Provides tools for multivariate outlier detection based on
    geometric properties of multivariate data using random directional
    projections. Observation-level outlier scores are computed by jointly
    probing radial magnitude and angular alignment through repeated
    projections onto random directions, with optional robust centering and
    covariance adjustment. In addition to global outlier scoring, the method
    produces dimension-level contribution measures to support interpretation
    of detected anomalies. Visualization utilities are included to summarize
    directional contributions for extreme observations.

Outlier detection method that flags suspicious values within observations,
  constrasting them against the normal values in a user-readable format, potentially
  describing conditions within the data that make a given outlier more rare.
  Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>.
  Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

Provides functions for detecting outliers in datasets using statistical methods. 
    The package supports identification of anomalous observations in numerical data and 
    is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows.

Create regression tables for publication.
    Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs.

Performs outrigger local polynomial regression/ distributional adaptation, using a score-matching spline estimator of the conditional score function.
    Details of the method can be found in Young, Shah and Samworth (2026) <doi:10.48550/arXiv.2603.11282>.

An approach to outlier detection in RNA-seq and related data
    based on five statistics. 'OutSeekR' implements an outlier test by comparing
    the distributions of these statistics in observed data with those of
    simulated null data.

For the problem of indirect treatment comparison with limited subject-level data, this 
    package provides tools for model-based standardisation with several different computation approaches.
        See Remiro‐Azócar A, Heath A, Baio G (2022) ``Parametric G‐computation for compatible
        indirect treatment comparisons with limited individual patient data'',
        Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.