Package: caretSDM 1.9.6

caretSDM: Build Species Distribution Modeling using 'caret'

Use machine learning algorithms and advanced geographic information system tools to build Species Distribution Modeling in a extensible and modern fashion.

Authors:Luíz Fernando Esser [aut, cre, cph], Reginaldo Ré [aut], Marcos R. Lima [aut], Edivando Couto [aut], José Hilário Delconte Ferreira [aut], Valéria Batista [aut], Dayani Bailly [aut]

caretSDM_1.9.6.tar.gz
caretSDM_1.9.6.zip(r-4.7)caretSDM_1.9.6.zip(r-4.6)caretSDM_1.9.6.zip(r-4.5)
caretSDM_1.9.6.tgz(r-4.6-any)caretSDM_1.9.6.tgz(r-4.5-any)
caretSDM_1.9.6.tar.gz(r-4.7-any)caretSDM_1.9.6.tar.gz(r-4.6-any)
caretSDM_1.9.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
caretSDM/json (API)
NEWS

# Install 'caretSDM' in R:

install.packages('caretSDM', repos = c('https://luizesser.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/luizesser/caretsdm/issues

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

Datasets:

algorithms - Caret Algorithms
bioc - Bioclimatic Variables
occ - Araucaria angustifolia occurrence data
parana - Paraná State
rivs - Hydrologic Variables
salm - Salminus brasiliensis occurrence data
scen - Bioclimatic Variables
scen_rs - Bioclimatic Variables

On CRAN:

5.20 score 6 stars 15 scripts 651 downloads 105 exports 181 dependencies

Last updated from:279246b446. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	528
source / vignettes	OK	323
linux-release-x86_64	OK	463
macos-release-arm64	OK	250
macos-oldrel-arm64	OK	293
windows-devel	OK	476
windows-release	OK	444
windows-oldrel	OK	515
wasm-release	OK	219

Exports:add_ensembles add_input_sdm add_models add_occurrences add_predictions add_predictors add_scenarios add_sdm_area algorithms_used background background_data background_method buffer_sdm correlate_sdm data_clean ensemble_sdm filter_species GBIF_data gcms_ensembles get_coords get_ensembles get_models get_occurrences get_pca_model get_pdp_sdm get_predictions get_predictor_names get_predictors get_scenarios_data get_sdm_area get_tune_length get_validation_metrics input_sdm is_input_sdm is_models is_occurrences is_predictions is_sdm_area join_area mapview_ensembles mapview_grid mapview_occurrences mapview_predictions mapview_predictors mapview_scenarios mean_validation_metrics models_hyperparameters multicollinearity_sdm n_background n_pseudoabsences n_records occurrences_as_df occurrences_sdm pca_predictors pca_summary pdp_sdm plot_background plot_ensembles plot_grid plot_niche plot_occurrences plot_predictions plot_predictors plot_scenarios predict_sdm prediction_change_sdm pseudoabsence_data pseudoabsence_method pseudoabsences scenarios_names sdm_area sdm_as_raster sdm_as_stars sdm_as_terra select_predictors select_scenarios selected_variables set_predictor_names set_scenarios_names set_variables_names species_names stack_sdm summary_sdm summary_sdm_presence_only test_variables_names train_sdm tsne_sdm tuneGrid_sdm use_esm use_mem validate_on_independent_data varImp_sdm vif_predictors vif_summary WorldClim_data write_background write_ensembles write_gpkg write_grid write_models write_occurrences write_predictions write_predictors write_pseudoabsences write_validation_metrics

Dependencies:abind ade4 adehabitatHR adehabitatLT adehabitatMA askpass backports base64enc BH biomod2 bslib cachem caret checkCLI checkmate class classInt cli clock cluster codetools colorspace CoordinateCleaner cpp11 crayon crul curl data.table DBI diagram digest dismo dplyr e1071 ECDFniche ecodist ecospat evaluate farver fastmap FNN fontawesome foreach foreign Formula fs future future.apply gbm generics geosphere ggplot2 ggpp ggspatial glmnet globals glue gower gridExtra gtable gtools hardhat highr Hmisc hms htmlTable htmltools htmlwidgets httpcode httr igraph ipred isoband iterators jpeg jquerylib jsonlite kernlab KernSmooth knitr ks labeling lattice lava lazyeval lemon lifecycle listenv lubridate lwgeom magrittr MASS Matrix matrixStats maxnet mclust memoise mgcv mime ModelMetrics multicool mvtnorm nabor nlme nnet numDeriv oai openssl parallelly permute pillar pixmap pkgconfig plyr png poibin polynom pracma PresenceAbsence prettyunits pROC prodlim progress progressr proxy purrr R6 rappdirs raster RColorBrewer Rcpp RcppArmadillo RcppEigen recipes reshape reshape2 rgbif rlang rmarkdown rnaturalearth rosm rpart rstudioapi s2 S7 sass scales sf shape sp sparsevctrs SQUAREM stars stringdist stringi stringr survival sys terra tibble tidyr tidyselect timechange timeDate tinytex triebeard tzdb units urltools utf8 vctrs vegan viridisLite whisker withr wk xfun xml2 xts yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add predictors to 'sdm_area'	add_predictors get_predictors
Add scenarios to 'sdm_area'	add_scenarios get_scenarios_data scenarios_names select_scenarios set_scenarios_names
Caret Algorithms	algorithms
Obtain Background data	background background_data background_method n_background
Bioclimatic Variables	bioc
Create buffer around occurrences	buffer_sdm
Correlation between projections	correlate_sdm
Presence data cleaning routine	data_clean
Ensemble Species Distribution Models	add_ensembles ensemble_sdm get_ensembles
Retrieve Species data from GBIF	GBIF_data
Ensemble GCMs into one scenario	gcms_ensembles
'input_sdm'	add_input_sdm input_sdm
'is_class' functions to check caretSDM data classes.	is_input_sdm is_models is_occurrences is_predictions is_sdm_area
Join Area	join_area
Multicollinearity Analysis	multicollinearity_sdm selected_variables
Araucaria angustifolia occurrence data	occ
Occurrences Managing	add_occurrences get_coords get_occurrences n_records occurrences_as_df occurrences_sdm species_names
Paraná State	parana
Predictors as PCA-axes	get_pca_model pca_predictors pca_summary
Model Response to Variables	get_pdp_sdm pdp_sdm
S3 Methods for plot and mapview	mapview_ensembles mapview_grid mapview_occurrences mapview_predictions mapview_predictors mapview_scenarios plot_background plot_ensembles plot_grid plot_niche plot_occurrences plot_predictions plot_predictors plot_scenarios
Predict SDM models in new data	add_predictions get_predictions predict_sdm
Prediction Change Analysis	prediction_change_sdm
Print method for ensembles	print.ensembles
Print method for input_sdm	print.input_sdm
Print method for models	print.models
Print method for occurrences	print.occurrences
Print method for predictions	print.predictions
Obtain Pseudoabsences	n_pseudoabsences pseudoabsences pseudoabsence_data pseudoabsence_method
Hydrologic Variables	rivs
Salminus brasiliensis occurrence data	salm
Bioclimatic Variables	scen
Bioclimatic Variables	scen_rs
Create a 'sdm_area' object	add_sdm_area get_sdm_area sdm_area
'sdm_as_X' functions to transform 'caretSDM' data into other classes.	sdm_as_raster sdm_as_stars sdm_as_terra
Tidyverse methods for caretSDM objects	filter.input_sdm filter.occurrences filter.sdm_area filter_species mutate.input_sdm mutate.sdm_area select.input_sdm select.sdm_area select_predictors
Predictors Names Managing	get_predictor_names set_predictor_names set_predictor_names.input_sdm set_predictor_names.sdm_area set_variables_names test_variables_names
Train a Stacked Ensemble for SDM	stack_sdm
Calculates performance across resamples	summary_sdm summary_sdm_presence_only validate_on_independent_data
Train SDM models	add_models algorithms_used get_models get_tune_length get_validation_metrics mean_validation_metrics models_hyperparameters train_sdm
tSNE	tsne_sdm
Retrieve tuneGrid from models	tuneGrid_sdm
Ensemble of Small Models (ESM) in caretSDM	use_esm
MacroEcological Models (MEM) in caretSDM	use_mem
Calculation of variable importance for models	varImp_sdm
Calculate VIF	vif_predictors vif_summary
Download WorldClim v.2.1 bioclimatic data	WorldClim_data
Write caretSDM data	write_background write_ensembles write_gpkg write_gpkg.sdm_area write_grid write_models write_occurrences write_predictions write_predictors write_pseudoabsences write_validation_metrics