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Releases: weecology/portalcasting

portalcasting v0.15.2

19 Dec 07:20
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shifting to github of portalr

to address the backwards incompatibility between the CRAN and GitHub versions of portalr and the need for the newest (GH) version because of the break in portalData

portalcasting v0.15.1

18 Dec 08:39
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patch bump to try zenodo build again (the zenodo build for v0.15.0 failed)

portalcasting v0.15.0

18 Dec 08:13
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JAGS vignette

  • Added a vignette that describes how to use the JAGS/runjags API within portalcasting.
  • addresses

Pulls code for match.call.defaults into the package

  • Use of it from DesignLibrary causes a problematic dependency chain with the docker image building

Patch bug in most_abundant_species

  • Wasn't using the species name function, and so was pulling in the traps column, which was causing a break in plotting.

portalcasting v0.14.0

18 Dec 08:13
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(done retroactively from the branch where this was done)

Adds exclosure data to the prefab models

portalcasting v0.13.0

13 Oct 00:10
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Full writing of control_files in model scripts

  • Previously, the controls list for the files in the model scripts was taken from the environment in which the script was run, which opens the script to everything, which is undesirable.
  • After the need to include a control list for runjags models forced an explicit writing of the list inputs, the code was available to transfer to the files control list.
  • This does mean that the function calls in the scripts are now super long and explicit, but that's ok.
  • To avoid super long model script lines (where event default inputs are repeated in the list functions), a function control_list_arg was made to generalize what was coded up from the runjags list for use also with the files control list. This function writes a script component that only includes arguments to the list function that are different from the formal definition.

portalcasting v0.12.0

12 Oct 06:38
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portalcast updates model scripts according to controls_model

  • Previously, if you changed any controls of a prefab model, you had to manually re-write the models using fill_models before running portalcast.
  • Using fill_models would result in hand-made scripts being overwritten, so a specific function (update_models) for updating the models was created.
  • update_models by default only updates the models listed in the controls_model input, to avoid overwriting model scripts. To change this behavior and also update all of the prefab models' scripts, set update_prefab_models = TRUE. This is particularly handy when changing a global (with respect to model scripts) argument: main, quiet, verbose, or arg_checks.
  • addresses

Messaging around trying to use not-complete directory improved

  • Indication now made that a component of the directory is missing and suggestion is made to run create_dir.
  • addresses

Patching data set bug in plotting

  • There was a bug with matching the interpolated to the non interpolated data sets within the ensembling, which has been fixed.
  • addresses

Updated messaging

  • Moved most of the messaging into tidied functions.

Changed behavior of prep_rodents_table and prep_rodents

  • Now there is no start_moon argument, and all of the data prior to end_moon are returned.
  • This aligns the rodents prep functions with the other (moons, covariates) prep functions.
  • Facilitates use of data prior to start_moon in forecasting models (e.g., for distributions of starting state variables).
  • Requires that model functions now explicitly trim the rodents table being used. This has been added to all prefab models.

Fixed codecov targets

  • Previous targets were restrictively high due to earlier near-perfect coverage.
  • A codecov.yml file is now included in the repo (and ignored for the R build) which sets the target arbitrarily at the still-quite-high-but-not-restrictively-so 95%.
  • It can be changed if needed in the future.

Simple EDM model added

JAGS infrastructure added

  • Using the runjags package, with extensive access to the API of run.jags via a control_runjags list (see runjags_control).
  • Currently in place with a very simple random walk model.
  • addresses

Prepared rodents table includes more content

  • Expanded back in time to the start.
  • Added effort columns (all default options in prefab_rodents_controls have effort = TRUE).

Updated adding a model and data vignette

  • Added section at the end about just extending existing models to new data sets.
  • addresses

v0.11.0

15 Sep 06:16
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Ensembling reintroduced

  • Associated with the reconfiguration of portalcasting from v0.8.1 to 0.9.0, ensembling was removed temporarily.
  • A basic ensemble is reintroduced, now as an unweighted average across all selected models, allowing us to have an ensemble but not have it be tied to AIC weighting (because AIC weighting is no longer possible with the split between interpolated and non-interpolated data for model fitting).
  • In a major departure from v0.8.1 and earlier, the ensemble's output is not saved like the actual models'. Rather, it is only calculated when needed on the fly.
  • In plotting, it is now the default to use the ensemble for plot_cast_ts and plot_cast_point and for the ensemble to be included in plot_casts_err_lead and plot_casts_cov_RMSE.

Return of most_abundant_species

  • Function used to select the most common species.
  • Now uses the actual data and not the casts to determine the species.

v0.10.0

14 Sep 03:03
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Model evaluation and ensembling added back in

  • Were removed with the updated version from 0.8.1 to 0.9.0 to allow time to develop the code with the new infrastructure.
  • Model evaluation happens within the cast tab output as before.

Temporarily removed figures returned

  • Associated with the evaluation.
  • Plotting of error as a function of lead time for multiple species and multiple models. Now has a fall-back arrangement that works for a single species-model combination.
  • Plotting RMSE and coverage within species-model combinations.

Flexing model controls to allow user-defined lists for prefab models

  • For sandboxing with existing models, it is useful to be able to change a parameter in the model's controls, such as the data sets. Previously, that would require a lot of hacking around. Now, it's as simple as inputting the desired controls and flipping arg_checks = FALSE.

v0.9.0

06 Sep 07:42
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Major API update: increase in explicit top-level arguments

  • Moved key arguments to focal top-level inputs, rather than nested within control options list. Allows full control, but with default settings working cleanly. addresses
  • Restructuring of the controls lists, retained usage in situations where necessary: model construction, data set construction, file naming, climate data downloading.
  • Openness for new setup functions, in particular setup_sandbox. addresses
  • Simplification of model naming inputs. Just put the names in you need, only use the model_names functions when you need to (usually in coding inside of functions or for setting default argument levels). addresses

Directory tree structure simplified

  • dirtree was removed
  • base (both as a function and a concept) was removed. To make that structure use main = "./name"
  • "PortalData" has been removed as a sub and replaced with "raw", which includes all raw versions of files (post unzipping) downloaded: Portal Data and Portal Predictions and covariate forecasts (whose saving is also new here).

Tightened messaging

  • Expanded use of quiet and verbose connected throughout the pipeline.
  • Additional messaging functions to reduce code clutter.
  • Formatting of messages to reduce clutter and highlight the outline structure.

Download capacity generalized

  • Flexible interface to downloading capacity through a url, with generalized and flexible functions for generating Zenodo API urls (for retrieving the raw data and historical predictions) and NMME API urls (for retrieving weather forecasts) to port into the download function. addresses and addresses and addresses

Changes for users adding their own models to the prefab set

  • Substantial reduction in effort for users who wish to add models (i.e. anyone who is sandboxing). You can even just plunk your own R script (which could be a single line calling out to an external program if desired) without having to add any model script writing controls, and just add the name of the model to the models argument in portalcast and it will run it with everything else.
  • Outlined in the updated Getting Started and Adding a Model/Data vignettes.
  • Users adding models to the prefab suite should now permanently add their model's control options to the source code in model_script_controls rather than write their own control functions.
  • Users adding models to the prefab suite should permanently add their model's function code to the prefab_models script (reusing and adding to the documentation in prefab_model_functions), rather than to its own script.
  • Users should still add their model's name to the source code in model_names.

Relaxed model requirements

  • Models are no longer forced to use interpolated data.
  • Models are no longer required to output a rigidly formatted data-table. Presently, the requirement is just a list, but soon some specifications will be added to improve reliability.
  • Outlined in the updated Adding a Model/Data vignette.

More organization via metadata

  • Generalized cast output is now tracked using a unique id in the file name associated with the cast, which is related to a row in a metadata table, newly included here. addresses and addresses and addresses
  • Additional control information (like data set setup) is sent to the model metadata and saved out.
  • Directory setting up configuration information is now tracked in a dir_config.yaml file, which is pulled from to save information about what was used to create, setup, and run the particular casts.

Changes for users interested in analyzing their own data sets not in the standard data set configuration

  • Users are now able to define rodent observation data sets that are not part of the standard data set ("all" and "controls", each also with interpolation of missing data) by giving the name in the data_sets argument and the controls defining the data set (used by portalr's summarize_rodent_data function) in the controls_rodents argument.
  • In order to actualize this, a user will need to flip off the argument checking (the default in a sandbox setting, if using a standard or production setting, set arg_checks = FALSE in the relevant function).
  • Users interested in permanently adding the treatment level to the available data sets should add the source code to the rodents_controls function, just like with the models.
  • addresses
  • Internal code points the pipeline to the files named via the data set inputs. The other data files are pointed to using the control_files (see file_controls) input list, which allows for some general flexibility with respect to what files the pipeline is reading in from the data subdirectory.

Split of standard data sets

  • The prefab all and controls were both default being interpolated for all models because of the use of AIC for model comparison and ensemble building. That forced all models to use interpolated data.
  • Starting in this version, the models are not required to have been fit in the same fashion (due to generalization of comparison and post-processing code), and so interpolation is not required if not needed, and we have split out the data to standard and interpolated versions.

Application of specific models to specific data sets now facilitated

  • write_model and model_template have a data_sets argument that is used to write the code out, replacing the hard code requirement of analyzing "all" and "controls" for every model. Now, users who wish to analyze a particular data component can easily add it to the analysis pipeline.

Generalization of code terms

  • Throughout the codebase, terminology has been generalized from "fcast"/"forecast"/"hindcast" to "cast" except where a clear distinction is needed (here primarily due to where the covariate values used come from).
  • Nice benefits: highlights commonality between the two (see next section) and reduces code volume.
  • start_newmoon is now start_moon like end_moon
  • addresses

"Hindcasting" becomes more similar to "forecasting"

  • In the codebase now, "hindcasting" is functionally "forecasting" with a forecast origin (end_moon) that is not the most recently occurring moon.
  • Indeed, "hindcast" is nearly entirely removed from the codebase and "forecast" is nearly exclusively retained in documentation (and barely in the code itself), with both functionally being replaced with the generalized (and shorter) "cast".
  • cast_type is retained in the metadata file for posterity, but functionality is more generally checked by considering end_moon and last_moon in combination, where end_moon is the forecast origin and last_moon is the most recent
  • Rather than the complex machinery used to iterate through multiple forecasts ("hindcasting") that involved working backwards and skipping certain moons (which didn't need to be skipped anymore due to updated code from a while back that allows us to forecast fine even without the most recent samples yet), a simple for loop is able to manage iterating. This is also facilitated by the downloading of the raw portalPredictions repository from Zenodo and critically its retention in the "raw" subdirectory, which allows quick re-calculation of historic predictions of covariates. addresses
  • cast_type has been removed as an input, it's auto determined now based on end_moon and the last moon available (if they're equal it's a "forecast", if not it's a "hindcast").

Softer handling of model failure

  • Within cast, the model scripts are now sourced within a for-loop (rather than sapply) to allow for simple error catching of each script. addresses

Improved argument checking flow

  • Arg checking is now considerably tighter, code-wise.
  • Each argument is either recognized and given a set of attributes (from an internally defined list) or unrecognized and stated to the user that it's not being checked (to help notify anyone building in the code that there's a new argument).
  • The argument's attributes define the logical checking flow through a series of pretty simple options.
  • There is also now a arg_checks logical argument that goes into check_args to turn off all of the underlying code, enabling the user to go off the production restrictions that would otherwise through errors, even though they might technically work under the hood.

Substantial re-writes of the vignettes

  • Done in general to update with the present version of the codebase.
  • Broke the adding a model or data vignette into "working locally" and "adding to the pipeline", also added checklists and screen shots. addresses
  • Reorganized the getting started vignette to an order that makes sense. addresses

Post-processing (evaluation and ensemble building) temporarily removed

  • T...
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portalcasting v0.8.1

11 Jul 22:40
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hook up with zenodo and some other minor documentation edits
no coding changes