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The latest release of the SAMtool package is available on CRAN.
OM@cpars$Perr_ycompare_RCM when fitting
to StockPars list.RCM rescales selectivity to maximum of 1 when
converting from length to age unitsdiagnostic uses match() instead of
grep() to find the correct MP. grep can return
more than one match.RCM2MOM (introduced in
1.6.5)@Misc to new slot
@report. @Misc is now available for users to
record information from OM building depending on the case study.StockPars
instead of an operating model with RCM(StockPars, RCMdata).
See StockPars section in help("RCM").RCM with argument pbc_earlyrecdev. The bias
correction from logspace to normal space is
exp(log_early_rec_dev[a] - 0.5 * pbc_earlyrecdev[a] * sigmaR^2)
if the year-specific rec dev is estimated.RCMdata@I_delta (survey timing within time step) can
now be a matrix by year and survey.Shortcut functionRCM in argument pbc_recdev. The bias
correction from logspace to normal space is
exp(log_rec_dev[y] - 0.5 * pbc_recdev[y] * sigmaR^2) if the
year-specific rec dev is estimated.RCMdata@I_delta slot to specify survey timing (0-1)
within year. Default assumption is zero is used which is consistent with
previous versions.RCM2MOM (report selectivity at length array,
catch fractions).RCM_output tab in plot(RCModel),
report simulations and convergence rate, and clean up comp plots.RCM(condition = "catch2"),
introduced in version 1.6.0.RCM updates to calculate fishery length comp when
CAL_n > 0 and protect plot.RCM from empty
index vectors.Perfect uses spawn_timing to calculate spawning biomass
(exp(-spawn_time * M)) in the middle of projection year.
Note that perfect HCR implementation needs to iteratively re-calculate
the projection year biomass, B/BMSY, B/B0
(exp(-spawn_time * [Ftarget + M])) when applying the HCR.
Recommend spawn_time = 0 to implement HCR perfectly.ObsPars$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 .HCRhist 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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.