Species Distribution Models with Tidymodels
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Documentation for package ‘tidysdm’ version 0.9.5
Help Pages
A B C D E F G H K L M N O P R S T Y
-- A --
| add_member | Add best member of workflow to a simple ensemble |
| add_member.default | Add best member of workflow to a simple ensemble |
| add_member.tune_results | Add best member of workflow to a simple ensemble |
| add_member.workflow_set | Add best member of workflow to a simple ensemble |
| add_repeat | Add repeat(s) to a repeated ensemble |
| add_repeat.default | Add repeat(s) to a repeated ensemble |
| add_repeat.list | Add repeat(s) to a repeated ensemble |
| add_repeat.simple_ensemble | Add repeat(s) to a repeated ensemble |
| autoplot.simple_ensemble | Plot the results of a simple ensemble |
| autoplot.spatial_initial_split | Create a ggplot for a spatial initial rsplit. |
| average_precision.sf | Probability metrics for 'sf' objects |
-- B --
| blockcv2rsample | Convert an object created with 'blockCV' to an 'rsample' object |
| boyce_cont | Boyce continuous index (BCI) |
| boyce_cont.data.frame | Boyce continuous index (BCI) |
| boyce_cont.sf | Boyce continuous index (BCI) |
| boyce_cont_vec | Boyce continuous index (BCI) |
| brier_class.sf | Probability metrics for 'sf' objects |
-- C --
| calib_class_thresh | Calibrate class thresholds |
| check_sdm_presence | Check that the column with presences is correctly formatted |
| check_splits_balance | Check the balance of presences vs pseudoabsences among splits |
| clamp_predictors | Clamp the predictors to match values in training set |
| clamp_predictors.default | Clamp the predictors to match values in training set |
| clamp_predictors.SpatRaster | Clamp the predictors to match values in training set |
| clamp_predictors.SpatRasterDataset | Clamp the predictors to match values in training set |
| classification_cost.sf | Probability metrics for 'sf' objects |
| collect_metrics.repeat_ensemble | Obtain and format results produced by tuning functions for ensemble objects |
| collect_metrics.simple_ensemble | Obtain and format results produced by tuning functions for ensemble objects |
| control_ensemble_bayes | Control wrappers |
| control_ensemble_grid | Control wrappers |
| control_ensemble_resamples | Control wrappers |
-- D --
| dist_pres_vs_bg | Distance between the distribution of climate values for presences vs background |
-- E --
| explain_tidysdm | Create explainer from your tidysdm ensembles. |
| explain_tidysdm.default | Create explainer from your tidysdm ensembles. |
| explain_tidysdm.repeat_ensemble | Create explainer from your tidysdm ensembles. |
| explain_tidysdm.simple_ensemble | Create explainer from your tidysdm ensembles. |
| extrapol_mess | Multivariate environmental similarity surfaces (MESS) |
| extrapol_mess.data.frame | Multivariate environmental similarity surfaces (MESS) |
| extrapol_mess.default | Multivariate environmental similarity surfaces (MESS) |
| extrapol_mess.SpatRaster | Multivariate environmental similarity surfaces (MESS) |
| extrapol_mess.SpatRasterDataset | Multivariate environmental similarity surfaces (MESS) |
-- F --
| feature_classes | Parameters for maxent models |
| filter_collinear | Filter to retain only variables that have low collinearity |
| filter_collinear.data.frame | Filter to retain only variables that have low collinearity |
| filter_collinear.default | Filter to retain only variables that have low collinearity |
| filter_collinear.matrix | Filter to retain only variables that have low collinearity |
| filter_collinear.SpatRaster | Filter to retain only variables that have low collinearity |
| filter_high_cor | Deprecated: Filter to retain only variables below a given correlation threshold |
| filter_high_cor.data.frame | Deprecated: Filter to retain only variables below a given correlation threshold |
| filter_high_cor.default | Deprecated: Filter to retain only variables below a given correlation threshold |
| filter_high_cor.matrix | Deprecated: Filter to retain only variables below a given correlation threshold |
| filter_high_cor.SpatRaster | Deprecated: Filter to retain only variables below a given correlation threshold |
-- G --
| gain_capture.sf | Probability metrics for 'sf' objects |
| gam_formula | Create a formula for gam |
| geom_split_violin | Split violin geometry for ggplots |
| grid_cellsize | Get default grid cellsize for a given dataset |
| grid_offset | Get default grid cellsize for a given dataset |
-- H --
| horses | Coordinates of radiocarbon dates for horses |
-- K --
| kap_max | Maximum Cohen's Kappa |
| kap_max.data.frame | Maximum Cohen's Kappa |
| kap_max.sf | Maximum Cohen's Kappa |
| kap_max_vec | Maximum Cohen's Kappa |
| km2m | Convert a geographic distance from km to m |
-- L --
| lacerta | Coordinates of presences for Iberian emerald lizard |
| lacerta_ensemble | A simple ensemble for the lacerta data |
| lacerta_rep_ens | A repeat ensemble for the lacerta data |
| lacertidae_background | Coordinates of presences for lacertidae in the Iberian peninsula |
-- M --
| maxent | MaxEnt model |
| maxent_params | Parameters for maxent models |
| mn_log_loss.sf | Probability metrics for 'sf' objects |
-- N --
| niche_overlap | Compute overlap metrics of the two niches |
-- O --
| optim_thresh | Find threshold that optimises a given metric |
-- P --
| plot_pres_vs_bg | Plot presences vs background |
| predict.repeat_ensemble | Predict for a repeat ensemble set |
| predict.simple_ensemble | Predict for a simple ensemble set |
| predict_raster | Make predictions for a whole raster |
| predict_raster.default | Make predictions for a whole raster |
| prob_metrics_sf | Probability metrics for 'sf' objects |
| pr_auc.sf | Probability metrics for 'sf' objects |
-- R --
| recipe.sf | Recipe for 'sf' objects |
| regularization_multiplier | Parameters for maxent models |
| repeat_ensemble | Repeat ensemble |
| roc_auc.sf | Probability metrics for 'sf' objects |
| roc_aunp.sf | Probability metrics for 'sf' objects |
| roc_aunu.sf | Probability metrics for 'sf' objects |
-- S --
| sample_background | Sample background points for SDM analysis |
| sample_background_time | Sample background points for SDM analysis for points with a time point. |
| sample_pseudoabs | Sample pseudo-absence points for SDM analysis |
| sample_pseudoabs_time | Sample pseudo-absence points for SDM analysis for points with a time point. |
| sdm_metric_set | Metric set for SDM |
| sdm_spec_boost_tree | Model specification for a Boosted Trees model for SDM |
| sdm_spec_gam | Model specification for a GAM for SDM |
| sdm_spec_glm | Model specification for a GLM for SDM |
| sdm_spec_maxent | Model specification for a MaxEnt for SDM |
| sdm_spec_rand_forest | Model specification for a Random Forest for SDM |
| sdm_spec_rf | Model specification for a Random Forest for SDM |
| simple_ensemble | Simple ensemble |
| spatial_initial_split | Simple Training/Test Set Splitting for spatial data |
| spatial_recipe | Recipe for 'sf' objects |
-- T --
| thin_by_cell | Thin point dataset to have 1 observation per raster cell |
| thin_by_cell_time | Thin point dataset to have 1 observation per raster cell per time slice |
| thin_by_dist | Thin points dataset based on geographic distance |
| thin_by_dist_time | Thin points dataset based on geographic and temporal distance |
| tss | TSS - True Skill Statistics |
| tss.data.frame | TSS - True Skill Statistics |
| tss_max | Maximum TSS - True Skill Statistics |
| tss_max.data.frame | Maximum TSS - True Skill Statistics |
| tss_max.sf | Maximum TSS - True Skill Statistics |
| tss_max_vec | Maximum TSS - True Skill Statistics |
-- Y --
| y2d | Convert a time interval from years to days |