Function reference
Setting up and train models
Key functions for setting up species distribution models and adding information to them. Start with distribution() and see articles examples on how to build a model from there. Also includes functions to specify priors for a model.
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distribution()
- Create distribution modelling procedure
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BARTPrior()
- Create a tree-based split probability prior for BART
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BARTPriors()
- Helper function when multiple variables are supplied for a BART prior
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BREGPrior()
- Create a new spike and slab prior for Bayesian generalized linear models
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BREGPriors()
- Helper function when multiple variables are supplied for a BREG prior
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GDBPrior()
- Monotonic constrained priors for boosted regressions
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GDBPriors()
- Helper function when multiple variables are supplied for a GDB prior
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GLMNETPrior()
- Regression penalty priors for GLMNET
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GLMNETPriors()
- Helper function when multiple variables are supplied for a GLMNET prior
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INLAPrior()
- Create a new INLA prior
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INLAPriors()
- Helper function when multiple variables and types are supplied for INLA
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STANPrior()
- Create a new STAN prior
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STANPriors()
- Helper function when multiple variables and types are supplied for STAN
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XGBPrior()
- Create a new monotonic prior for boosted regressions
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XGBPriors()
- Helper function when multiple variables are supplied for XGBOOST
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Prior-class
Prior
- Base Prior class
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PriorList-class
PriorList
- List of Priors supplied to an class
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print(<distribution>)
print(<BiodiversityDistribution>)
print(<BiodiversityDatasetCollection>)
print(<BiodiversityDataset>)
print(<PredictorDataset>)
print(<DistributionModel>)
print(<BiodiversityScenario>)
print(<Prior>)
print(<PriorList>)
print(<Engine>)
print(<Settings>)
print(<Log>)
print(<Id>)
print(<Id>)
print(<tbl_df>)
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summary(<distribution>)
summary(<DistributionModel>)
summary(<PredictorDataset>)
summary(<BiodiversityScenario>)
summary(<PriorList>)
summary(<Settings>)
- Summarises a trained model or predictor object
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pseudoabs_settings(<ANY>,<numeric>,<numeric>,<character>,<numeric>,<logical>,<logical>,<ANY>)
- Settings for specifying pseudo-absence points within the model background
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train()
- Train the model from a given engine
Add or modify data and parameters
Functions to add or modify data and parameters in a distribution object. These can be used to add or remove biodiversity, covariates and priors in various forms.
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add_biodiversity_poipa()
- Add biodiversity point dataset to a distribution object (presence-absence).
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add_biodiversity_poipo()
- Add biodiversity point dataset to a distribution object (presence-only)
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add_biodiversity_polpa()
- Add biodiversity polygon dataset to a distribution object (presence-absence)
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add_biodiversity_polpo()
- Add biodiversity polygon dataset to a distribution object (presence-only)
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add_constraint(<BiodiversityScenario>,<character>)
- Add a constraint to an existing
scenario
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add_constraint_MigClim(<BiodiversityScenario>,<character>,<numeric>,<numeric>,<character>,<numeric>,<numeric>,<numeric>,<numeric>,<numeric>,<character>)
- Add constrains to the modelled distribution projection using the MigClim approach
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add_constraint_adaptability(<BiodiversityScenario>,<character>,<character>,<numeric>,<numeric>)
- Adds an adaptability constraint to a scenario object
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add_constraint_boundary(<BiodiversityScenario>,<sf>,<character>)
add_constraint_boundary(<BiodiversityScenario>,<ANY>,<character>)
- Adds a boundary constraint to a scenario object
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add_constraint_connectivity(<BiodiversityScenario>,<character>,<numeric>,<ANY>)
- Adds a connectivity constraint to a scenario object.
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add_constraint_dispersal(<BiodiversityScenario>,<character>,<numeric>)
- Adds a dispersal constrain to a scenario object.
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add_constraint_minsize(<BiodiversityScenario>,<numeric>,<character>,<logical>)
- Adds a size constraint on a scenario
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add_control_bias()
- Add a control to a BiodiversityModel object to control biases
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add_control_extrapolation()
- Add a control to a BiodiversityModel object to control extrapolation
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add_latent_spatial()
- Add latent spatial effect to the model equation
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add_log()
- Adds a log file to distribution object
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add_offset()
- Specify a spatial explicit offset
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add_offset_bias()
- Specify a spatial explicit offset as bias
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add_offset_elevation()
- Specify elevational preferences as offset
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add_offset_range()
- Specify a expert-based species range as offset
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add_predictor_elevationpref()
- Create lower and upper limits for an elevational range and add them as separate predictors
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add_predictor_range()
- Add a range of a species as predictor to a distribution object
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add_predictors()
- Add predictors to a Biodiversity distribution object
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add_predictors_globiom()
- Add GLOBIOM-DownScaleR derived predictors to a Biodiversity distribution object
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add_predictors_model()
- Add predictors from a fitted model to a Biodiversity distribution object
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add_priors()
- Add priors to an existing distribution object
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add_pseudoabsence()
- Add pseudo-absence points to a point data set
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set_priors()
- Add priors to an existing distribution object
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sel_predictors()
- Select specific predictors from a distribution object
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rm_predictors()
- Remove specific predictors from a distribution object
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rm_priors()
- Remove existing priors from an existing distribution object
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get_data()
- Small helper function to obtain predictions from an object
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get_ngbvalue()
- Function to extract nearest neighbour predictor values of provided points
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get_priors()
- Create priors from an existing distribution model
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get_rastervalue()
- Function to extract directly the raster value of provided points
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engine_bart()
- Engine for use of Bayesian Additive Regression Trees (BART)
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engine_breg()
- Engine for Bayesian regularized regression models
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engine_gdb()
- Use of Gradient Descent Boosting for model estimation
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engine_glmnet()
- Engine for regularized regression models
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engine_inla()
- Use INLA as engine
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engine_inlabru()
- Use inlabru as engine
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engine_stan()
- Use Stan as engine
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engine_xgboost()
- Engine for extreme gradient boosting (XGBoost)
Create spatial-temporal projections
After a model has been trained, the functions in here can be used to create projections with scenario() objects. Constraints can be on such scenarios to limit extrapolations.
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scenario()
- Create a new scenario based on trained model parameters
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project(<BiodiversityScenario>)
project(<BiodiversityScenario>,<character>,<logical>,<character>,<character>,<logical>)
- Project a fitted model to a new environment and covariates
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simulate_population_steps(<BiodiversityScenario>,<matrix>,<numeric>,<ANY>,<ANY>,<ANY>,<ANY>,<logical>)
- Simulate population dynamics following the steps approach
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add_constraint(<BiodiversityScenario>,<character>)
- Add a constraint to an existing
scenario
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add_constraint_MigClim(<BiodiversityScenario>,<character>,<numeric>,<numeric>,<character>,<numeric>,<numeric>,<numeric>,<numeric>,<numeric>,<character>)
- Add constrains to the modelled distribution projection using the MigClim approach
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add_constraint_adaptability(<BiodiversityScenario>,<character>,<character>,<numeric>,<numeric>)
- Adds an adaptability constraint to a scenario object
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add_constraint_boundary(<BiodiversityScenario>,<sf>,<character>)
add_constraint_boundary(<BiodiversityScenario>,<ANY>,<character>)
- Adds a boundary constraint to a scenario object
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add_constraint_connectivity(<BiodiversityScenario>,<character>,<numeric>,<ANY>)
- Adds a connectivity constraint to a scenario object.
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add_constraint_dispersal(<BiodiversityScenario>,<character>,<numeric>)
- Adds a dispersal constrain to a scenario object.
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add_constraint_minsize(<BiodiversityScenario>,<numeric>,<character>,<logical>)
- Adds a size constraint on a scenario
Model summary and validation
Key functions to summarize, validate or extract information from trained models.
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plot(<DistributionModel>)
plot(<BiodiversityDatasetCollection>)
plot(<PredictorDataset>)
plot(<Engine>)
plot(<BiodiversityScenario>)
- Plot wrappers
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print(<distribution>)
print(<BiodiversityDistribution>)
print(<BiodiversityDatasetCollection>)
print(<BiodiversityDataset>)
print(<PredictorDataset>)
print(<DistributionModel>)
print(<BiodiversityScenario>)
print(<Prior>)
print(<PriorList>)
print(<Engine>)
print(<Settings>)
print(<Log>)
print(<Id>)
print(<Id>)
print(<tbl_df>)
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summary(<distribution>)
summary(<DistributionModel>)
summary(<PredictorDataset>)
summary(<BiodiversityScenario>)
summary(<PriorList>)
summary(<Settings>)
- Summarises a trained model or predictor object
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coef(<DistributionModel>)
- Obtains the coefficients of a trained model
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validate(<ANY>,<character>,<sf>,<character>,<character>)
validate(<SpatRaster>,<character>,<sf>,<character>)
- Validation of a fitted distribution object
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similarity()
- Calculate environmental similarity of reference datasets to predictors.
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effects(<DistributionModel>)
- Plot effects of trained model
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partial()
- Obtain partial effects of trained model
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spartial()
- Obtain spatial partial effects of trained model
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partial_density()
- Visualize the density of the data over the environmental data
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limiting()
- Identify local limiting factor
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threshold()
- Threshold a continuous prediction to a categorical layer
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ensemble(<ANY>,<character>,<numeric>,<numeric>,<character>,<logical>,<character>)
- Function to create an ensemble of multiple fitted models
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ensemble_partial(<ANY>,<character>,<character>,<character>,<ANY>,<logical>)
- Function to create an ensemble of partial effects from multiple models
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ensemble_spartial(<ANY>,<character>,<character>,<character>,<ANY>,<numeric>,<logical>)
- Function to create an ensemble of spartial effects from multiple models
Utility functions
These functions are used by engines or spatial processing in the package. Most of them are for internal use, but can be of use if input needs to be reformatted.
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posterior_predict_stanfit()
- Create a posterior prediction from a rstanfit object
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alignRasters()
- Align a
SpatRaster
object to another by harmonizing geometry and extend.
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emptyraster()
- Create an empty
SpatRaster
based on a template
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get_ngbvalue()
- Function to extract nearest neighbour predictor values of provided points
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get_rastervalue()
- Function to extract directly the raster value of provided points
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predictor_transform()
- Spatial adjustment of environmental predictors and raster stacks
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predictor_derivate()
- Create spatial derivative of raster stacks
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predictor_filter()
- Filter a set of correlated predictors to fewer ones
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run_stan()
- Fit cmdstanr model and convert to rstan object
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wrap_stanmodel()
- Wrap a list with stan model code
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sanitize_names()
- Sanitize variable names
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get_data()
- Small helper function to obtain predictions from an object
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stancode(<DistributionModel>)
- Show the stan code from a trained model
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write_model(<ANY>,<character>,<logical>,<logical>)
- Save a model for later use
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write_output(<ANY>,<character>,<character>,<logical>)
write_output(<BiodiversityScenario>,<character>,<character>,<logical>)
write_output(<SpatRaster>,<character>,<character>,<logical>)
write_output(<data.frame>,<character>,<character>,<logical>)
write_output(<stars>,<character>,<character>,<logical>)
- Generic function to write spatial outputs
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write_stanmodel()
- Write a cmdstanr model output to a specific file
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write_summary(<ANY>,<character>,<logical>,<logical>)
- Generic function to write summary outputs from created models.
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load_model(<character>,<logical>)
- Load a pre-computed model
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formatGLOBIOM()
- Function to format a prepared GLOBIOM netCDF file for use in Ibis.iSDM
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mask(<DistributionModel>)
mask(<BiodiversityDatasetCollection>)
mask(<PredictorDataset>)
mask(<BiodiversityScenario>)
- Mask data with an external layer
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myLog()
- Custom messaging function for scripts
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predictor_homogenize_na()
- Homogenize NA values across a set of predictors.
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thin_observations()
- Functionality for geographic and environmental thinning
Class definitions and methods
These pages document the package’s internal data structures and functions for manipulating them—they contain information that is really only useful when adding new functionality to the package.
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BiodiversityDataset-class
BiodiversityDataset
- BiodiversityDataset prototype description
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BiodiversityDatasetCollection-class
BiodiversityDatasetCollection
- BiodiversityDatasetCollection super prototype description
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BiodiversityDistribution-class
BiodiversityDistribution
- Biodiversity Distribution master class
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BiodiversityScenario-class
BiodiversityScenario
- Prototype for a biodiversity scenario from a trained model
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DistributionModel-class
DistributionModel
- Prototype for the trained Model object
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Engine-class
Engine
- Engine prototype.
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Prior-class
Prior
- Base Prior class
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PriorList-class
PriorList
- List of Priors supplied to an class
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Settings-class
Settings
- Prototype for model settings object
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PredictorDataset-class
PredictorDataset
- PredictorDataset prototype description
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as.Id()
- As Id
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is.Id()
is.Waiver()
is.formula()
is.Raster()
is.stars()
- Check whether a provided object is truly of a specific type
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check()
- Check objects in the package for common errors or issues
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bivplot()
- Bivariate plot wrapper for distribution objects
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ibis_dependencies()
- Install ibis dependencies
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ibis_future()
- Options to set up ibis for parallel processing with future
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ibis_options()
- Print ibis options
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new_id()
- Identifier
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new_waiver()
- Waiver
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plot(<DistributionModel>)
plot(<BiodiversityDatasetCollection>)
plot(<PredictorDataset>)
plot(<Engine>)
plot(<BiodiversityScenario>)
- Plot wrappers
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print(<distribution>)
print(<BiodiversityDistribution>)
print(<BiodiversityDatasetCollection>)
print(<BiodiversityDataset>)
print(<PredictorDataset>)
print(<DistributionModel>)
print(<BiodiversityScenario>)
print(<Prior>)
print(<PriorList>)
print(<Engine>)
print(<Settings>)
print(<Log>)
print(<Id>)
print(<Id>)
print(<tbl_df>)
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myLog()
- Custom messaging function for scripts