Create small multiples of several leaflet web maps with (optional) 
    synchronised panning and zooming control. When syncing is enabled all maps 
    respond to mouse actions on one map. This allows side-by-side comparisons
    of different attributes of the same geometries. Syncing can be adjusted
    so that any combination of maps can be synchronised.

Use the 'leaflet-timeline' plugin with a leaflet widget to add an
    interactive slider with play, pause, and step buttons to explore temporal
    geographic spatial data changes.

Bayesian inversion of leaf wax hydrogen isotopes to reconstruct
    precipitation isotopes using hierarchical spatial models. Provides
    fourteen Bayesian models that vary in their use of spatial Gaussian
    processes and ancillary covariates (precipitation amount, plant functional
    type, C4 fraction). Models are pre-computed using 'Stan' and stored as
    posterior distributions, so prediction does not require 'Stan' to be
    installed. A 100-draw fixture ships with the package; full 1000-draw
    posteriors are downloaded from a versioned 'Zenodo' deposit on first use;
    see Bradley (2026) <doi:10.5281/zenodo.20085465>.

Detects near-duplicate images across dataset splits using
    perceptual hashing, reports the resulting train/validation/test
    contamination, and produces a corrected, leak-free split assignment.
    Intended for machine learning researchers who need to verify that
    image classification splits do not share near-duplicate samples
    across partitions before reporting model metrics.

Provides utilities to detect common data leakage patterns including train/test
           contamination, temporal leakage, and data duplication, enhancing model reliability and
           reproducibility in machine learning workflows. Generates diagnostic reports and visual
           summaries to support data validation. Methods based on best practices from Hastie,
           Tibshirani, and Friedman (2009, ISBN:978-0387848570).

Instrumental variables (IVs) are a popular and powerful tool for 
  estimating causal effects in the presence of unobserved confounding. However, 
  classical methods rely on strong assumptions such as the exclusion criterion,
  which states that instrumental effects must be entirely mediated by 
  treatments. In the so-called "leaky" IV setting, candidate instruments are 
  allowed to have some direct influence on outcomes, rendering the average 
  treatment effect (ATE) unidentifiable. But with limits on the amount of 
  information leakage, we may still recover sharp bounds on the ATE, providing 
  partial identification. This package implements methods for ATE bounding in 
  the leaky IV setting with linear structural equations. For details, see Watson
  et al. (2024) <doi:10.48550/arXiv.2404.04446>.