Package index
Stage 1 - Validation data
Read BirdNET selection tables, select clips for manual review, and cut and pack them.
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extract_validation_clips() - Build the validation clip set
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read_selection_tables() - Read BirdNET selection tables
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select_clips() - Select the clips to validate
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cut_clips() - Cut the selected clips
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pack_clips() - Pack the clips into one zip per species
Stage 1 - After the review
Classify the reviewed clips and summarise precision, thresholds, presence and retraining clips.
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post_validation() - Summarise the manual validation
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classify_review() - Classify the reviewed clips
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precision_by_bin() - Precision by confidence bin
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suggest_thresholds() - Suggest a confidence threshold per species
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nightly_presence() - Confirmed presence by night
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retraining_clips() - Positive clips for retraining
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plot_precision() - Plot the precision curves
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read_validation_tables() - Read and stack the validation tables
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build_training_set() - Build the training folders for BirdNET-Analyzer
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exclude_training_clips() - Leave the training clips out of a manifest
Stage 1 - Several reviewers
Split the clips among reviewers, measure their agreement and combine their verdicts.
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assign_reviewers() - Assign the clips to several reviewers
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split_by_reviewer() - Copy each reviewer's clips to their own folder
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reviewer_agreement() - Agreement between reviewers
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consensus_verdicts() - Consensus verdicts of several reviewers
Stage 2 - Calibration
Calibrate confidence scores into probabilities of true detection and build nightly detection histories.
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calibrate() - Calibrate confidence scores and build detection histories
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read_validations() - Read the validation tables for calibration
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calibration_models() - Candidate calibration models
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calibration_folds() - Assign cross-validation folds for calibration
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compare_calibration_models() - Compare calibration models by cross-validation
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select_calibration_model() - Select the calibration model
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fit_calibration() - Fit the calibration model
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calibration_effort() - Effort curve of the calibration
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score_detections() - Score every detection with the calibration model
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detection_history() - Nightly detection history
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summarise_history() - Summarise the detection history by site and species
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plot_calibration() - Plot the calibration curves
Stage 2 - Bayesian calibration
Fit the calibration with brms, check the prior and the fit, and propagate the uncertainty to the histories and the matrices.
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calibration_prior() - Example prior for a Bayesian calibration model
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prior_predictive_calibration() - Prior predictive calibration curves
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calibration_diagnostics() - Convergence diagnostics of a Bayesian calibration model
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calibration_sensitivity() - Prior and likelihood sensitivity of a Bayesian calibration model
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plot_calibration_draws() - Calibration curves of posterior (or prior) draws
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history_draws() - Nightly detection histories over posterior draws
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community_matrices_draws() - Community matrices over posterior draws
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build_community_matrices() - Write the community matrices
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community_matrices() - Community matrices from the detection history
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cell_status() - Status of each site x species cell for the community matrices
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suggest_core_species() - Suggest the core species of the community matrices
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sheepdogsheepdog-package - sheepdog: Sample, Validate and Calibrate 'BirdNET' Detections