Code associated with ICES paper (Fisch et al. 2023)
SpatialModel_Function.R - Spatially Explicit Simulation Operating Model
Get_Data.R - Sampling Model
Other files in the main directory are those used for the Spatially Explicit OM
TMB_Models includes all of the TMB Estimation models used
EM1111 - Baseline Estimation model with Dirichlet-multinomial for fitting composition data
EM1112 - Baseline Estimation model with Logistic-normal for fitting composition data
EM1113 - Baseline Estimation model with Multinomial for fitting composition data
EM1114 - Baseline Estimation model with Logistic-normal however without AR1 terms for fitting composition data
EM1115 - Baseline Estimation model with Multivariate-Tweedie for fitting composition data
EM1116 - Baseline Estimation model with Logistic-normal for fitting composition data however without relative weighting between years based on sample size
EM1121 - Estimation models with white noise variability in Natural Mortality and Dirichlet-multinomial for fitting composition data
EM1122 - Estimation models with white noise variability in Natural Mortality and Logistic-normal for fitting composition data
EM1131 - Estimation models with random walk variability in Natural Mortality and Dirichlet-multinomial for fitting composition data
EM1132 - Estimation models with random walk variability in Natural Mortality and Logistic-normal for fitting composition data
EM1141 - Estimation models with 2D AR(1) variability in Natural Mortality and Dirichlet-multinomial for fitting composition data
EM1142 - Estimation models with 2D AR(1) variability in Natural Mortality and Logistic-normal for fitting composition data
EM1211 - Estimation models with white noise variability in Catchability and Dirichlet-multinomial for fitting composition data
EM1212 - Estimation models with white noise variability in Catchability and Logistic-normal for fitting composition data
EM1311 - Estimation models with random walk variability in Catchability and Dirichlet-multinomial for fitting composition data
EM1312 - Estimation models with random walk variability in Catchability and Logistic-normal for fitting composition data
EM2111 - Estimation models with white noise variability in Selectivity and Dirichlet-multinomial for fitting composition data
EM2112 - Estimation models with white noise variability in Selectivity and Logistic-normal for fitting composition data
EM3111 - Estimation models with 2D AR(1) variability in Selectivity and Dirichlet-multinomial for fitting composition data
EM3112 - Estimation models with 2D AR(1) variability in Selectivity and Logistic-normal for fitting composition data
EM2211 - Estimation models with white noise variability in Selectivity and white noise variability in Catchability and Dirichlet-multinomial for fitting composition data
EM2212 - Estimation models with white noise variability in Selectivity and white noise variability in Catchability and Logistic-normal for fitting composition data
EM2311 - Estimation models with white noise variability in Selectivity and random walk variability in Catchability and Dirichlet-multinomial for fitting composition data
EM2312 - Estimation models with white noise variability in Selectivity and random walk variability in Catchability and Logistic-normal for fitting composition data
EM3131 - Estimation models with 2D AR(1) variability in Selectivity and random walk variability in Natural Mortality and Dirichlet-multinomial for fitting composition data
EM3132 - Estimation models with 2D AR(1) variability in Selectivity and random walk variability in Natural Mortality and Logistic-normal for fitting composition data
EM3141 - Estimation models with 2D AR(1) variability in Selectivity and 2D AR(1) variability in Natural Mortality and Dirichlet-multinomial for fitting composition data
EM3142 - Estimation models with 2D AR(1) variability in Selectivity and 2D AR(1) variability in Natural Mortality and Logistic-normal for fitting composition data
EM3211 - Estimation models with 2D AR(1) variability in Selectivity and and white noise variability in Catchability Dirichlet-multinomial for fitting composition data
EM3212 - Estimation models with 2D AR(1) variability in Selectivity and and white noise variability in Catchability Logistic-normal for fitting composition data
EM3311 - Estimation models with 2D AR(1) variability in Selectivity and random walk variability in Catchability and Dirichlet-multinomial for fitting composition data
EM3312 - Estimation models with 2D AR(1) variability in Selectivity and random walk variability in Catchability and Logistic-normal for fitting composition data