This package aims to support the construction of Abundance-based species distribution models, including data preparation, model fitting, prediction, and model exploration. The package offers several modeling approaches (i.e., algorithms) that users can fine-tune and customize. Models can be predicted in geographic space and explored regarding performance and response curves. Because modeling workflows in adm are constructed based on a combination of distinct functions and simple outputs, adm can be easily integrated into other packages.
adm functions are grouped in three categories: modeling, post-modeling, and miscellaneous tools
Functions to tune, fit, and validate models with nine different algorithms, with a suite of possible model-specific hyperparameters
Fit and validate models without hyperparameters tuning
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fit_abund_cnn()Fit and validate Convolutional Neural Network Model -
fit_abund_dnn()Fit and validate Deep Neural Network model -
fit_abund_gam()Fit and validate Generalized Additive Models -
fit_abund_gbm()Fit and validate Generalized Boosted Regression models -
fit_abund_glm()Fit and validate Generalized Linear Models -
fit_abund_net()Fit and validate Artificial Neural Network models -
fit_abund_raf()Fit and validate Random Forests models -
fit_abund_svm()Fit and validate Support Vector Machine models -
fit_abund_xgb()Fit and validate Extreme Gradient Boosting models
Fit and validate models with hyperparameters tuning
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tune_abund_cnn()Fit and validate Convolutional Neural Network with exploration of hyper-parameters that optimize performance -
tune_abund_dnn()Fit and validate Deep Neural Network model with exploration of hyper-parameters that optimize performance -
tune_abund_gam()Fit and validate Generalized Additive Models with exploration of hyper-parameters that optimize performance -
tune_abund_gbm()Fit and validate Generalized Boosted Regression models with exploration of hyper-parameters that optimize performance -
tune_abund_glm()Fit and validate Generalized Linear Models with exploration of hyper-parameters that optimize performance -
tune_abund_net()Fit and validate Shallow Neural Networks models with exploration of hyper-parameters that optimize performance -
tune_abund_raf()Fit and validate Random Forest models with exploration of hyperparameters that optimize performance -
tune_abund_svm()Fit and validate Support Vector Machine models with exploration of hyper-parameters that optimize performance -
tune_abund_xgb()Fit and validate Extreme Gradient Boosting models with exploration of hyper-parameters that optimize performance
Modeling evaluation
adm_eval()Calculate different model performance metrics
Functions to predict abundance across space and construct partial dependence plots to explore the relationships between abundance and environmental predictors
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adm_predict()Spatial predictions from individual and ensemble models -
p_abund_bpdp()Bivariate partial dependence plots for abundance-based distribution models -
p_abund_pdp()Partial dependent plots for abundance-based distribution models -
data_abund_bpdp()Calculate data to construct bivariate partial dependence plots -
data_abund_pdp()Calculate data to construct partial dependence plots
Extra functions to support the modeling workflow, including data handling, transformations, and hyperparameter selection.
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adm_extract()Extract values from a spatial raster based on x and y coordinates -
adm_summarize()Merge model performance tables -
adm_transform()Performs data transformation on a variable based on the specified method. -
balance_dataset()Balance database at a given absence-presence ratio -
cnn_make_samples()Creates sample data for Convolutional Neural Network -
croppin_hood()Crop rasters around a point (for Convolutional Neural Networks) -
family_selector()Select probability distributions for GAM and GLM -
generate_arch_list()Generate architecture list for Deep Neural Network and Convolutional Neural Network -
generate_cnn_architecture()Generate architectures for Convolutional Neural Network -
generate_dnn_architecture()Generate architectures for Deep Neural Network -
model_selection()Best hyper-parameters selection -
res_calculate()Calculate the output resolution of a layer -
select_arch_list()Select architectures for Convolutional Neural Network or Deep Neural Network
You can install the development version of adm from github
# For Windows and Mac OS operating systems
remotes::install_github("sjevelazco/adm")See the package website (https://sjevelazco.github.io/adm/) for functions explanation and vignettes.
de Oliveira Junior A.C., Velazco S.J.E. (2025). adm: an R package for constructing abundance-based species distribution models. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X70074