Description of Model and Data Dimensions

The following tables describes all elements contained within input_list$data, which is generated using the SPoRC::Setup_x functions. Note that they do not detail how the SPoRC::Setup_x functions should be utilized. Rather, they detail the components of input_list$data. Thus, for further details on how arguments for SPoRC::Setup_x functions should be defined, users should refer to the function documentation. Furthermore, note that when n_sexes > 1, the first dimension will always be females and the second dimension will always be males.

Data Inputs for Defining Model Dimensions

Name	Description
years	Vector specifying number of years to model
ages	Vector specifying number of ages to model
lens	Vector specifying number of lengths to model
n_regions	Value specifying number of regions to model
n_sexes	Value specifying number of sexes to model
n_fish_fleets	Value specifying number of fishery fleets to model
n_srv_fleets	Value specifying number of survey fleets to model
n_proj_yrs_devs	Number of years to project deviations / random effect parameters forward

Data Inputs for Defining Recruitment Processes

Name	Description
rec_lag	Value specifying the delay between spawning and when recruits enter the population. For example, if recruits enter the population as age 2, rec_lag would be specified as 2, such that the spawning biomass from year – 2 produces these recruits
Use_h_prior	Value specifying whether steepness priors are used. 0: Don’t use priors, 1: Use Priors. Steepness priors are bounded between 0.2 and 1 with a scaled beta penalty.
h_prior	Data frame specifying prior distributions for steepness parameters. Must include columns: region (region index), mu (mean of the prior in normal space), and sd (standard deviation of the prior in normal space). For each row, a beta distribution is scaled to the interval [0.2, 1], and the corresponding h_trans value is transformed and penalized using the log-density from the scaled beta distribution.
do_rec_bias_ramp	Value specifying whether or not the Methot and Taylor recruitment bias ramp is conducted. 0: Don’t do bias ramp, 1: Do bias ramp
max_bias_ramp_fct	Value specifying the maximum bias correction factor applied to the recruitment bias ramp.
bias_year	Vector of years, specifying when to change the bias ramp. Can be specified with an NA if do_rec_bias_ramp = 0
sigmaR_switch	Value specifying when to transition between using an early period sigma and late period sigma R for penalizing initial age deviations and recruitment deviations. Specify as 0 if sigmaR early is equal to sigmaR late.
init_age_strc	Value specifying how the population age structure should be initialized. 0: Initialize via iteration to equilibrium, 1: Initialize with geometric series solution.
equil_init_age_strc	Value specifying how initial age deviations arise. 0 == equilibrium, 1 == stochastic except for plus group, 2 == stochastic for all ages (including the plus group)
init_F_prop	Value specifying the proportion of fishing mortality to apply to the initial age deviations relative to the mean fishing mortality parameter for fishery fleet 1 (the dominant fleet)
rec_model	Value specifying the recruitment model. 0 == Mean Recruitment, 1 == Beverton-Holt with steepness parameterization
rec_dd	Value specifying the recruitment density dependence (only used when there is a stock recruitment relationship) 0 == local density, 1 == global density, 999 == no density dependent stock recruitment form is used
t_spawn	Fraction of year in which spawning occurs
Use_Rec_prop_Prior	Value specifying whether recruitment proportion priors are used. 0: Don’t use priors, 1: Use Priors. Recruitment proportion priors are applied using a Dirichlet distribution.
Rec_prop_prior	Prior values defined for recruitment proportions if used (i.e., the Dirichlet concentration parameters) dimensioned by n_regions
sexratio_blocks	Matrix specifying sexratio blocks (n_regions x n_years)

Data Inputs for Defining Biological Processes

Name	Description
WAA	Weight-at-age values dimensioned by n_regions, n_years, n_ages, n_sexes
WAA_fish	Weight-at-age values for the fishery dimensioned by n_regions, n_years, n_ages, n_sexes, n_fish_fleets
WAA_srv	Weight-at-age values for the survey dimensioned by n_regions, n_years, n_ages, n_sexes, n_srv_fleets
MatAA	Maturity-at-age values dimensioned by n_regions, n_years, n_ages, n_sexes
AgeingError	Ageing error dimensioned by number of years, number of modelled ages, number of observed composition ages, where rows across modelled ages sum to 1
fit_lengths	Value describing whether or not to fit length composition data. 0: Don’t fit lengths, 1: Fit lengths
SizeAgeTrans	Size-age transition matrix dimensioned by n_regions, n_years, n_lens, n_ages, n_sexes. Can be specified as NA if length compositions are not fit
addtocomp	Constant to add to all composition data.
addtofishidx	Constant to add to all fishery index data.
addtosrvidx	Constant to add to all survey index data.
addtotag	Constant to add to all tagging data.
Use_M_prior	Value specifying whether natural mortality priors are used. 0: Don’t use prior, 1: Use prior
M_prior	A dataframe specifying how natural mortality priors should be applied.
Selex_Type	Value specifying whether age or length-based selectivity is used. 0: Age-based, 1: Length-based
use_fixed_natmort	Value specifying whether a fixed mortality array is used. 0: Don’t use and estimate M, 1: Use
Fixed_natmort	Natural mortality array dimensioned by n_regions, n_years, n_ages, and n_sexes.
M_blocks	Array that specifies the dimensions in which natural mortality is blocked (n_regions, n_years, n_ages, and n_sexes)

Data Inputs for Defining Movement Processes

Name	Description
do_recruits_move	Value specifying whether recruits are allowed to move. 0: Recruits don’t move, 1: Recruits move
use_fixed_movement	Value specifying whether or not to use a fixed movement matrix. 0: Don’t use fixed movement, 1: Use fixed movement
Fixed_Movement	Fixed movement matrix dimensioned by n_regions, n_regions, n_years, n_ages, n_sexes
Use_Movement_Prior	Value specifying whether or not to use movement priors. 0: Don’t use movement prior, 1: Use movement prior
Movement_prior	Array dimensioned by n_regions, n_regions, n_years, n_ages, n_sexes specifying values for a Dirichlet prior
map_Movement_Pars	Array by n_regions, n_regions, n_years, n_ages, n_sexes specifying which movement parameters are shared and mapped off
cont_vary_movement	Integer indicating whether movement is continuously varying across regions, years, and ages (0 = none, 1 = iid deviations)
map_logit_move_devs	Array dimensioned by n_regions, n_regions - 1, n_years, n_ages, and n_sexes specifying which movement parameters are shared and mapped off

Data Inputs for Defining Tagging Processes

Name	Description
UseTagging	Value indicating whether to use tagging data. 0: Don’t use tagging data, 1: Use tagging data
tag_release_indicator	Matrix dimensioned by n_tag_cohorts, 2. The 2 columns in this matrix represent the tag region (column 1) for a given cohort (rows of the matrix) and the tag year of the cohort (column 2).
n_tag_cohorts	Value specifying the number of tag cohorts released
max_tag_liberty	Value specifying the maximum tag liberty to track cohorts
Tagged_Fish	Array dimensioned by n_tag_cohorts, n_ages, n_sexes for the number of tagged fish from a given cohort
Obs_Tag_Recap	Array dimensioned by max_tag_liberty, n_tag_cohorts, n_regions, n_ages, n_sexes of individuals that are recaptured. If no age or sex information is available to distinguish recaptured individuals, users can initialize the array with 0s and the recaptured individuals should be input into the first dimension of n_ages and the first dimension of n_sexes. This is then coupled with move_age_tag_pool and move_sex_tag_pool (see last two rows in this table for an example) to ensure that the predicted recaptures are summed across ages and sexes when fitting to the observed recaptures for a given likelihood.
Tag_LikeType	Value specifying the tag likelihood to use. 0: Poisson, 1: Negative Binomial, 2: Multinomial release conditioned, 3: Multinomial recaptured conditioned
mixing_period	Value specifying the mixing period for tag cohorts. This still requires the full history of the tag cohort to be input, but it ignores the mixing period in the likelihood calculations.
t_tagging	Fraction of year in which tagging occurs. Specify at 1 if tagging happens at the start of the year such that mortality is not discounted
Use_TagRep_Prior	Value specifying whether or not a tag reporting rate prior should be used. 0: Don’t use prior, 1: Use prior
TagRep_Prior	Data frame containing prior specifications for tag reporting parameters. Must include columns: region (region index), block (time block index), mu (numeric mean for tag reporting prior in normal space; use NA if symmetric beta is used), sd (numeric standard deviation for the prior in normal space), and type (0 = symmetric beta, 1 = regular beta). Each row specifies a beta prior for one tag reporting parameter. Only parameters with rows in this data frame will have priors applied.
tag_selex	Value specifying how tag selectivity is specified. 0 == Uniform with F resulting from Fleet 1, 1 == Sex Aggregated Selectivity with F resulting from Fleet 1, 2 == Sex-Specific Selectivity with F resulting from Fleet 1, 3 == Uniform with F resulting from a weighted sum of all fleets, 4 == Sex Aggregated Selectivity with F resulting from a weighted sum of all fleets, 5 == Sex-Specific Selectivity with F resulting from a weighted sum of all fleets
tag_natmort	Value specifying how tag natural mortality is specified. 0 == Natural mortality aggregated across ages and sexes, 1 == Natural mortality is age-specific but sex-aggregated, 2 == Natural mortality is age aggregated but sex-specific, 3 == Natural mortality is age and sex-specific
move_age_tag_pool	Data list object detailing how tag recaptures along the age axis should be pooled. If we have age information for tagging data, but are fitting two age blocks (10 total ages) and want to pool these two age blocks together when fitting to reduce computational cost, this can be specified as: move_age_tag_pool <- list(c(1:5), c(6:10)) where the first element in the list specifies the ages to sum or pool across when, while the second element in the list specifies the age groups to pool across when fitting. Conversely, if we don’t have an age information, this list would be specified as: move_age_tag_pool <- list(c(1:30)) where we are summing across all ages for the observed and predicted tag recaptures when fitting to these data
move_sex_tag_pool	Data list object detailing how tag recaptures along the sex axis should be pooled. If we have sex information for tagging data and are estimating sex-specific movement (2 sexes in this case), this would be specified as: move_sex_tag_pool <- list(1, 2) where the first element in the list specifies the first sex and the second element in the list specifies the second sex (i.e., no pooling in this case). Conversely, if we don’t have sex information, this list would be specified as: move_sex_tag_pool <- list(c(1:2)) where we are summing across all sexes for the observed and predicted tag recaptures when fitting to these data
Tag_Reporting_blocks	Array dimensioned by n_regions and n_years indicating the regions and years for which a block is specified
map_Tag_Reporting_Pars	Array dimensioned by n_regions and n_years indicating the regions and years for which a block is specified and which tagging parameters are mapped off / shared.

Data Inputs for Defining Catch and Fishing Mortality Processes

Name	Description
ObsCatch	Observed catch data dimensioned by n_regions, n_years, n_fish_fleets
Catch_Type	Matrix dimensioned by n_years, n_fish_fleets describing when and for what fishery fleet catch is aggregated across regions (0) or spatially explicit (1). For example, in a case where we have 10 years and 1 fishery fleet, where fishery fleet 1 is spatially aggregated in the first 5 years and fishery fleet 2 is spatially explicit, this would be structured as: Catch_Type <- array(c(rep(0, 5), rep(1, 5)), dim = c(n_years, n_fish_fleets))
UseCatch	Array dimensioned by n_regions, n_years, n_fish_fleets describing whether to fit to catch data in a given year for a given fleet. 0: Don’t fit catch, 1: Fit catch
est_all_regional_F	Value specifying whether regional fishing mortality deviations are all estimated or only a subset are. 0: A subset of fishing mortality deviations are spatially aggregated, 1: All fishing mortality deviations are spatially explicit, irrespective of whether catch is aggregated across regions in certain periods
Use_F_pen	Value specifying whether to use a fishing mortality penalty to regularize fishing mortality deviations. 0: Don’t use regularity penalty, 1: Use regularity penalty

Data Inputs for Defining Fishery Indices and Compositions

Name	Description
ObsFishIdx	Fishery index dimensioned by n_regions, n_years, n_fish_fleets
ObsFishIdx_SE	Fishery index standard errors dimensioned by n_regions, n_years, n_fish_fleets
UseFishIdx	Array dimensioned by n_regions, n_years, n_fish_fleets describing whether to fit to fishery index in a given year for a given fleet. 0: Don’t fit fishery index, 1: Fit fishery index
fish_idx_type	Matrix dimensioned by n_regions, n_fish_fleets specifying the index type for a given region and fishery fleet. 0: Abundance index, 1: Biomass index (uses WAA for calculations), 999: None Available
ObsFishAgeComps	Observed fishery age compositions dimensioned by n_regions, n_years, n_ages, n_sexes, n_fish_fleets. This can be either input as proportions or as numbers, since these are normalized again within the model
UseFishAgeComps	Array dimensioned by n_regions, n_years, n_fish_fleets specifying whether or not to fit to fishery age compositions. 0: Don’t fit fishery ages, 1: Fit fishery ages
ISS_FishAgeComps	Array dimensioned by n_regions, n_years, n_fish_fleets specifying the input sample size for a multinomial or Dirichlet-multinomial likelihood. This is used in conjunction with Wt_FishAgeComps or ln_FishAge_theta if a Dirichlet-multinomial is used
Wt_FishAgeComps	Array dimensioned by n_regions, n_sexes, n_fish_fleets specifying a multinomial weight to apply to fishery age compositions, which should ideally be derived using Francis re-weighting methods
ObsFishLenComps	Observed fishery length compositions dimensioned by n_regions, n_years, n_lens, n_sexes, n_fish_fleets. This can be either input as proportions or as numbers, since these are normalized again within the model
UseFishLenComps	Array dimensioned by n_regions, n_years, n_fish_fleets specifying whether or not to fit to fishery length compositions. 0: Don’t fit fishery lengths, 1: Fit fishery lengths
ISS_FishLenComps	Array dimensioned by n_regions, n_years, n_fish_fleets specifying the input sample size for a multinomial or Dirichlet-multinomial likelihood. This is used in conjunction with Wt_FishLenComps or ln_FishLen_theta if a Dirichlet-multinomial is used
FishAgeComps_LikeType	Vector dimensioned by n_fish_fleets specifying the likelihood to use for fitting fishery age compositions. 0: Multinomial, 1: Dirichlet-multinomial, 2: Logistic-normal with iid covariance, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
FishLenComps_LikeType	Vector dimensioned by n_fish_fleets specifying the likelihood to use for fitting fishery length compositions. 0: Multinomial, 1: Dirichlet-multinomial, 2: Logistic-normal with iid covariance, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
FishAgeComps_Type	Matrix dimensioned by n_years, n_fish_fleets specifying how age composition data should be structured and fit to. 0: Aggregated across sexes and regions, 1: Split by sexes and regions, 2: Joint by sex but split by region, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
FishLenComps_Type	Matrix dimensioned by n_years, n_fish_fleets specifying how length composition data should be structured and fit to. 0: Aggregated across sexes and regions, 1: Split by sexes and regions, 2: Joint by sex but split by region, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R

Data Inputs for Defining Survey Indices and Compositions

Name	Description
ObsSrvIdx	Survey index dimensioned by n_regions, n_years, n_srv_fleets
ObsSrvIdx_SE	Survey index standard errors dimensioned by n_regions, n_years, n_srv_fleets
UseSrvIdx	Array dimensioned by n_regions, n_years, n_srv_fleets describing whether to fit to survey index in a given year for a given fleet. 0: Don’t fit survey index, 1: Fit survey index
srv_idx_type	Matrix dimensioned by n_regions, n_srv_fleets specifying the index type for a given region and survey fleet. 0: Abundance index, 1: Biomass index (uses WAA for calculations), 999: None Available.
ObsSrvAgeComps	Observed survey age compositions dimensioned by n_regions, n_years, n_ages, n_sexes, n_srv_fleets. This can be either input as proportions or as numbers, since these are normalized again within the model
UseSrvAgeComps	Array dimensioned by n_regions, n_years, n_srv_fleets specifying whether or not to fit to survey age compositions. 0: Don’t fit survey ages, 1: Fit survey ages
ISS_SrvAgeComps	Array dimensioned by n_regions, n_years, n_srv_fleets specifying the input sample size for a multinomial or Dirichlet-multinomial likelihood. This is used in conjunction with Wt_SrvAgeComps or ln_SrvAge_theta if a Dirichlet-multinomial is used
Wt_SrvAgeComps	Array dimensioned by n_regions, n_sexes, n_srv_fleets specifying a multinomial weight to apply to survey age compositions, which should ideally be derived using Francis re-weighting methods
ObsSrvLenComps	Observed survey length compositions dimensioned by n_regions, n_years, n_lens, n_sexes, n_srv_fleets. This can be either input as proportions or as numbers, since these are normalized again within the model
UseSrvLenComps	Array dimensioned by n_regions, n_years, n_srv_fleets specifying whether or not to fit to survey length compositions. 0: Don’t fit survey lengths, 1: Fit survey lengths
ISS_SrvLenComps	Array dimensioned by n_regions, n_years, n_srv_fleets specifying the input sample size for a multinomial or Dirichlet-multinomial likelihood. This is used in conjunction with Wt_SrvLenComps or ln_SrvLen_theta if a Dirichlet-multinomial is used
SrvAgeComps_LikeType	Vector dimensioned by n_srv_fleets specifying the likelihood to use for fitting survey age compositions. 0: Multinomial, 1: Dirichlet-multinomial, 2: Logistic-normal with iid covariance, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
SrvLenComps_LikeType	Vector dimensioned by n_srv_fleets specifying the likelihood to use for fitting survey length compositions. 0: Multinomial, 1: Dirichlet-multinomial, 2: Logistic-normal with iid covariance, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
SrvAgeComps_Type	Matrix dimensioned by n_years, n_srv_fleets specifying how age composition data should be structured and fit to. 0: Aggregated across sexes and regions, 1: Split by sexes and regions, 2: Joint by sex but split by region, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
SrvLenComps_Type	Matrix dimensioned by n_years, n_srv_fleets specifying how length composition data should be structured and fit to. 0: Aggregated across sexes and regions, 1: Split by sexes and regions, 2: Joint by sex but split by region, 999: None Available. More options and details can be found in Get_Comp_Likelihoods.R
t_srv	Matrix dimensioned by n_years, n_srv_fleets specifying survey timing as a proportion of the year.

Data Inputs for Defining Fishery Selectivity and Catchability

Name	Description
cont_tv_fish_sel	Matrix dimensioned by n_regions, n_fish_fleets specifying whether or not to do continuous time-varying selectivity, and the type of continuous time-varying selectivity to do. 0: Don’t do continuous time-varying selectivity, 1: iid continuous time-varying selectivity, 2: random walk time-varying selectivity, 3: 3d gmrf continuous time-varying selectivity with marginal variance (semi-parametric), 4: 3d gmrf continuous time-varying selectivity with conditional variance (semi-parametric). More options and details on this can be found in Get_PE_loglik.R, Get_3d_precision.R, and Get_Selex.R
fish_sel_blocks	Array dimensioned by n_regions, n_years, n_fish_fleets specifying when a new selectivity might be specified. Unique numbers denote when a given fishery selectivity block should be specified. For example, if we have 1 region, 1 fishery fleet and 10 years, and we have two evenly spaced time blocks, this would be specified as: fish_sel_blocks <- array(c(rep(0,5),rep(1,5)), dim = c(n_regions, n_years, n_fish_fleets)) where each unique number represents the selectivity block pattern to apply in that given period.
fish_sel_model	Array dimensioned by n_regions, n_years, n_fish_fleets specifying the selectivity pattern for a given region, year, and fishery fleet. Several selectivity forms are currently available. 0: Logistic with a50 and slope, 1: Gamma dome-shaped, 2: Power function, 3: Logistic with a50 and a95, 4: Double Normal with 6 parameters. More options and details can be found in Get_Selex.R and the model equations vignette.
fish_q_blocks	Array dimensioned by n_regions, n_years, n_fish_fleets specifying when a catchability block might be specified. Unique numbers denote when a new catchability block should be specified. For example, if we have 1 region, 1 fishery fleet and 10 years, and we have two evenly spaced catchability blocks, this would be specified as: fish_q_blocks <- array(c(rep(0,5),rep(1,5)), dim = c(n_regions, n_years, n_fish_fleets)) where each unique number represents the unique catchability estimate to apply in that given period.
Use_fish_q_prior	Fishery catchability prior indicator 0 == don’t use, 1 == use
fish_q_prior	Data frame containing prior specifications for fishery catchability parameters. Must include columns: region (region index), fleet (fleet index), block (time block index), mu (prior mean on natural scale), and sd (prior standard deviation on log scale). Each row specifies a log-normal prior N(log(mu), sd) for one catchability parameter.
map_ln_fishsel_devs	Array of values dimensioned by n_regions, n_years, n_ages, n_sexes, and n_fish_fleets indicating which values are mapped off
map_fish_q	Array of values dimensioned by n_regions, n_q_time_blocks, and n_fish_fleets indicating which values are mapped off
Use_fish_selex_prior	Integer (0 or 1). Flag to enable/disable fishery selectivity priors. When set to 1, applies log-normal priors to fishery selectivity parameters as specified in fish_selex_prior. When set to 0, no priors are applied.
fish_selex_prior	Data frame containing prior specifications for fishery selectivity parameters. Must include columns: region (region index), fleet (fleet index), block (time block index), sex (sex index), par (parameter index), mu (prior mean on natural scale), and sd (prior standard deviation on log scale). Each row specifies a log-normal prior N(log(mu), sd) for one selectivity parameter.
cont_tv_fish_sel_penalty	Boolean on whether fishery selectivity penalties are applied to continous time-varying selectivity

Data Inputs for Defining Survey Selectivity and Catchability

Name	Description
cont_tv_srv_sel	Matrix dimensioned by n_regions, n_srv_fleets specifying whether or not to do continuous time-varying selectivity, and the type of continuous time-varying selectivity to do. 0: Don’t do continuous time-varying selectivity, 1: iid continuous time-varying selectivity, 2: random walk time-varying selectivity, 3: 3d gmrf continuous time-varying selectivity with marginal variance (semi-parametric), 4: 3d gmrf continuous time-varying selectivity with conditional variance (semi-parametric). More options and details on this can be found in Get_PE_loglik.R, Get_3d_precision.R, and Get_Selex.R
srv_sel_blocks	Array dimensioned by n_regions, n_years, n_srv_fleets specifying when a new selectivity might be specified. Unique numbers denote when a given survey selectivity block should be specified. For example, if we have 1 region, 1 survey fleet and 10 years, and we have two evenly spaced time blocks, this would be specified as: srv_sel_blocks <- array(c(rep(0,5),rep(1,5)), dim = c(n_regions, n_years, n_srv_fleets)) where each unique number represents the selectivity block pattern to apply in that given period.
srv_sel_model	Array dimensioned by n_regions, n_years, n_srv_fleets specifying the selectivity pattern for a given region, year, and survey fleet. Several selectivity forms are currently available. 0: Logistic with a50 and slope, 1: Gamma dome-shaped, 2: Power function, 3: Logistic with a50 and a95, 4: Double Normal with 6 parameters. More options and details can be found in Get_Selex.R and the model equations vignette.
srv_q_blocks	Array dimensioned by n_regions, n_years, n_srv_fleets specifying when a catchability block might be specified. Unique numbers denote when a new catchability block should be specified. For example, if we have 1 region, 1 survey fleet and 10 years, and we have two evenly spaced catchability blocks, this would be specified as: srv_q_blocks <- array(c(rep(0,5),rep(1,5)), dim = c(n_regions, n_years, n_srv_fleets)) where each unique number represents the unique catchability estimate to apply in that given period.
Use_srv_q_prior	Survey catchability prior indicator 0 == don’t use, 1 == use
srv_q_prior	Data frame containing prior specifications for survey catchability parameters. Must include columns: region (region index), fleet (fleet index), block (time block index), mu (prior mean on natural scale), and sd (prior standard deviation on log scale). Each row specifies a log-normal prior N(log(mu), sd) for one catchability parameter.
map_ln_srvsel_devs	Array of values dimensioned by n_regions, n_years, n_ages, n_sexes, and n_srv_fleets indicating which values are mapped off
map_srv_q	Array of values dimensioned by n_regions, n_q_time_blocks, and n_srv_fleets indicating which values are mapped off
do_srv_q_cov	Numeric value indicating whether a survey catchability covariate is used in the model. 0 == Not used, 1 == Used
srv_q_cov	Array dimensioned by n_regions, n_years, n_srv_fleets, n_covariates representing the covariates used to compute survey catchability.
Use_srv_selex_prior	Integer (0 or 1). Flag to enable/disable survey selectivity priors. When set to 1, applies log-normal priors to survey selectivity parameters as specified in srv_selex_prior. When set to 0, no priors are applied.
srv_selex_prior	Data frame containing prior specifications for survey selectivity parameters. Must include columns: region (region index), fleet (fleet index), block (time block index), sex (sex index), par (parameter index), mu (prior mean on natural scale), and sd (prior standard deviation on log scale). Each row specifies a log-normal prior N(log(mu), sd) for one selectivity parameter.
cont_tv_srv_sel_penalty	Boolean on whether survey selectivity penalties are applied to continous time-varying selectivity

Data Inputs for Defining Model Weighting

Name	Description
Wt_Catch	Weight applied to fishery catch likelihoods. Either a numeric scalar or an array of scalars dimensioned by n_regions, n_years, n_fish_fleets.
Wt_FishIdx	Weight applied to fishery index likelihoods. Either a numeric scalar or an array of scalars dimensioned by n_regions, n_years, n_fish_fleets.
Wt_SrvIdx	Weight applied to survey index likelihoods. Either a numeric scalar or an array of scalars dimensioned by n_regions, n_years, n_srv_fleets.
Wt_Rec	Weight applied to initial age deviations and recruitment deviations.
Wt_F	Weight applied to fishing mortality deviations.
Wt_Tagging	Weight applied to tagging data.
Wt_FishAgeComps	Array dimensioned by n_regions, n_years, n_sexes, n_fish_fleets specifying a multinomial weight to apply to fishery age compositions, which should ideally be derived using Francis re-weighting methods
Wt_FishLenComps	Array dimensioned by n_regions, n_years, n_sexes, n_fish_fleets specifying a multinomial weight to apply to fishery length compositions, which should ideally be derived using Francis re-weighting methods
Wt_SrvAgeComps	Array dimensioned by n_regions, n_years, n_sexes, n_srv_fleets specifying a multinomial weight to apply to survey age compositions, which should ideally be derived using Francis re-weighting methods
Wt_SrvLenComps	Array dimensioned by n_regions, n_years, n_sexes, n_srv_fleets specifying a multinomial weight to apply to survey length compositions, which should ideally be derived using Francis re-weighting methods