{ "_id": "6a22774bcd65a98ecbd4b50b", "Package": "ibis.iSDM", "Type": "Package", "Title": "Modelling framework for integrated biodiversity distribution\nscenarios", "Version": "0.1.7", "Authors@R": "c(person(given = \"Martin\",\nfamily = \"Jung\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"jung@iiasa.ac.at\",\ncomment = c(ORCID = \"0000-0002-7569-1390\")),\nperson(given = \"Maximilian H.K.\",\nfamily = \"Hesselbarth\",\nrole = c(\"ctb\"),\nemail = \"hesselbarth@iiasa.ac.at\",\ncomment = c(ORCID = \"0000-0003-1125-9918\"))\n)", "Maintainer": "Martin Jung ", "Description": "Integrated framework of modelling the distribution of\nspecies and ecosystems in a suitability framing. This package\nallows the estimation of integrated species distribution models\n(iSDM) based on several sources of evidence and provided\npresence-only and presence-absence datasets. It makes heavy use\nof point-process models for estimating habitat suitability and\nallows to include spatial latent effects and priors in the\nestimation. To do so 'ibis.iSDM' supports a number of engines\nfor Bayesian and more non-parametric machine learning\nestimation. 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"topics": [ "check", "check,ANY-method" ] }, { "page": "coef", "title": "Obtains the coefficients of a trained model", "topics": [ "coef", "coef.DistributionModel" ] }, { "page": "combine_formulas", "title": "Combine or concatenate multiple formula objects", "topics": [ "combine_formulas" ] }, { "page": "distribution", "title": "Create distribution modelling procedure", "topics": [ "distribution", "distribution,sf-method", "distribution,SpatRaster-method" ] }, { "page": "DistributionModel-class", "title": "Class for the trained Model object", "topics": [ "DistributionModel", "DistributionModel-class" ] }, { "page": "effects", "title": "Plot effects of trained model", "topics": [ "effects", "effects.DistributionModel" ] }, { "page": "emptyraster", "title": "Create an empty 'SpatRaster' based on a template", "topics": [ "emptyraster" ] }, { "page": "engine_bart", "title": "Engine for use of Bayesian Additive Regression Trees (BART)", "concept": [ "engine" ], "topics": [ "engine_bart" ] 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"engine" ], "topics": [ "engine_stan" ] }, { "page": "engine_xgboost", "title": "Engine for extreme gradient boosting (XGBoost)", "concept": [ "engine" ], "topics": [ "engine_xgboost" ] }, { "page": "Engine-class", "title": "Engine class description", "topics": [ "Engine", "Engine-class" ] }, { "page": "ensemble", "title": "Function to create an ensemble of multiple fitted models", "topics": [ "ensemble", "ensemble,ANY-method" ] }, { "page": "ensemble_partial", "title": "Function to create an ensemble of partial effects from multiple models", "topics": [ "ensemble_partial", "ensemble_partial,ANY-method" ] }, { "page": "ensemble_spartial", "title": "Function to create an ensemble of spartial effects from multiple models", "topics": [ "ensemble_spartial", "ensemble_spartial,ANY-method" ] }, { "page": "GDBPrior", "title": "Monotonic constrained priors for boosted regressions", "concept": [ "prior" ], "topics": [ "GDBPrior", "GDBPrior,character-method" ] }, { "page": "GDBPriors", "title": "Helper function when multiple variables are supplied for GDB priors", "concept": [ "prior" ], "topics": [ "GDBPriors", "GDBPriors,character-method" ] }, { "page": "get_data", "title": "Small helper function to obtain predictions from an object", "topics": [ "get_data", "get_data,ANY-method" ] }, { "page": "get_ngbvalue", "title": "Function to extract nearest neighbour predictor values of provided points", "topics": [ "get_ngbvalue" ] }, { "page": "get_priors", "title": "Create priors from an existing distribution model", "concept": [ "prior" ], "topics": [ "get_priors", "get_priors,ANY,character-method" ] }, { "page": "get_rastervalue", "title": "Function to extract point values directly from a SpatRaster", "topics": [ "get_rastervalue" ] }, { "page": "GLMNETPrior", "title": "Regression penalty priors for GLMNET", "concept": [ "prior" ], "topics": [ "GLMNETPrior", "GLMNETPrior,character-method" ] }, { "page": "GLMNETPriors", "title": "Helper function when multiple variables are supplied for GLMNET priors", "concept": [ "prior" ], "topics": [ "GLMNETPriors", "GLMNETPriors,character-method" ] }, { "page": "ibis_dependencies", "title": "Install ibis dependencies", "topics": [ "ibis_dependencies" ] }, { "page": "ibis_enable_parallel", "title": "Set the parallel processing flag to TRUE", "topics": [ "ibis_enable_parallel" ] }, { "page": "ibis_future", "title": "Internal function to enable (a)synchronous parallel processing", "topics": [ "ibis_future" ] }, { "page": "ibis_options", "title": "Print ibis options", "topics": [ "ibis_options" ] }, { "page": "ibis_set_strategy", "title": "Set the number of threads for parallel processing.", "topics": [ "ibis_set_strategy" ] }, { "page": "ibis_set_threads", "title": "Set the threads for parallel processing.", "topics": [ "ibis_set_threads" ] }, { "page": "INLAPrior", "title": "Create a new INLA prior", "concept": [ "prior" ], "topics": [ "INLAPrior", "INLAPrior,character,character-method" ] }, { "page": "INLAPriors", "title": "Helper function when multiple variables and types are supplied for INLA priors", "concept": [ "prior" ], "topics": [ "INLAPriors", "INLAPriors,vector,character-method" ] }, { "page": "interpolate_gaps", "title": "Approximate missing time steps between dates", "topics": [ "interpolate_gaps" ] }, { "page": "is.formula", "title": "Check whether a formula is valid", "topics": [ "is.formula" ] }, { "page": "is.Id", "title": "Check whether a provided object is truly of a specific type", "topics": [ "is.Id" ] }, { "page": "is.Raster", "title": "Tests if an input is a SpatRaster object.", "topics": [ "is.Raster" ] }, { "page": "is.stars", "title": "Tests if an input is a stars object.", "topics": [ "is.stars" ] }, { "page": "is.Waiver", "title": "Is the provided object of type waiver?", "topics": [ "is.Waiver" ] }, { "page": "limiting", "title": "Identify local limiting factor", "concept": [ "Partly inspired by the rmaxent package." ], "topics": [ "limiting", "limiting,ANY-method" ] }, { "page": "load_model", "title": "Load a pre-computed model", "topics": [ "load_model", "load_model,character-method" ] }, { "page": "Log-class", "title": "Log prototype.", "topics": [ "Log", "Log-class" ] }, { "page": "mask", "title": "Mask data with an external layer", "topics": [ "mask", "mask.BiodiversityDatasetCollection", "mask.BiodiversityScenario", "mask.DistributionModel", "mask.PredictorDataset" ] }, { "page": "modal", "title": "Calculate the mode of a provided vector", "topics": [ "modal" ] }, { "page": "new_id", "title": "Identifier", "topics": [ "new_id" ] }, { "page": "new_waiver", "title": "Waiver", "topics": [ "new_waiver" ] }, { "page": "nicheplot", "title": "Niche plot for distribution objects", "topics": [ "nicheplot", "nicheplot,ANY-method" ] }, { "page": "partial", "title": "Obtain partial effects of trained model", "topics": [ "partial", "partial,ANY-method", "partial.DistributionModel" ] }, { "page": "partial_density", "title": "Visualize the density of the data over the environmental data", "concept": [ "Visual style emulated from ENMTools package." ], "topics": [ "partial_density", "partial_density,ANY,character-method" ] }, { "page": "plot", "title": "Plot wrappers", "topics": [ "plot", "plot.BiodiversityDatasetCollection", "plot.BiodiversityScenario", "plot.DistributionModel", "plot.Engine", "plot.PredictorDataset" ] }, { "page": "posterior_predict_stanfit", "title": "Create a posterior prediction from a rstanfit object", "topics": [ "posterior_predict_stanfit" ] }, { "page": "predictor_derivate", "title": "Create spatial derivative of raster stacks", "topics": [ "predictor_derivate" ] }, { "page": "predictor_filter", "title": "Filter a set of correlated predictors to fewer ones", "topics": [ "predictor_filter" ] }, { "page": "predictor_homogenize_na", "title": "Homogenize NA values across a set of predictors.", "topics": [ "predictor_homogenize_na" ] }, { "page": "predictor_summarize_zones", "title": "Summarize and replace values in predictors with an aggregation", "topics": [ "predictor_summarize_zones" ] }, { "page": "predictor_transform", "title": "Spatial adjustment of environmental predictors and raster stacks", "topics": [ "predictor_transform" ] }, { "page": "PredictorDataset-class", "title": "PredictorDataset class description", "topics": [ "PredictorDataset", "PredictorDataset-class" ] }, { "page": "print", "title": "Print", "topics": [ "print", "print,Id-method", "print,tbl_df-method", "print.BiodiversityDataset", "print.BiodiversityDatasetCollection", "print.BiodiversityDistribution", "print.BiodiversityScenario", "print.distribution", "print.DistributionModel", "print.Engine", "print.Id", "print.Log", "print.PredictorDataset", "print.Prior", "print.PriorList", "print.Settings" ] }, { "page": "Prior-class", "title": "Base Prior class", "topics": [ "Prior", "Prior-class" ] }, { "page": "PriorList-class", "title": "List of Priors supplied to an class", "topics": [ "PriorList", "PriorList-class" ] }, { "page": "priors", "title": "Creates a new PriorList object", "concept": [ "prior" ], "topics": [ "priors", "priors,ANY-method" ] }, { "page": "project", "title": "Project a fitted model to a new environment and covariates", "topics": [ "project", "project,BiodiversityScenario-method", "project,DistributionModel-method", "project.BiodiversityScenario", "project.DistributionModel" ] }, { "page": "pseudoabs_settings", "title": "Settings for specifying pseudo-absence points within the model background", "topics": [ "pseudoabs_settings", "pseudoabs_settings,ANY-method" ] }, { "page": "render_html", "title": "render_html", "topics": [ "render_html", "render_html,ANY-method" ] }, { "page": "rm_biodiversity", "title": "Remove specific BiodiversityDataset from a distribution object", "topics": [ "rm_biodiversity", "rm_biodiversity,BiodiversityDistribution-method" ] }, { "page": "rm_control", "title": "Remove control from an existing distribution object", "concept": [ "control" ], "topics": [ "rm_control", "rm_control,BiodiversityDistribution-method" ] }, { "page": "rm_latent", "title": "Function to remove a latent effect", "topics": [ "rm_latent", "rm_latent,BiodiversityDistribution-method", "rm_latent,BiodiversityScenario-method" ] }, { "page": "rm_limits", "title": "Remove limits from an existing distribution object", "concept": [ "control" ], "topics": [ "rm_limits", "rm_limits,BiodiversityDistribution-method" ] }, { "page": "rm_offset", "title": "Function to remove an offset", "concept": [ "offset" ], "topics": [ "rm_offset", "rm_offset,BiodiversityDistribution-method" ] }, { "page": "rm_predictors", "title": "Remove specific predictors from a distribution object", "topics": [ "rm_predictors", "rm_predictors,BiodiversityDistribution,character-method" ] }, { "page": "rm_priors", "title": "Remove existing priors from an existing distribution object", "concept": [ "prior" ], "topics": [ "rm_priors", "rm_priors,BiodiversityDistribution-method" ] }, { "page": "run_parallel", "title": "Parallel computation of function", "topics": [ "run_parallel" ] }, { "page": "run_stan", "title": "Fit cmdstanr model and convert to rstan object", "topics": [ "run_stan" ] }, { "page": "sanitize_names", "title": "Sanitize variable names", "concept": [ "Inspired from [`inlabru`] \nlist(\"\\\"bru_standardise_names\\\"\")\n function." ], "topics": [ "sanitize_names" ] }, { "page": "scenario", "title": "Create a new scenario based on trained model parameters", "topics": [ "scenario", "scenario,ANY-method" ] }, { "page": "sel_predictors", "title": "Select specific predictors from a distribution object", "topics": [ "sel_predictors", "sel_predictors,BiodiversityDistribution,character-method" ] }, { "page": "set_priors", "title": "Add priors to an existing distribution object", "topics": [ "set_priors" ] }, { "page": "set_priors-BiodiversityDistribution-method", "title": "Add priors to an existing distribution object", "topics": [ "set_priors,BiodiversityDistribution-method" ] }, { "page": "Settings-class", "title": "Prototype for model settings object", "topics": [ "Settings", "Settings-class" ] }, { "page": "similarity", "title": "Calculate environmental similarity of reference datasets to predictors.", "topics": [ "similarity", "similarity,BiodiversityDistribution-method", "similarity,SpatRaster-method" ] }, { "page": "simulate_population_steps", "title": "Simulate population dynamics following the steps approach", "concept": [ "constraint" ], "topics": [ "simulate_population_steps", "simulate_population_steps,BiodiversityScenario,matrix-method" ] }, { "page": "spartial", "title": "Obtain spatial partial effects of trained model", "topics": [ "spartial", "spartial,ANY,character-method", "spartial.DistributionModel" ] }, { "page": "stancode", "title": "Show the stan code from a trained model", "topics": [ "stancode", "stancode.DistributionModel" ] }, { "page": "STANPrior", "title": "Create a new STAN prior", "concept": [ "prior" ], "topics": [ "STANPrior", "STANPrior,character,character-method" ] }, { "page": "STANPriors", "title": "Helper function when multiple variables and types are supplied for Stan priors", "concept": [ "prior" ], "topics": [ "STANPriors", "STANPriors,vector,character-method" ] }, { "page": "summary", "title": "Summarises a trained model or predictor object", "topics": [ "summary", "summary.BiodiversityScenario", "summary.distribution", "summary.DistributionModel", "summary.PredictorDataset", "summary.PriorList", "summary.Settings" ] }, { "page": "thin_observations", "title": "Functionality for geographic and environmental thinning", "topics": [ "thin_observations" ] }, { "page": "threshold", "title": "Threshold a continuous prediction to a categorical layer", "topics": [ "threshold", "threshold,ANY-method", "threshold,BiodiversityScenario-method", "threshold,SpatRaster-method" ] }, { "page": "train", "title": "Train the model from a given engine", "topics": [ 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