Flexible Species Distribution Modeling Workflow

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Documentation for package ‘flexsdm’ version 1.4.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

abies	A data set containing localities and environmental condition of an Abies (fir tree) species in California, USA
backg	A data set containing environmental conditions of background points
calib_area	Delimit calibration area for constructing species distribution models
correct_colinvar	Collinearity reduction of predictor variables
data_bpdp	Calculate data to construct partial dependence surface plots
data_pdp	Calculate data to construct partial dependence plots
env_outliers	Integration of outliers detection methods in environmental space
esm_gam	Fit and validate Generalized Additive Models based on Ensembles of Small Models approach
esm_gau	Fit and validate Gaussian Process models based on Ensembles of Small Models approach
esm_gbm	Fit and validate Generalized Boosted Regression models based on Ensembles of Small Models approach
esm_glm	Fit and validate Generalized Linear Models based on Ensembles of Small Models approach
esm_max	Fit and validate Maximum Entropy Models based on Ensemble of Small of Model approach
esm_net	Fit and validate Neural Networks based on Ensembles of Small of Models approach
esm_svm	Fit and validate Support Vector Machine models based on Ensembles of Small of Models approach
extra_eval	Measure model extrapolation based on Shape extrapolation metric
extra_truncate	Truncate suitability predictions based on an extrapolation value
fit_dom	Fit and validate Domain models
fit_ensemble	Ensemble model fitting and validation
fit_gam	Fit and validate Generalized Additive Models
fit_gau	Fit and validate Gaussian Process models
fit_gbm	Fit and validate Generalized Boosted Regression models
fit_glm	Fit and validate Generalized Linear Models
fit_max	Fit and validate Maximum Entropy models
fit_net	Fit and validate Neural Networks models
fit_raf	Fit and validate Random Forests models
fit_svm	Fit and validate Support Vector Machine models
get_absences	Build a presence-absence database from multi-species occurrence data
get_block	Transform a spatial partition layer to the same spatial properties as environmental variables
hespero	A data set containing localities of Hesperocyparis stephensonii species in California, USA
homogenize_na	Homogenize cells with NAs across all layers
interp	Raster interpolation between two time periods
map_env_dist	Calculate environmental distance between presences and projection data
msdm_posteriori	Methods to correct overprediction of species distribution models based on occurrences and suitability patterns.
msdm_priori	Create spatial predictor variables to reduce overprediction of species distribution models
occfilt_env	Perform environmental filtering on species occurrences
occfilt_geo	Perform geographical filtering on species occurrences
occfilt_select	Select filtered occurrences
occ_reproj	Reproject location coordinates to a new coordinate reference system
palms	A data set containing presences of palms species from Southern Brazil
part_random	Conventional data partitioning methods
part_sband	Spatial band cross-validation
part_sblock	Spatial block cross-validation
part_senv	Environmental and spatial cross-validation
plot_res	Plot different resolutions to be used in part_sblock
p_bpdp	Bivariate partial dependence plot
p_extra	Graphical exploration of extrapolation or suitability pattern in the environmental and geographical space
p_pdp	Partial Dependent Plot
sample_background	Sample background points
sample_pseudoabs	Sample pseudo-absences
sdm_directory	Create directories for saving the outputs of the flexsdm
sdm_eval	Calculate different model performance metrics
sdm_extract	Extract environmental data values from a spatial raster based on x and y coordinates
sdm_predict	Spatial predictions from individual and ensemble models
sdm_summarize	Merge model performance tables
sdm_uncertainty	Calculate species distribution model uncertainty
sdm_varimp	Calculate permutation-based variable importance scores for SDMs
spp	A data set containing presences and absences of three virtual species
tune_gbm	Fit and validate Generalized Boosted Regression models with exploration of hyper-parameters that optimize performance
tune_max	Fit and validate Maximum Entropy models with exploration of hyper-parameters that optimize performance
tune_net	Fit and validate Neural Networks models with exploration of hyper-parameters
tune_raf	Fit and validate Random Forest models with exploration of hyper-parameters that optimize performance
tune_svm	Fit and validate Support Vector Machine models with exploration of hyper-parameters that optimize performance