Package index • multiSA

Data

S4 objects for data setup.

Prepare the data and parameters, then run the model.

check_data(): Check dimensions and inputs in MSAdata object
make_parameters() make_map() check_parameters(): Make list of parameters for RTMB
prior_h() prior_M() prior_q(): Priors for MSA model
fit_MSA(): Fit MSA model
MSAassess-class: MSAassess S4 object

Evaluate model fit and run diagnostics.

profile(<MSAassess>) plot(<MSAprof>): Profile parameters of MSA model
report(): Generate markdown reports
residuals(<MSAassess>): Calculate model residuals
retrospective() plot(<MSAretro>) summary(<MSAretro>) report(<MSAretro>): Retrospective analysis
simulate(<MSAassess>): Simulate data

Functions used in the population model.

calc_F(): Newton-Raphson search for fishing mortality
calc_POP() calc_HSP(): Predict the probability of CKMR kinship pairs
calc_index(): Calculate index at age
calc_nextN(): Project stock abundance to the next time step
calc_phi_project(): Equilibrium spawners per recruit by projection
calc_phi_simple() calc_NPR(): Simple spawners per recruit calculation
calc_population(): Multi-fleet, multi-area, multi-stock population dynamics model
calc_recruitment(): Calculate recruitment from stock-recruit function
conv_mov(): Calculate movement matrix for all age classes
conv_selpar() calc_sel_len() calc_fsel_age() calc_isel_age(): Selectivity at age and length
calc_eqdist(): Equilibrium distribution from movement matrix

Calculate the log-likelihood or log-prior density functions from data and estimated parameters.

Other useful functions.

calc_LAK(): Length-at-age key
calc_growth(): Calculate von Bertalanffy length-at-age
CondExpLt() CondExpLe() CondExpGt() CondExpGe() CondExpEq(): If statements compatible with RTMB
conv_Sigma(): Calculate covariance matrix
get_MSAdata(): Retrieve data object used to fit model
get_sdreport(): Calculate standard errors
optimize_RTMB(): Optimize RTMB model
posfun(): Quadratic penalty function
softmax(): Softmax function