Package index • pigauto

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Read traits and a tree, inspect the fit-free input check, run the one-call imputation entry point, and extract completed data.

read_traits(): Read trait data from a CSV file or data frame
read_tree(): Read a phylogenetic tree from a file
check_pigauto(): Check whether traits and a tree are ready for pigauto
print(<pigauto_check>): Print a pigauto input check
impute(): Impute missing phylogenetic traits (convenience wrapper)
completed_data(): Extract completed trait data
summary(<pigauto_result>): Summarise a pigauto result
print(<summary_pigauto_result>): Print a pigauto result summary

Experimental analysis-aware MI for fixed effects

The analysis model is declared before imputations are drawn. Initial support is one incomplete continuous covariate under MAR with lm, binomial-logit glm, or one-random-intercept lmer. Fixed effects only; the backend passed its package-level fixed-effect gate and remains an experimental, deliberately narrow interface.

multi_impute_analysis(): Analysis-aware multiple imputation for narrow regression models
with_imputations(): Fit a downstream model on every imputed dataset
pool_mi(): Pool downstream model fits across multiple imputations (Rubin's rules)

Stochastic prediction diagnostics

Conformal-width, Brownian/MC-dropout, PMM, and posterior-tree draws. These paths failed or were outside the downstream inferential gate and must not be used as analysis-aware multiple imputations.

multi_impute(): Generate experimental stochastic completion datasets
multi_impute_trees(): Posterior-tree prediction sensitivity

Pipeline — fine-grained control

Individual steps of the impute() pipeline, exposed for benchmarking and custom workflows.

preprocess_traits(): Preprocess trait data: align to tree, encode into latent space
build_phylo_graph(): Build a phylogenetic graph representation from a tree
make_missing_splits(): Split cells into train/val/test for imputation evaluation
mask_missing(): Create an observed/missing mask matrix
fit_baseline(): Fit the phylogenetic baseline
fit_pigauto(): Fit a pigauto model for trait imputation
predict(<pigauto_fit>): Impute missing traits using a fitted pigauto model
evaluate(): Evaluate a fitted pigauto model on its test set
evaluate_imputation(): Evaluate imputation performance against known values

Active imputation (model-based ranking)

Model-based BM/label-propagation proxy rankings under their stated assumptions. They are not a demonstrated field-gain or an optimal sampling guarantee.

suggest_next_observation(): Suggest model-based candidate observations

Covariate data helpers

Optional helpers for assembling environmental covariate matrices from public data sources. Both are designed to plug into impute(..., covariates = X).

pull_gbif_centroids(): Fetch species range-centroid covariates from GBIF
pull_worldclim_per_species(): Fetch per-species bioclim covariates from WorldClim v2.1

Benchmarks and cross-validation

simulate_benchmark(): Run a simulation benchmark for pigauto
simulate_non_bm(): Simulate non-BM trait data for benchmarking
cross_validate(): k-fold cross-validation for pigauto trait imputation
compare_methods(): Compare BM baseline and pigauto methods across replicates

Reporting and plotting

pigauto_report(): Generate an HTML benchmark report from a pigauto fit
plot(<pigauto_fit>): Plot diagnostics for a fitted pigauto model
plot(<pigauto_pred>): Plot predictions from a pigauto model
plot(<pigauto_benchmark>): Plot a pigauto benchmark
plot_comparison(): Forest-plot style comparison of benchmark results
plot_history_gg(): Plot training history (ggplot2, deprecated)
plot_uncertainty(): Plot uncertainty ribbons for imputed trait values
summary(<pigauto_fit>): Summary method for pigauto_fit objects
calibration_df(): Compute calibration data for probability predictions
confusion_matrix(): Compute a confusion matrix for categorical or binary predictions

I/O helpers

save_pigauto(): Save a fitted pigauto model
load_pigauto(): Load a saved pigauto model

Bundled data

avonet300: AVONET morphological and ecological trait data for 300 bird species
tree300: Example bird phylogeny for the 300 species in avonet300
trees300: 50 posterior phylogenies for the 300 species in avonet300
avonet_full: Full AVONET morphological and ecological trait data for 9,993 bird species
tree_full: Example bird phylogeny for the species in avonet_full
ctmax_sim: Simulated multi-observation-per-species CTmax data