🐍 Further slackening the package by transferring to BNRTools

iiasa · Dec 13, 2024 · 016222e · 016222e
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diff --git a/NAMESPACE b/NAMESPACE
@@ -99,7 +99,6 @@ export(engine_xgboost)
 export(ensemble)
 export(ensemble_partial)
 export(ensemble_spartial)
-export(formatGLOBIOM)
 export(get_data)
 export(get_ngbvalue)
 export(get_priors)

diff --git a/NEWS.md b/NEWS.md
@@ -3,6 +3,7 @@
 #### New features
 
 #### Minor improvements and bug fixes
+* IIASA internal functionalities such as preparation of GLOBIOM data have been transferred to [BNRTools](https://github.com/iiasa/BNRTools)
 
 # ibis.iSDM 0.1.5
 

diff --git a/R/add_predictors_globiom.R b/R/add_predictors_globiom.R
diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -136,7 +136,6 @@ reference:
       - predictor_derivate
       - predictor_filter
       - interpolate_gaps
-      - objects_size
       - run_stan
       - wrap_stanmodel
       - sanitize_names

diff --git a/man/add_predictors_globiom.Rd b/man/add_predictors_globiom.Rd
diff --git a/man/formatGLOBIOM.Rd b/man/formatGLOBIOM.Rd
diff --git a/vignettes/articles/04_biodiversity_projections.Rmd b/vignettes/articles/04_biodiversity_projections.Rmd
@@ -241,9 +241,3 @@ sc.fit2$summary_beforeafter()
 ```
 
 Another option for constraining prediction is also by imposing a zonal limit (for instance climatically defined) on the projections (see alternatively `add_constraint_boundary()` above). This has to be done while fitting the SDM for the reference conditions (see the example with limits ([1](01_train_simple_model.html)) ) and is considered when doing (future) projections.
-
-## Specific parsers for GLOBIOM related scenarios
-
-IIASA's Global Biosphere Management Model ([GLOBIOM](https://iiasa.github.io/GLOBIOM/)) is a partial equilibrium model and used to analyze the competition for land use between agriculture, forestry, and bioenergy, which are the main land-based production sectors. It builds on.
-
-With the ibis.iSDM being part of IIASA's suite of integrated models, there is a direct link available to make use of downscaled GLOBIOM outputs. Implemented are functions to either directly format the data via [`formatGLOBIOM()`] or add them to any `DistributionModel-class` or `BiodiversityScenario-class` object directly via `add_predictors_globiom()` .