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Data Access and Management","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003c!-- badges: start --\u003e\n[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![DOI](https://zenodo.org/badge/293626039.svg)](https://zenodo.org/badge/latestdoi/293626039)\n[![R-CMD-check](https://github.com/IUCNN/IUCNN/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/IUCNN/IUCNN/actions/workflows/check-standard.yaml)\n\u003c!-- badges: end --\u003e\n\n**IUCNN has been updated to version 4.0 on github and will shortly be updated on CRAN to adapt to the retirement of sp and raster. The update may not be compatible with analysis-pipelines build with version 2.x**\n\n# IUCNN\nBatch estimation of species' IUCN Red List threat status using neural networks.\n\n# Installation\n1. Install IUCNN directly from Github using remotes (some users, will need to start from the step 2 before installing the package). These steps only need to be performed once.\n```r\ninstall.packages(\"remotes\")\nlibrary(remotes)\n\ninstall_github(\"IUCNN/IUCNN\")\n```\n\n2. Since some of IUCNNs functions are run in Python, IUCNN needs to set up a Python environment. This is easily done from within R, using the `install_miniconda()` function of the package `reticulate` (this will need ca. 3 GB disk space).\nIf problems occur at this step, check the excellent [documentation of reticulate](https://rstudio.github.io/reticulate/index.html).\n```r\ninstall.packages(\"reticulate\")\nlibrary(reticulate)\ninstall_miniconda()\n```\n\n3. Create a Miniconda environment called _icunn_ and install the required Python libraries. Note that you may need a fresh R session to run the following code.\n```r\nconda_create(envname = \"iucnn\", python_version = \"3.12\", packages = c(\"numpy\", \"scipy\", \"matplotlib\", \"pandas\", \"tensorflow\", \"keras\"))\n```\n\n4. Activate the Miniconda environment.\nuse_condaenv(\"iucnn\")\n\n5. Install the npBNN python library from Github:\n\n```r\nreticulate::py_install(\"https://github.com/dsilvestro/npBNN/archive/refs/tags/v.0.1.17.tar.gz\", pip = TRUE)\n```\n\n\n# Usage\nThere are multiple models and features available in IUCNN. A vignette with a detailed tutorial on how to use those is available as part of the package: `vignette(\"Approximate_IUCN_Red_List_assessments_with_IUCNN\")`. Running IUCNN will write files to your working directory.\n\nA simple example run for terrestrial orchids (This will take about 5 minutes and download ~500MB of data for feature preparation into the working directory):\n\n```r\nlibrary(reticulate)\nuse_condaenv(\"iucnn\")\nlibrary(tidyverse)\nlibrary(IUCNN)\n\n#load example data \ndata(\"training_occ\") #geographic occurrences of species with IUCN assessment\ndata(\"training_labels\")# the corresponding IUCN assessments\ndata(\"prediction_occ\") #occurrences from Not Evaluated species to prdict\n\n# 1. Feature and label preparation\nfeatures \u003c- iucnn_prepare_features(training_occ) # Training features\nlabels_train \u003c- iucnn_prepare_labels(x = training_labels,\n                                     y = features) # Training labels\nfeatures_predict \u003c- iucnn_prepare_features(prediction_occ) # Prediction features\n\n# 2. Model training\nm1 \u003c- iucnn_train_model(x = features, lab = labels_train)\n\nsummary(m1)\nplot(m1)\n\n# 3. Prediction\niucnn_predict_status(x = features_predict,\n                     model = m1)\n```\nAdditional features quantifying phylogenetic relationships and geographic sampling bias are available via `iucnn_phylogenetic_features` and `iucnn_bias_features`.\n\n\nWith model testing\n\n```r\nlibrary(tidyverse)\nlibrary(IUCNN)\n\n#load example data \ndata(\"training_occ\") #geographic occurrences of species with IUCN assessment\ndata(\"training_labels\")# the corresponding IUCN assessments\ndata(\"prediction_occ\") #occurrences from Not Evaluated species to predict\n\n# Feature and label preparation\nfeatures \u003c- iucnn_prepare_features(training_occ) # Training features\nlabels_train \u003c- iucnn_prepare_labels(x = training_labels,\n                                     y = features) # Training labels\nfeatures_predict \u003c- iucnn_prepare_features(prediction_occ) # Prediction features\n\n\n# Model testing\n# For illustration models differing in dropout rate and number of layers\n\nmod_test \u003c- iucnn_modeltest(x = features,\n                            lab = labels_train,\n                            mode = \"nn-class\",\n                            dropout_rate = c(0.0, 0.1, 0.3),\n                            n_layers = c(\"30\", \"40_20\", \"50_30_10\"),\n                            cv_fold = 5,\n                            init_logfile = TRUE)\n\n# Select best model\nm_best \u003c- iucnn_best_model(x = mod_test,\n                          criterion = \"val_acc\",\n                          require_dropout = TRUE)\n\n# Inspect model structure and performance\nsummary(m_best)\nplot(m_best)\n\n# Train the best model on all training data for prediction\nm_prod \u003c- iucnn_train_model(x = features,\n                            lab = labels_train,\n                            production_model = m_best)\n\n# Predict RL categories for target species\npred \u003c- iucnn_predict_status(x = features_predict,\n                             model = m_prod)\nplot(pred)\n\n```\n\nUsing a convolutional neural network\n\n```r\nfeatures \u003c- iucnn_cnn_features(training_occ) # Training features\nlabels_train \u003c- iucnn_prepare_labels(x = training_labels,\n                                     y = features) # Training labels\nfeatures_predict \u003c- iucnn_cnn_features(prediction_occ) # Prediction features\n\n```\n\n# Citation\n```r\nlibrary(IUCNN)\ncitation(\"IUCNN\")\n```\n\nZizka A, Andermann T, Silvestro D (2022). \"IUCNN - Deep learning approaches to approximate species’ extinction risk.\" [Diversity and Distributions, 28(2):227-241 doi: 10.1111/ddi.13450](https://doi.org/10.1111/ddi.13450). \n\nZizka A, Silvestro D, Vitt P, Knight T (2021). “Automated conservation assessment of the orchid family with deep\nlearning.” [Conservation Biology, 35(3):897-908, doi: 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