Enhanced RTF wrapper written in R for use with existing R tables
    packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in
    certain packages can be written out to RTF, but cannot add certain metadata
    or features to the document that are required/expected in a report for a
    regulatory submission, such as multiple levels of titles and footnotes,
    making the document landscape, and controlling properties such as margins.

The 'pharmaverse' is a set of packages that compose multiple pathways 
    through clinical data generation and reporting in the pharmaceutical industry.
    This package is designed to guide users to our work-spaces on 'GitHub', 'Slack' 
    and 'LinkedIn' as well as our website and examples.
    Learn more about the 'pharmaverse' at <https://pharmaverse.org>.

A set of Analysis Data Model (ADaM) datasets constructed using the 
    Study Data Tabulation Model (SDTM) datasets contained in the 'pharmaversesdtm' package and 
    the template scripts from the 'admiral' family of packages. ADaM dataset specifications are 
    described in: CDISC Analysis Data Model Team
    (2021) <https://www.cdisc.org/system/files/members/standard/foundational/ADaMIG_v1.3.pdf>.

A set of Study Data Tabulation Model (SDTM) datasets from the Clinical
    Data Interchange Standards Consortium (CDISC) pilot project used for testing
    and developing Analysis Data Model (ADaM) datasets inside the pharmaverse 
    family of packages. SDTM dataset specifications are described in: CDISC Submission Data Standards Team
    (2021) <https://www.cdisc.org/system/files/members/standard/foundational/SDTMIG%20v3.4-FINAL_2022-07-21.pdf>.

Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R.

Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) <doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976) <doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999) <doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986) <doi:10.1111/j.1440-1681.1986.tb00943.x>. Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) <doi:10.1021/ac60214a047>) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>. Rose plots and circular analysis are based on methods from - Batschelet (1981) <ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.