NEWS

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spict 1.3.8 (2023-12-08)

New features

• Added function calc.process.resid() that calculates the process residuals for the biomass and fishing mortality processes. The residuals are added to the fitted spict object in the data frame called rep$process.resid. The residuals can be summarised with the function sumspict.diagnostics() and visualised with the function plotspict.diagnostic.process(). Note, that the process residuals have mainly been tested for scenarios without seasonal fishing patterns.

• Added function plotspict.compare that shows estimates from multiple fits, of B, F, catch, F/Fmsy, B/Bmsy and the production curve on the same plot. The fits can be given separately, like plotspict.compare(fit1, fit2) or in a list, like plotspict.compare(list(fit1, fit2)).

Bug fixes

• make.man.inp now checks all fractiles to be between 0 and 0.5 (closes #167)
• add.manlines correctly reports shorter management periods than 1 year (closes #168)
• add.manlines checks for errflag for multiple management scenarios
• annual function was rewritten to accommodate cases where intime does not include full years. This happens when the first data point is not on a full year (closes #170, thanks to Paz Sampedro)
• man.select checks for NAs for multiple management scenarios
• summary now respects the CI argument. Added CI argument for sumspict.* functions

Minor changes

• Retro runs have names: Baseline, -1, -2, -3, ... (closes #172)

spict 1.3.7 (2022-09-06)

New features

• Added functionality for performing and plotting hindcast analysis introducing the functions hindcast() and plotspict.hindcast.

Bug fixes:

• fixed retro when last year's observations where missing

spict 1.3.6 (2022-06-06)

Bug fixes:

• sim.spict saves the dtc (and dte) (closes #159)
• Fixed optimisation of absolute catch management scenario
• Fixed plotting of simulated data

spict 1.3.5 (2021-09-09)

New features:

• Use TMB simulation functionality to simulate data from fitted spict object. Set argument use.tmb = TRUE in sim.spict() function to use TMB SIMULATE{}.
• All functions using parallel::mclapply() have the argument mc.cores to specify the number of cores to be used. By default mc.cores is equal to 1.

Bug fixes

• Default labeling of management scenarios was not using increasing numbers, but reused the label "cutsomScenario_1". This is corrected and increasing numbers are now used, i.e. "customScenario_2", etc.
• In the case there is an abundance index after the last catch interval inside the intermediate period and the argument intermediatePeriodCatch in manage() is used, the intermediate period was calculated incorrectly, using the argument inp$indpred rather than inp$indCpred. This also led to an error message in plotspict.catch() with these management scenarios.

Minor changes:

• More informative error messages and user-friendly functionality of sim.spict()
• Three new arguments in the inp list (created by check.inp): sim.random.effects that allows to turn the simulation of random effects on and off, and sim.fit that allows to define whether the estimated parameters from the last fit or initial values should be used for simulation.
• The application of the retro function in the vignette was set to mc.cores=1 to circumvent multithread MKL and parallel problems.

spict 1.3.4 (2021-02-21)

New features:

• Option to set Blim in the hockey-stick HCR to anything else than 0, by specifying two values in breakpointB, e.g. breakpointB = c(0.3,0.5).
• New argument to set time for the evaluation of the hockey-stick HCR: evalBreakpointB.
• New plotting function: plotspict.hcr that visualises management scenarios
• See changes for each spict version using news(package = "spict")

Bug fixes

• Corrected biomass predictions for scenarios that use catch fractiles

Minor changes:

• Updated documentation, vignettes and guidelines

spict 1.3.3

Minor changes:

• Extra arguments sumspict.manage control what is returned (uncertainty, absolute states)
• sumspict.manage shows informative messages when scenarios are not comparable due to assumption differences
• Management scenarios targeting absolute catch (cabs) or fishing mortality (fabs). Absolute catch scenarios are based on finding the fishing mortalirt (ffac) that leads to the target catch using a simple optimisation.
• Management scenarios stored in the input object
• Legend in most plots have transparent backgrounds

Bug fixes:

• Fixed previously ignored argument include.Ebinf in sumspict.manage

spict 1.3.2

Minor changes:

• TMB version 1.7.1 is required
• Package ellipse is now imported
• Removed plyr dependency

Bug fixes:

• Fixed test.spict to work with the management updates

spict 1.3.1

New features:

• New function: plotspict.retro.fixed. Plots parameter estimates (and confidence intervals) in each of the retrospective runs

Minor changes:

• plotspict.retro returns Mohn's rho if add.mohn is TRUE
• plotspict.retro was polished, added legend, better spacing
• mohns_rho now works for the predicted index Ipred
• Retro plotting and Mohn's rho remove not converged retro runs (closes #49)
• Reduced the size of retro object and added an argument to control it (closes #97)
• Added argument checks in retro, plotspict_retro and mohns_rho

Bug fixes:

• Fixed Mohn's rho calculation that was ignoring the base run
• Fixed bug in Mohn's rho calculation that included forecast period
• Fixed shorten.inp for catch and effort

spict 1.3.0 (2020-10-23)

New features:

• The forecast & management section in SPiCT handbook was updated and describes the new management functionality of the package.
• Two new variables for controlling management-related settings are introduced: inp$maninterval and inp$maneval. The first is comprised of two numeric values which define the start and end of the management period, i.e. the period to which fishing mortality or catch restrictions are applied to and for which the catch (or TAC) is predicted for. inp$maneval defines the management evaluation time, i.e. the time point at which model states should be predicted for (corresponds to the 'p' quantities in the model, e.g. logBpBmsy). Thus, these variables replace the deprecated variables 'timepredc', 'dtpredc', and 'manstart'. The new variables have a higher priority than the older ones, but old variables can still be used.
• With the new management variables come new default values for the management period, length of the period, and evaluation time: The management period starts now by default at the start of the next full new year after the last observation. This fixes a bug concerning default values for specific cases with seasonal catches (see below). Independent of annual or seasonal catches, the default duration of the management period is now 1 year. The default management evaluation time is at the end of the management period, i.e. by default at the end of the next year after the last observation. Please note that these changes might lead to different values for some predicted quantities (e.g. 'Predictions' in the summary output of a SPiCT assessment), because they affect the 'p' states in the model, e.g. logBp.
• The whole management functionality of the package is updated, including:
  • New management scenario labelled 'ices' in the manage() function
  • New function add.man.scenario() to add customised scenarios to rep$man
  • New function get.TAC() to estimate TAC of a specified scenario
  • New function man.tac() to estimate TAC of all scenarios in rep$man
  • New function man.timeline() to visualise management times in form of a timeline
  • New function sumspict.manage() to print a summary of all management scenarios
  • New function check.man.time() to check and correct model times in respect to management times
  • New function man.select() to select certain scenarios from rep$man
• New function plot2() allowing to plot a selection of 4 plots: biomass relative to Bmsy, Fishing mortality relative to Fmsy, catches, and the Kobe plot.
• New argument 'verbose' in all management-related functions, fit.spict() and check.inp() which allows to turn off console printing. This may be relevant for automatic reports and parallel scripts. By default verbose = TRUE and informative text messages and warnings are printed to the console.
• The new argument 'inp$reportmode' allows to change the number of ADreported quantities. So far, the default option (0) which reports all quantities, a minimal selection of quantities relevant for management (1), and the option to only report the predicted catch (logCp with inp$reportmode == 2) are implemented.
• A new family of ADreported quantities is introduced: The 'm' quantities, e.g. logFmFmsy, correspond to the start of the management period.
• New function mohns_rho that calculates Mohn's rho from the retrospective analysis of user selected quantities

Minor changes:

• The guidelines vignette was updated.
• New test scripts for all management-related functions.
• The manbase scenario was removed.
• The manage() function overwrites all scenarios in rep$man.
• The quantities 'perc.dF' and 'perc.dB' in the management summary output relate the fishing mortality or biomass, respectively, from the end of the management period to the start of the management period of each scenario. Thus, these quantities are omitted from the summary output if the timelines of the scenarios vary.
• DLMtool dependent functions were removed.

Bug fixes:

• The argument inp$indlastobs which is used to estimate all 'l' quantities, e.g. logFlFmsy, did not consider the length of the catch (or effort) interval. Fixing this bug might lead to different values for some quantities (e.g. 'States' in the summary output of a SPiCT assessment), because they affect the 'l' states in the model, e.g. logBl.
• The new default values of management-related variables explained above fix inconsistent default values for seasonal data, which might have lead 'manstart' to be after 'timepredc'.
• Fixed bug in retro function when effort data are used.
• Allow to plot into same plotting device for all plotspict.* family plots, e.g. for changing the ylim or adding a horizontal reference line.
• All management scenarios allow for the definition of intermediate periods (period between last observation and start of the management period). By default the assumptions during the intermediate period are equal (continuing the F process) for all scenarios.
• The argument 'maxtime' in the function shorten.inp() did not account for the length of the catch (or effort) interval. Thus, a catch observation for the interval 1990-1991 was included although maxtime = 1990. After this bug fix, this observation is not included anymore as it ranges beyond 'maxtime'.

spict 1.2.8 (2019-12-02)

New features:

• Added new vignette: "Guidelines for the use of the stochastic production model in continuous time (SPiCT)"
• Added helper function calc.bmsyk that returns the Bmsy / K ratio of a fitted object
• Added helper function calc.om that return a matrix with the order of magnitude of the confidence interval range of F/Fmsy and B/Bmsy
• Added helper function shorten.inp that cuts the input time series to the specified range
• Added option for gamma distributed prior for the parameter determining the shape of the production (n) curve on the log scale
• Added option to report the states in terms of biomass and fishing mortality relative to the overall average over the whole time series (including the prediction period) Brel and Frel, respectively

Minor changes:

• Added sdfac argument in manage that sets the standard deviation factor keep current catch scenario
• Increased maximum number of iterations and function evaluations of the nlminb optimiser
• Increased tested code coverage

Bug fixes:

• Use non seasonal F in manage
• Use better default value for the standard deviation factor in take.c
• Remove inverse gamma priors which were not fully functional

spict 1.2.7 (2019-04-03)

New features:

• Added new management scenario: ICES MSY advice rule
• Added NEWS file

Minor changes:

• Updated documentation for check.inp and spict2DLMtool
• Makefile now takes the version number from DESCRIPTION file