An implementation of the full-likelihood Bayes factor (FLB)
    for evaluating segregation evidence in clinical medical genetics. The
    method was introduced by Thompson et al. (2003) <doi:10.1086/378100>.
    This implementation supports custom penetrance values and liability
    classes, and allows visualisations and robustness analysis as
    presented in Ratajska et al. (2023) <doi:10.1002/mgg3.2107>. See also the 
    online app 'shinyseg', <https://chrcarrizosa.shinyapps.io/shinyseg>, which 
    offers interactive segregation analysis with many additional features 
    (Carrizosa et al. (2024) <doi:10.1093/bioinformatics/btae201>).

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.

Extended Susceptible-Exposed-Infected-Recovery Model for 
    handling high false negative rate and symptom based administration of 
    diagnostic tests. <doi:10.1101/2020.09.24.20200238>.

An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See <http://snap.stanford.edu/seismic/> for more information and datasets.