The latest release of the SAMtool package is available on CRAN.
RCM_output
tab in plot(RCModel)
, report simulations and convergence rate, and clean up comp plots.RCM(condition = "catch2")
, introduced in version 1.6.0.RCM
updates to calculate fishery length comp when CAL_n > 0
and protect plot.RCM
from empty index vectors.Perfect
uses spawn_timing to calculate spawning biomass (exp(-spawn_time * M)
) in the middle of projection year. Note that perfect HCR implementation needs to iteratively re-calculate the projection year biomass, B/BMSY, B/B0 (exp(-spawn_time * [Ftarget + M])
) when applying the HCR.ObsPars$Isd
.spawn_time_frac
argument to RCM.RCM(map = list(q = c(1, 1)))
. This example allows sharing q between 2 indices. Currently, q can only be an explicit estimated parameter (map argument is an integer), solved analytically (map argument is NA), or fixed to 1 (map argument is NA and additional specification in RCMdata@abs_I).x^0.01 * (1 - x)^0.01
where x is the ratio of the length of full selectivity to Linf or age of full selectivity to maxage.plot_composition(plot_type = "heat_residuals")
. Also re-adjust default bubble residual size.SCA2
and VPA
.make_MP
don’t match formal arguments in .Assess
and .HCR
hist
function will report NA rate (percent of NA’s) in a vector. Seen in markdown reports.max
function excludes infinite values. Primarily used when generating axes limits in markdown reports.Data
object is passed to RCM
.resample = TRUE
with stochastic fits to RCM
.dnorm(log(Shinge/min(SSB)), 0, 2)
for hockey-stick SRR when the hinge point is less than the smallest SSB.RCMdata@Chist
. The trivially small catch still allows predictions of fishery age composition from Baranov equation.diagnostic
introduced in 1.5.0.simulate
method for RCM and assessment models.map
and start
arguments for RCM
.pbapply
.RCM
using the Mesnil and Rochet (2010) parameterization.r
and MSY
for surplus production models.MSE
object.Data@CAL
check when using RCM
.Gmisc::fastDoCall
when fitting models, e.g., SP_Fox
. Gmisc
is a Suggests
package.RCM2MOM
converts the output of RCM
to a multi-fleet operating model.RCM_assess
for using the RCM model as an assessment in closed-loop projections. More arguments will be added in the future for flexibility with model configuration.make_project_MP
creates management procedures that update TAC annually from stock assessment projections.posterior
wrapper function added to run MCMC of RCM models. RCMstan
updates OMs with MCMC output.Shortcut
and Perfect
assessment functions.HCR_segment
with yield per recruit (F01 and Fmax).interim_MP
include adding NULL catch for catch advice and adding missing feature to report assessment output when diagnostic = 'full'
.HCR_segment
and HCR_ramp
.R0
and log(R0)
for RCM models and assessment models.RCM
only enter the objective function once.RCM
reporting.SCA_RWM
can accept multiple years to the refyear
argument, e.g., expression(1:Data@Year)
. The model will calculate reference points (MSY, unfished values, and steepness) using the mean M during the specified years.NA
in Rec@TAC
when multiple assessments do not converge.Shortcut
indexing to align year of assessment with projection. An MP using the Perfect
assessment and HCR_MSY
annually will produce F = FMSY in the OM.VPA
when the catch-at-age in the plusgroup and plusgroup-1 is very small.RCM
will check age and length comp data for NA’s and replaces with zeroRCM
reports annual equilibrium unfished reference points using constant stock recruit alpha and betamake_interim_MP
function is added to generate MPs that adjust the TAC between periodic assessments using the index.SP
is added to avoid negative biomass situations.RCM
so that the mean is one in normal space. This error was apparent when autocorrelation was very large.HCR_segment
allows for creating control rules with any number of linear segments.RCM
.RCMdata
, is used to send data to the RCM model, i.e., RCM(OM, RCMdata)
. For now, backwards compatibility should still be maintained when feeding a data list (used prior to v1.2) to fit the model.profile
generic is now available for RCM
models. Steepness, R0, and final depletion can be profiled.compare_RCM
.RCM
are now lognormal instead of normal.Catch
, CAA
, and CAL
in addition to Index
in a named list LWT
. Backwards compatibility remains to provide LWT
as a vector for index likelihood weights only.SCA_DDM
) is added.SCA_CAL
) is added.MW = TRUE
. The functions will look for mean weight data series in Data@Misc[[x]]$MW
, otherwise will convert length composition Data@CAL
to weights and calculate annual means.Shortcut2
). This function fits an SCA assessment and then characterizes the assessment error relative to the operating model using a vector autoregressive (VAR) model. The functions samples the operating model with error predicted from the VAR model for the projection period. This is a useful function to guide the level of error in the shortcut method.HCR_ramp
are available to create harvest control rules based on dynamic B0, and F-based rules (F/FMSY, F/F01, F/F-SPR).HCR_escapement
).RCM
will now incorporate catches into the likelihood as a default. This allows the model to estimate F and R0 when conditioned on effort and there is patchy catch data.multiMSE
remains in MSEtool.SCA
, SCA_Pope
, SSS
) start at age 0 following the change in the MSEtool OM.SCA_RWM
) can be used to estimate time-varying M (constant with age) as a random walk. Fix the random walk SD to a low value to effectively estimate a time-constant M (see help page).nlminb
) are turned off. Convergence status and issues can be checked in the conv
slot of the output Assessment object. In closed-loop simulation, the diagnostic
function can be used to track the behavior of model-based MPs. By default, pre-packaged model-based MPs and MPs made from the make_MP
function are designed to report convergence info (stored in MSE@PPD
).Shortcut
assess function samples the OM with error and autocorrelation for HCRs as an emulator of a stock assessment in closed-loop simulation. The Perfect
function samples the OM without error.AddInd
argument of functions which index slots in the Data object will be used among Data@Ind, Data@SpInd, Data@VInd, and Data@AddInd. Within series weighting is applied by using the corresponding CV slot, i.e., Data@CV_Ind for Data@Ind, etc. Among series weighting can also be tuned using likelihood weights with LWT
argument. For SCA and VPA models, the selectivity is fixed in the model using Data@AddIndV for indices in Data@AddInd.RCM
(Rapid Conditioning Model).maxage + 1
which corresponds to ages 0 to maxage.condition = "catch"
), the likelihood for the catch can now have a user-defined standard deviation indicated in data$C_sd
(year and fleet specific, the previous default was 0.01 was built-in for all catches).OM@cpars$LatASD
.