{ "_id": "6a215cb5cd65a98ecbd2f371", "Package": "animl", "Title": "A Collection of ML Tools for Conservation Research", "Version": "3.2.0", "Authors@R": "c(person(given=\"Kyra\", family=\"Swanson\",email=\"tswanson@sdzwa.org\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-1496-3217\")), person(given=\"Mathias\",family=\"Tobler\",role = \"aut\"))", "Description": "Functions required to classify subjects within camera trap\nfield data. The package can handle both images and videos. The\nauthors recommend a two-step approach using Microsoft's\n'MegaDector' model and then a second model trained on the\nclasses of interest.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-04 11:06:17 UTC", "User": "root" }, "Author": "Kyra Swanson [aut, cre] (ORCID:\n), Mathias Tobler [aut]", "Maintainer": "Kyra Swanson ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-02-04 03:11:43 UTC", "RemoteUrl": "https://github.com/cran/animl", "RemoteRef": "HEAD", "RemoteSha": "509b7812c0e25f5fd94e395d56a0a25b3305eed4", "MD5sum": "9afd5ff988d55037b4cc5fc663d8b931", "_user": "cran", "_type": "src", "_file": "animl_3.2.0.tar.gz", "_fileid": "d5a8fe64e0ee47f8e579688422bb69de69c1d5b92bb26abcdde5a647374a7a4d", "_filesize": 174784, "_sha256": "d5a8fe64e0ee47f8e579688422bb69de69c1d5b92bb26abcdde5a647374a7a4d", "_created": "2026-06-04T11:06:17.000Z", "_published": 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{ "version": "1.0.0", "date": "2022-03-23" }, { "version": "1.1.0", "date": "2023-05-13" }, { "version": "2.0.0", "date": "2025-02-11" }, { "version": "3.2.0", "date": "2026-02-04" } ], "_exports": [ "animl_install", "animl_install_instructions", "build_file_manifest", "check_python", "classify", "compute_batched_distance_matrix", "compute_distance_matrix", "cosine_distance", "create_pyenv", "delete_pyenv", "detect", "download_model", "euclidean_squared_distance", "export_camtrapR", "export_coco", "export_folders", "export_megadetector", "export_timelapse", "extract_frames", "extract_miew_embeddings", "get_animals", "get_empty", "get_frame_as_image", "list_models", "load_animl", "load_class_list", "load_classifier", "load_data", "load_detector", "load_json", "load_miew", "parse_detections", "plot_all_bounding_boxes", "plot_box", "remove_diagonal", "remove_link", "save_classifier", "save_json", "sequence_classification", "single_classification", "test_main", "train_main", 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"compute_batched_distance_matrix" ] }, { "page": "compute_distance_matrix", "title": "A wrapper function for computing distance matrix.", "topics": [ "compute_distance_matrix" ] }, { "page": "cosine_distance", "title": "Computes cosine distance of two sets of vectors", "topics": [ "cosine_distance" ] }, { "page": "create_pyenv", "title": "Install python if necessary and create the environment animl_env", "topics": [ "create_pyenv" ] }, { "page": "delete_pyenv", "title": "Delete the animl_env environment", "topics": [ "delete_pyenv" ] }, { "page": "detect", "title": "Apply MegaDetector to a Given Batch of Images", "topics": [ "detect" ] }, { "page": "download_model", "title": "Download specified model to the given directory.", "topics": [ "download_model" ] }, { "page": "euclidean_squared_distance", "title": "Computes euclidean squared distance of two sets of vectors", "topics": [ "euclidean_squared_distance" ] }, { "page": "export_camtrapR", "title": "Export data into sorted folders organized by station", "topics": [ "export_camtrapR" ] }, { "page": "export_coco", "title": "Converts the .csv file to a COCO-formatted .json file.", "topics": [ "export_coco" ] }, { "page": "export_folders", "title": "Create SymLink Directories and Sort Classified Images", "topics": [ "export_folders" ] }, { "page": "export_megadetector", "title": "Converts the .csv file to the MD-formatted .json file.", "topics": [ "export_megadetector" ] }, { "page": "export_timelapse", "title": "Converts the Manifests to a csv file that contains columns needed for TimeLapse conversion in later step", "topics": [ "export_timelapse" ] }, { "page": "extract_frames", "title": "Extract frames from video for classification", "topics": [ "extract_frames" ] }, { "page": "extract_miew_embeddings", "title": "Extract Embeddings from MiewID", "topics": [ "extract_miew_embeddings" ] }, { "page": "get_animals", "title": "Return a dataframe of only MD animals", "topics": [ "get_animals" ] }, { "page": 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