Package: tidysdm 1.0.4.9007

Andrea Manica

tidysdm: Species Distribution Models with Tidymodels

Fit species distribution models (SDMs) using the 'tidymodels' framework, which provides a standardised interface to define models and process their outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects, models and metrics specific to SDMs, as well as a number of specialised functions to process occurrences for contemporary and palaeo datasets. The full functionalities of the package are described in Leonardi et al. (2024) <doi:10.1111/2041-210X.14406>.

Authors:Michela Leonardi [aut], Margherita Colucci [aut], Andrea Vittorio Pozzi [aut], Eleanor M.L. Scerri [aut], Ben Tupper [ctb], Ondrej Pelanek [ctb], Andrea Manica [aut, cre]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidysdm/json (API)

# Install 'tidysdm' in R:
install.packages('tidysdm', repos = c('https://evolecolgroup.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/evolecolgroup/tidysdm/issues

Pkgdown/docs site:https://evolecolgroup.github.io

Datasets:

On CRAN:

Conda:

species-distribution-modellingtidymodels

9.34 score 37 stars 191 scripts 368 downloads 62 exports 135 dependencies

Last updated from:b983919618 (on dev). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK370
source / vignettesOK473
linux-release-x86_64OK347
macos-release-arm64OK298
macos-oldrel-arm64OK208
windows-develOK319
windows-releaseOK283
windows-oldrelOK293
wasm-releaseOK196

Exports:%>%add_memberadd_repeatblockcv2initial_splitblockcv2rsampleboyce_contboyce_cont_veccalib_class_threshcheck_sdm_presencecheck_splits_balanceclamp_predictorscollect_class_threshcontrol_ensemble_bayescontrol_ensemble_gridcontrol_ensemble_resamplesdist_pres_vs_bgexplain_tidysdmextrapol_messfeature_classesfilter_collinearfilter_high_corgam_formulageom_split_violinget_repeatgrid_cellsizegrid_offsetkap_maxkap_max_veckm2mmake_mask_from_presencemaxentmaxnet_fitmaxnet_predictniche_overlapoptim_threshpairsplot_pres_vs_bgpredict_rasterregularization_multiplierrepeat_ensemblesample_backgroundsample_background_timesample_pseudoabssample_pseudoabs_timesdm_metric_setsdm_spec_boost_treesdm_spec_gamsdm_spec_glmsdm_spec_maxentsdm_spec_rand_forestsdm_spec_rfsimple_ensemblespatial_initial_splitspatial_recipethin_by_cellthin_by_cell_timethin_by_distthin_by_dist_timetsstss_maxtss_max_vecy2d

Dependencies:abindbackportsbase64encbroombslibcachemclassclassIntcliclockcodetoolsconflictedcpp11DALEXdata.tableDBIdiagramdialsDiceDesigndigestdoFuturedplyre1071evaluatefarverfastmapfontawesomeforeachfsfurrrfuturefuture.applyGauProgenericsggplot2glmnetglobalsgluegowergridExtragtablehardhathighrhtmltoolsiBreakDowninferingredientsipredisobanditeratorsjquerylibjsonlitekernelshapKernSmoothknitrlabelinglatticelavalbfgslifecyclelistenvlubridatemagrittrMASSMatrixmaxnetmemoisemimemixoptmodeldatamodelenvnnetnumDerivparallellyparsnippatchworkpillarpkgconfigprettyunitsprodlimprogressrproxypurrrR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenrecipesrlangrmarkdownrpartrsamplerstudioapis2S7sassscalessfsfdshapeslidersparsevctrsspatialsamplesplitfngrSQUAREMstarsstringistringrsurvivaltailorterratibbletidymodelstidyrtidyselecttimechangetimeDatetinytextunetzdbunitsutf8vctrsviridisLitewarpwithrwkworkflowsworkflowsetsxfunxgboostyamlyardstick

tidysdm
SDMs with tidymodels | Accessing the data for this vignette: how to use rgbif | Preparing your data | Map projection | Thinning step | Fit the model by cross-validation | Projecting to the present | Projecting to the future | Dealing with extrapolation | Visualising the contribution of individual variables | Repeated ensembles
Rendered fromtidysdm.Rmdusingknitr::rmarkdown

Last update: 2026-07-15
Started: 2025-12-05

Examples of additional tidymodels features
Using multi-level factors as predictors | Exploring models with DALEX | Different recipes for certain models | The initial split | Stack ensembles
Rendered froma2_tidymodels_additions.Rmdusingknitr::rmarkdown

Last update: 2026-03-11
Started: 2023-09-27

Troubleshooting models that fail
NAs in the data | Recipes and the response variable | Using the desired formula with GAM | When only some splits fail
Rendered froma3_troubleshooting.Rmdusingknitr::rmarkdown

Last update: 2025-05-05
Started: 2023-09-27

Application with palaeodata
SDMs with tidymodels for palaeo data | Preparing your data | Fit the model by crossvalidation | Projecting to other times
Rendered froma1_palaeodata_application.Rmdusingknitr::rmarkdown

Last update: 2025-03-06
Started: 2023-07-14

Readme and manuals

Help Manual

Help pageTopics
Add best member of workflow to a simple ensembleadd_member add_member.default add_member.tune_results add_member.workflow_set
Add repeat(s) to a repeated ensembleadd_repeat add_repeat.default add_repeat.list add_repeat.simple_ensemble
Plot the results of a repeat ensembleautoplot.repeat_ensemble
Plot the results of a simple ensembleautoplot.simple_ensemble
Create a ggplot for a spatial initial rsplit.autoplot.spatial_initial_split
Create an initial train/test split from a blockCV objectblockcv2initial_split
Convert an object created with 'blockCV' to an 'rsample' objectblockcv2rsample
Boyce continuous index (BCI)boyce_cont boyce_cont.data.frame boyce_cont.sf boyce_cont_vec
Calibrate class thresholdscalib_class_thresh calib_class_thresh.default calib_class_thresh.repeat_ensemble calib_class_thresh.simple_ensemble
Check that the column with presences is correctly formattedcheck_sdm_presence
Check the balance of presences vs pseudoabsences among splitscheck_splits_balance
Clamp the predictors to match values in training setclamp_predictors clamp_predictors.default clamp_predictors.SpatRaster clamp_predictors.SpatRasterDataset clamp_predictors.stars
Obtain and format the class thresholds for ensemble objectscollect_class_thresh
Obtain and format results produced by tuning functions for ensemble objectscollect_metrics.repeat_ensemble collect_metrics.simple_ensemble
Control wrapperscontrol_ensemble_bayes control_ensemble_grid control_ensemble_resamples
Distance between the distribution of climate values for presences vs backgrounddist_pres_vs_bg
Create explainer from your tidysdm ensembles.explain_tidysdm explain_tidysdm.default explain_tidysdm.repeat_ensemble explain_tidysdm.simple_ensemble
Multivariate environmental similarity surfaces (MESS)extrapol_mess extrapol_mess.data.frame extrapol_mess.default extrapol_mess.SpatRaster extrapol_mess.SpatRasterDataset extrapol_mess.stars
Filter to retain only variables that have low collinearityfilter_collinear filter_collinear.data.frame filter_collinear.default filter_collinear.matrix filter_collinear.SpatRaster filter_collinear.stars
Deprecated: Filter to retain only variables below a given correlation thresholdfilter_high_cor filter_high_cor.data.frame filter_high_cor.default filter_high_cor.matrix filter_high_cor.SpatRaster
Create a formula for gamgam_formula
Split violin geometry for ggplotsgeom_split_violin
Extract a simple ensemble out of a repeat ensembleget_repeat
Get default grid cellsize for a given datasetgrid_cellsize
Get default grid cellsize for a given datasetgrid_offset
Coordinates of radiocarbon dates for horseshorses
Maximum Cohen's Kappakap_max kap_max.data.frame kap_max.sf kap_max_vec
Convert a geographic distance from km to mkm2m
Coordinates of presences for Iberian emerald lizardlacerta
A simple ensemble for the lacerta datalacerta_ensemble
A repeat ensemble for the lacerta datalacerta_rep_ens
Coordinates of presences for lacertidae in the Iberian peninsulalacertidae_background
Make a mask from presence datamake_mask_from_presence
MaxEnt modelmaxent
Parameters for maxent modelsfeature_classes maxent_params regularization_multiplier
Compute overlap metrics of the two nichesniche_overlap
Find threshold that optimises a given metricoptim_thresh
Pairwise matrix of scatterplot for stars objectspairs,stars-method
Plot presences vs backgroundplot_pres_vs_bg
Make predictions for a whole rasterpredict_raster predict_raster.default
Predict for a repeat ensemble setpredict.repeat_ensemble
Predict for a simple ensemble setpredict.simple_ensemble
Probability metrics for 'sf' objectsaverage_precision.sf brier_class.sf classification_cost.sf gain_capture.sf mn_log_loss.sf prob_metrics_sf pr_auc.sf roc_auc.sf roc_aunp.sf roc_aunu.sf
Recipe for 'sf' objectsrecipe.sf spatial_recipe
Repeat ensemblerepeat_ensemble
Sample background points for SDM analysissample_background
Sample background points for SDM analysis for points with a time point.sample_background_time
Sample pseudo-absence points for SDM analysissample_pseudoabs
Sample pseudo-absence points for SDM analysis for points with a time point.sample_pseudoabs_time
Metric set for SDMsdm_metric_set
Model specification for a Boosted Trees model for SDMsdm_spec_boost_tree
Model specification for a GAM for SDMsdm_spec_gam
Model specification for a GLM for SDMsdm_spec_glm
Model specification for a MaxEnt for SDMsdm_spec_maxent
Model specification for a Random Forest for SDMsdm_spec_rand_forest sdm_spec_rf
Simple ensemblesimple_ensemble
Simple Training/Test Set Splitting for spatial dataspatial_initial_split
Thin point dataset to have 1 observation per raster cellthin_by_cell
Thin point dataset to have 1 observation per raster cell per time slicethin_by_cell_time
Thin points dataset based on geographic distancethin_by_dist
Thin points dataset based on geographic and temporal distancethin_by_dist_time
TSS - True Skill Statisticstss tss.data.frame
Maximum TSS - True Skill Statisticstss_max tss_max.data.frame tss_max.sf tss_max_vec
Convert a time interval from years to daysy2d