Provides tests for segregation distortion in F1 
    polyploid populations under different assumptions of meiosis. 
    These tests can account for double reduction, partial preferential pairing,
    and genotype uncertainty through the use of genotype likelihoods. 
    Parallelization support is provided. Details of these methods are 
    described in Gerard et al. (2025a) <doi:10.1007/s00122-025-04816-z>
    and Gerard et al. (2025b) <doi:10.1101/2025.06.23.661114>.
    Part of this material is based upon work supported by the
    National Science Foundation under Grant No. 2132247.  The
    opinions, findings, and conclusions or recommendations expressed
    are those of the author and do not necessarily reflect the views
    of the National Science Foundation.

Implementation of the original Sequence Globally Unique Identifier (SEGUID) algorithm [Babnigg and Giometti (2006) <doi:10.1002/pmic.200600032>] and SEGUID v2 (<https://www.seguid.org>), which extends SEGUID v1 with support for linear, circular, single- and double-stranded biological sequences, e.g. DNA, RNA, and proteins.

A very nice interface to Princeton's 'WordNet' without 'rJava' dependency. 'WordNet' data is not included. Princeton University makes 'WordNet' available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).

Convert a time series of observations to a time series of standardised indices that can be used to monitor variables on a common and probabilistically interpretable scale. The indices can be aggregated and rescaled to different time scales, visualised using plot capabilities, and calculated using a range of distributions. This includes flexible non-parametric and non-stationary methods.

Efficient and user-friendly routines for modern ecological
    inference. Implements the methods described in McCartan & Kuriwaki (2025+)
    <doi:10.48550/arXiv.2509.20194>, which generalize ecological regression as introduced by
    Goodman (1953) <doi:10.2307/2088121>. Includes routines for preprocessing,
    synthetic data generation, double/debiased machine learning (DML)
    estimation, partial identification bounds, and sensitivity analysis.