Package index • decamod

Plot functions

Functions for visualising model inputs, outputs, and diagnostics.

plot_mcmc_diagnostics(): MCMC diagnostics plot
plot_likelihood_components(): Likelihood components plot
pairs_rtmb(): Pairs plot
plot_Fs(): Plot fishing mortality
plot_Us(): Plot exploitation rate
plot_compare_U(): Plot exploitation rate (ability to compare)
plot_catch(): Plot fishing catch
plot_cpue(): Plot CPUE fits
plot_sexr(): Plot sex ratio fits
plot_compare_sexr(): Compare sex ratios
plot_lfs(): Plot LF fits
plot_compare_lfs(): Plot LF fit comparison
plot_vb(): Compare adjusted vulnerable biomass
plot_ssb(): Compare spawning biomass
plot_compare_cpue(): Compare CPUE fits
plot_rec(): Compare recruitment trajectories
plot_sel(): Plot selectivity at length
plot_M(): Plot natural mortality at length
plot_mat(): Compare maturity
plot_ninit(): Compare initial numbers
plot_posteriors(): Plot posteriors and priors
plot_growth_increment(): Plot growth increments
plot_growth_increment2(): Plot growth increments compare multiple models
plot_growth_density(): Plot growth density
plot_loo(): Plot PSIS LOO diagnostics
plot_profile(): Plot a likelihood profile
plot_priors(): Plot priors
plot_mps(): Plot candidate MPs
plot_tag_residuals(): Plot tag residuals
plot_lf_residuals(): Plot length frequency (LF) residuals
plot_sexr_residuals(): Plot sex ratio residuals
plot_cpue_residuals(): Plot CPUE residuals
plot_catch_residuals(): Plot fishing catch
plot_recruitment(): Plot recruitment time series
plot_retro_biomass(): Plot a retrospective analysis

Model and utility functions

Core model functions and utilities for data preparation and post-processing.

check_bounds_opt(): Check optimized parameters against their bounds
check_data(): Check the data
check_priors(): Check prior specifications against the parameters they apply to
cmb(): Bind data to an objective function
decamod(): Integrated rock lobster stock assessment model
decamod_globals(): Build MCMC globals for decamod models
do_dynamics(): Run the dynamics
diagnose_exploitation(): Diagnose catch-conditioned exploitation without changing population dynamics
do_mpe(): Run management procedure evaluation
do_mpe_old(): Run legacy management procedure evaluation
do_projection(): Do projection
double_normal_ogive(): Double normal curve
dmultinom_fractional(): Fractional multinomial log pseudo-likelihood
drobust(): Robust normal likelihood
dstudent_t_sd(): Student-t density parameterized by standard deviation
evaluate_priors(): Evaluate priors
extend_years(): Extend year-varying value into projection years
get_F(): Estimate fishing mortality
get_M(): Extract natural mortality at length
get_MSY(): Calculate MSY
get_MSY2(): Calculate MSY
get_M_length(): Natural mortality at length
get_RAM(): RAM legacy output
get_U(): Calculate exploitation rate
get_bounds(): Obtain default parameter bounds
get_cpue(): Extract CPUE observations and predictions
get_cpue_likelihood(): Get CPUE likelihood
get_derived_quants(): Get derived quantities
get_equilibrium_numbers(): Obtain the equilibrium numbers at length
get_equilibrium_numbers_lsd(): Obtain the equilibrium numbers at length: LSD approach
get_growth_generator(): Obtain a continuous-time growth generator
get_growth_increment(): Obtain growth increments
get_growth_increment_fixed(): Attach length-diffusion uncertainty to fixed expected growth increments
get_growth_increment_linf(): Obtain generalized Linf/K growth increments
get_growth_increment_ode(): Evaluate the monotone-ODE mean growth curve
get_growth_increment_vb(): Obtain von Bertalanffy growth increments
get_growth_matrix(): Obtain a growth transition matrix
get_growth_matrix_ctmc(): Obtain a time-consistent continuous-time growth matrix
get_growth_shape_contrast_linf(): Expected small-size growth contrast under generalized Linf/K growth
get_growth_tag_likelihood_ctmc(): Evaluate tag observations under a continuous-time latent growth process
get_lf_Francis(): Francis weights
get_loo(): Obtain the leave-one-out information criterion (LOO IC)
get_mcmc_profile(): Get posterior samples for a likelihood profile
get_ninit(): Get initial numbers
get_posterior(): Obtain samples from the posterior distribution of reported quantities
get_priors(): Obtain default priors
get_rec(): Get recruitment
get_reference_region(): Calculate reference levels separately in each region
get_recruitment_size(): Obtain the size at recruitment
get_rho(): Estimate Temporal Autocorrelation in Recruitment Deviations
get_sel(): Get selectivity
get_sexr(): Get sex ratios
get_ssb(): Get spawning stock biomass
get_stock_recruitment(): Obtain stock-recruitment relationship
get_vb(): Get AW adjusted vulnerable biomass
get_weight(): Get weight array by year, season, region, sex, and length
growth_francis_to_linf(): Convert Francis reference increments to generalized Linf/K parameters
growth_linf_to_francis(): Convert generalized Linf/K parameters to Francis reference increments
growth_model(): Standalone tag-growth model
growth_reference_to_linf(): Convert constrained reference-increment coordinates to Linf/K
ht(): Head and tail
inv_lb_ub(): Lower and upper bounds inverse transform
inv_simplex(): Inverse simplex
jac_exp(): Jacobian for exponential transform
jac_invlogit(): Jacobian for inverse logit transform
jac_log(): Jacobian for log transform
jac_logit(): Jacobian for logit transform
lb_ub(): Lower and upper bounds transform
length_to_weight(): Convert length to weight
logistic_capped_ogive(): Capped logistic curve
logistic_ogive(): Logistic curve
mar(): Median of the absolute residual (MAR)
midcut(): Midcut
number_pars(): Number parameters in data frame with column par
posfun(): Positive continuation with a feasibility penalty
post_to_array(): List of posterior outputs to array to input into model
post_to_plot(): List of posterior outputs to data frame for plotting
prepare_growth_transition_data(): Prepare tag records for continuous-time growth transitions
prepare_mpe(): Prepare data and parameters for MPE
prepare_mpe_rm(): Remove years for MPE
prepare_proj(): Prepare data and values lists for projections
process_metric(): Summarise projection performance metrics
project_recruitment(): Project recruitment deviates
redo_obj(): Recreate a fitted model object
residuals_pit_beta(): Calculate Probability Integral Transform (PIT) residuals
residuals_pit_lnorm(): Calculate Probability Integral Transform (PIT) residuals
residuals_pit_norm(): Calculate Probability Integral Transform (PIT) residuals
rule_tacc(): Apply TACC decision rules
run_retrospective(): Do a retrospective analysis
sdnr(): Standard deviation of the normalised residual (SDNR)
simplex(): Simplex
spm_model(): Surplus production model
tmbprofile2(): Adaptive likelihood profiling

Management procedures and MSE

Define estimation models and harvest control rules, evaluate complete procedures, and inspect paired results.

mse_target(): Declare the biological timing of a relative-biomass target
mse_observations(): Construct the observation packet available to a management procedure
em_spm() em_gp() em_function(): Define an estimation model
fit_em_gp(): Fit a frozen observation-to-status Gaussian process
predict_em_gp(): Batch prediction from a frozen observation-to-status GP
hcr_lobster(): Define the rock-lobster harvest control rule
hcr_function(): Define a custom harvest control rule
mp(): Combine an estimation model and harvest control rule
mp_grid(): Cross estimation models and harvest control rules
print(<decamod_mp>) print(<decamod_em>) print(<decamod_mse>): Print MSE specifications and results
run_mp() run_em(): Operate one annual management-procedure update
mse_om(): Define an operating-model adapter
om_decamod(): Use decamod as the operating model for compositional MSE
mse_robustness(): Specify prospective operating-model robustness perturbations
mse_outputs(): Configure additional closed-loop MSE outputs
run_mse(): Run compositional management strategy evaluation
validate_mse(): Validate a completed multi-procedure MSE
summarise_mse(): Summarise management and estimation performance for every EM-HCR combination
summarise_mse_diagnostics(): Summarise OM removal diagnostics without dropping affected trajectories
summarise_mse_indicators(): Summarise expanded MSE performance indicators
compare_mse(): Paired management contrasts with within-chain block resampling
mse_plateau_design(): Construct a plateau-height search using exactly one estimation model
run_plateau_search(): Run a single-EM, paired plateau-height search
summarise_plateau_search(): Summarise a plateau search without assuming a monotonic response

Label helper functions

Helpers for resolving model dimension labels.

default_labels(): Create fallback labels
data_labels(): Resolve labels from model data
year_labels(): Resolve model year labels
season_labels(): Resolve season labels
region_labels(): Resolve region labels
sex_labels(): Resolve sex labels
fishery_labels(): Resolve fishery labels
length_labels(): Resolve length-bin labels
cpue_labels(): Resolve CPUE index labels
model_labels(): Build model output dimension labels

Datasets

Example data objects included in the package.

RAM_template: RAM legacy template
berry_CRA2: Berry observations for CRA 2
catch_CRA2: Catch observations for CRA 2
celr_CRA2: CELR survey observations for CRA 2
fsu_CRA2: FSU survey observations for CRA 2
lf_CRA2: Length frequency observations for CRA 2
logbook_CRA2: Logbook CPUE observations for CRA 2
mature_CRA2: Maturity observations for CRA 2
sexr_CRA2: Sex ratio observations for CRA 2
tags_CRA2: Tag recapture observations for CRA 2
tags_all: Tag recapture observations