5. Compare models | 6. Model plots and diagnostics

Fitting the operating model | Fitting the estimation model (Tier 3 HCR) | Running the MSE | Check endyr before you start | Reproducibility | Evaluating the MSE with plots | Summary statistics | Alternative scenarios | Different reference points | Triennial bottom-trawl survey | 4-year assessment cycle and reduced survey coverage | A missed assessment, and a buffer while advice is stale | Climate-linked MSE | Climate-linked OM | Run MSE | Overall summary

When to use each mode | Bering Sea example | Gulf of Alaska example | Predator-prey suitability (suitMode) | Comparing outputs

The quick path: build_data() | Which inputs does my model need? | Building the list by hand | Setup | 1. Species / model controls | 2. Fleet control table | 3. Survey index data | 4. Catch data | 5. Composition data (optional) | 6. Conditional age-at-length (CAAL) data (optional) | 7. Biological inputs | Age-to-length transition matrix | Ageing error matrix | Weight-at-age | Maturity-at-age | Sex ratio at age | Fixed selectivity (optional) | Fixed numbers-at-age (optional) | Natural mortality (M1 base) | Environmental covariate data (optional) | 8. Multi-species bioenergetics parameters (optional in single-species mode) | 9. Validate and fit | 10. Export back to Excel (optional) | 11. Persist and share a run configuration (optional)

Model controls | Fleets | Catch data | Survey indices | Composition data | Ageing error | Weight-at-age | Other inputs | Verifying the conversion against SS | See also

Setup | Fixed-F projections | Recruitment in the projection period | Manual recruitment deviations | Stochastic recruitment via sample_rec() | Comparing projection scenarios | Random recruitment effects | Available harvest control rules

Overview | Load data and inspect the growth dataset | Build a growth model with linkages | What this linkage does | Time-varying growth | Fit the model | Inspect the linkage table and prior contributions | Compare with an unlinked baseline model and Richards model | Matching a Stock Synthesis growth specification | Notes on priors through linkages