Implementation of the DISCO (Density-based Internal Score for
    Clusterings with nOise) metric, a cluster validity index for evaluating
    density-based clustering results without ground truth labels. DISCO is the
    first index to explicitly assess the quality of noise point assignments in
    addition to cluster quality. It uses density-connectivity distance derived
    from a minimum spanning tree of the mutual-reachability graph, providing
    interpretable, bounded scores in [-1, 1]. Higher scores indicate better
    clustering. Based on Beer et al. (2025)
    <doi:10.48550/arXiv.2503.00127>.

Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 <doi:10.1016/j.intell.2016.08.008>], [Sims, Trattner, and Garrison, 2024 <doi:10.3389/fpsyg.2024.1430978>] for empirical examples, and [Garrison and colleagues for theoretical work <doi:10.1101/2025.08.25.25334395>].

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

Analyzes group patterns using discourse analysis data with
    graph theory mathematics. Takes the order of which individuals talk
    and converts it to a network edge and weight list. Returns the
    density, centrality, centralization, and subgroup information for each
    group. Based on the analytical framework laid out in Chai et al.
    (2019) <doi:10.1187/cbe.18-11-0222>.