R: Species Distribution Models with Tidymodels

# Species Distribution Models with Tidymodels

## Documentation for package ‘tidysdm’ version 1.0.4

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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
clamp_predictors.stars | Clamp the predictors to match values in training set
classification_cost.sf | Probability metrics for 'sf' objects
collect_class_thresh | Obtain and format the class thresholds for ensemble 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)
extrapol_mess.stars | 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_collinear.stars | 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 --

make_mask_from_presence | Make a mask from presence data
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 --

pairs-method | Pairwise matrix of scatterplot for stars objects
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