Package index • flexsdm

All functions

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_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_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

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

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_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