Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <doi:10.1093/bioinformatics/btad366>].

A set of functions to compute the Hodrick-Prescott (HP)
    filter with automatically selected jumps. The original
    HP filter extracts a smooth trend from a time series, and our version
    allows for a small number of automatically identified jumps.
    See Maranzano and Pelagatti (2024) <doi:10.2139/ssrn.4896170> for details.

Structure and formatting requirements for clinical trial table and listing outputs
    vary between pharmaceutical companies. 'junco' provides additional tooling for use alongside
    the 'rtables', 'rlistings' and 'tern' packages when creating table and listing outputs. While
    motivated by the specifics of Johnson and Johnson Clinical and Statistical Programming's
    table and listing shells, 'junco' provides functionality that is general and reusable.
    Major features include a) alternative and extended statistical analyses beyond what 'tern'
    supports for use in standard safety and efficacy tables, b) a robust production-grade
    Rich Text Format (RTF) exporter for both tables and listings, c) structural support
    for spanning column headers and risk difference columns in tables, and d) robust
    font-aware automatic column width algorithms for both listings and tables.

Individual based simulations of hybridizing populations,
    where the accumulation of junctions is tracked. Furthermore,
    mathematical equations are provided to verify simulation outcomes.
    Both simulations and mathematical equations are based on Janzen
    (2018, <doi:10.1101/058107>) and Janzen (2022,
    <doi:10.1111/1755-0998.13519>).

Leverages the 'yum' package to
             implement a 'YAML' ('YAML Ain't Markup Language', a human
             friendly standard for data serialization; see <https://yaml.org>)
             standard for documenting justifications, such as for decisions
             taken during the planning, execution and analysis of a study
             or during the development of a behavior change intervention
             as illustrated by Marques & Peters (2019)
             <doi:10.17605/osf.io/ndxha>. These justifications are both
             human- and machine-readable, facilitating efficient extraction
             and organisation.