comsir: COM-SIR method
In datalimited/datalimited: Stock Assessment Methods for Data-limited Fisheries

Description Arguments References Examples

Catch-only model with sampling-importance-resampling based on the method described in Vasconcellos and Cochrane (2005).

`yr`	A time series of years associated with the catch
`ct`	A time series of catch
`start_r`	A numeric vector of length 2 giving the lower and upper bounds on the population growth rate parameter. This can either be specified manually or by translating resiliency categories via the function `resilience`.
`k_bounds`	Minimum and maximum possible stock size at carrying capacity
`x_bounds`	Minimum and maximum possible x (effort dynamics parameter) values
`a_bounds`	Minimum and maximum possible a (effort dynamics parameter) values
`norm_k`	Logical: should `k` have a normal prior (`TRUE`) or a uniform prior between `mink` and `maxk` (`FALSE`)?
`logk`	If `logk = TRUE` and `norm_k = FALSE` the prior on `k` will be an exponentiated form a uniform distribution on a log scale between `log(mink)` and `log(maxk)`.
`nsim`	Number of iterations to run before sampling
`logistic_model`	Logical: if `TRUE` then the effort dynamics model will be a logistic model. If `FALSE` then the effort dynamics model will be linear.
`n_posterior`	Number of posterior samples to draw
`obs`	Logical: if `FALSE` then a measurement-error catch model is used. If `TRUE` then a process-error catch model is used.
`effort_bounds`	Lower and upper limits on rate of decrease and increase in effort from one time step to the next.
`dampen`	Should effort dynamics parameters be excluded that may give unstable dynamics?
`cv_bounds`	Min and max limits on residual error CV around catch

Vasconcellos, M., and K. Cochrane. 2005. Overview of World Status of Data-Limited Fisheries: Inferences from Landings Statistics. Pages 1-20 in G. H. Kruse, V. F. Gallucci, D. E. Hay, R. I. Perry, R. M. Peterman, T. C. Shirley, P. D. Spencer, B. Wilson, and D. Woodby, editors. Fisheries Assessment and Management in Data-Limited Situations. Alaska Sea Grant, University of Alaska Fairbanks.

x <- comsir(ct = blue_gren$ct, yr = blue_gren$yr, nsim = 1e5,
  n_posterior = 2e3)
par(mfrow = c(1, 2))
hist(x$quantities$bbmsy)
with(x$posterior, plot(r, k))