Profiles political leaders at a distance from text data
    such as speeches, interviews, press conferences, and other public
    statements. Computes Leadership Trait Analysis scores for seven
    personality traits -- including need for power, conceptual complexity,
    and self-confidence -- and classifies leaders into one of eight
    leadership styles. Also computes Operational Code Analysis scores
    summarising a leader's beliefs about politics and the use of power.

The leader clustering algorithm provides
 a means for clustering a set of data points. Unlike many other clustering
 algorithms it does not require the user to specify the number of clusters,
 but instead requires the approximate radius of a cluster as its primary
 tuning parameter. The package provides a fast implementation of this
 algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
 and infinity) as well as for spatial data using the Haversine
 formula, which takes latitude/longitude pairs as inputs and clusters
 based on great circle distances.

Extracts and creates an analysis pipeline for the JSON data files from Brain Sense sessions using Medtronic's Deep Brain Stimulation surgery electrode implants.

A unified framework for symbolic regression (SR) and multi-view 
    symbolic regression (MvSR) designed for complex, nonlinear systems, 
    with particular applicability to ecological datasets. The package 
    implements a four-stage workflow: data subset generation, 
    functional form discovery, numerical parameter optimization, and 
    multi-objective evaluation. It provides a high-level formula-style interface that 
    abstracts and extends multiple discovery engines: genetic programming 
    (via PySR), Reinforcement Learning with Monte Carlo Tree Search 
    (via RSRM), and exhaustive generalized linear model search. 'leaf' 
    extends these methods by enabling multi-view discovery, where 
    functional structures are shared across groups while parameters are 
    fitted locally, and by supporting the enforcement of domain-specific 
    constraints, such as sign consistency across groups. The framework 
    automatically handles data normalization, link functions, and 
    back-transformation, ensuring that discovered symbolic equations 
    remain interpretable and valid on the original data scale. 
    Implements methods following ongoing work by the authors 
    (2026, in preparation).