Using the FLjjm package | Loading jjms outputs | Running jjms | jjms.sa | Observation Error Model (oem) | Indices of abundance to generate in MP: | Example / Workflow 1 | Example / Workflow 2 | Reference | buildjjmdata | buildFLIsjjm | FLIndex | More information | Software Versions | Author information | References

Primary goals | Collaboration | Coding style | Suggested norms - General | Organization | Projects | Example folder structure | Scripts | Structure guide | Style guide | Naming conventions | Object names | Dates | Spacing | Writing functions | Function components | Suggested norms - Specific | Framework | Code framework | Teams framework

Additional functionality | Example script

afscISS::get_ISS() | afscISS::get_popn() | afscISS::get_comp() | afscISS::get_bias()

Loading & viewing your data | Detecting Breakpoints | Re-fitting growth curves at putative breaks | Updating and re-fitting the breakpoints

Base model structure for a single survey and stratum | Extending to multiple biomass survey strata | Addition of an auxiliary catch per unit effort (CPUE) survey | The ADMB version of REMA | Estimation of additional observation error | Exploration of the Tweedie distribution for zero biomass observations | Experimental One-Step Ahead (OSA) residuals | References

Background | A testing framework for REMA model validation | Prepare data for each stock here | 1. Simulation self-test: Can we recover parameters without bias? | 2. Residual analysis | 3. Laplace approximation: Are the model assumptions related to random effects estimation reasonable? | 4. Parameter correlation: Are the model parameters identifiable and non-redundant? | Results of parameter correlation analysis | 5. Is the GOA Thornyhead model really converged? | When should we care about failed model diagnostics? | Preliminary recommendations | References

Model 1: Zeros as NAs | Model 2: Add a small constant | Model 3: Tweedie distribution | Compare model results

EBS Pollock Model Description | Dynamics | Recruitment | Diagnostics | Parameter estimation | Uncertainty in mean body mass | Tier 1 projections

Example use of ebswp library for setting up a new model | Read in an existing model | For SSB1 | And SSB2 | And obs-error run | Now run them all in parallel

Example

Model 1: Fit to the biomass and CPUE survey through the estimation of a scaling parameter $q$ | A comparison of the ADMB and TMB models | Model 2: Estimating additional observation error in the two surveys | Model comparison | Model selection | Apportionment results

The rema workflow: | Example 1: Univariate random effects (RE) model with a single survey and stratum | Example 2: Multivariate random effects model (REM) with a single survey and multiple strata

Let's make a plot!