Bayesian trophic position models using 'stan' by leveraging 'brms' for 
             stable isotope data. Trophic position models are derived by using 
             equations from 
             Post (2002) <doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2>,
             Vander Zanden and Vadeboncoeur (2002) <doi:10.1890/0012-9658(2002)083[2152:FAIOBA]2.0.CO;2>, 
             and 
             Heuvel et al. (2024) <doi:10.1139/cjfas-2024-0028>.

Gives the required 2^n treatment combinations in a 2^n symmetric factorial experiment in their respective standard order.

Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).

Implements rank-preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials.

Provides a convenient R interface to the 'National Health Service 
    NHS Technology Reference Update Distribution (TRUD) API', allowing users 
    to list available releases for their subscribed items, retrieve metadata, 
    and download release files. For more information on the API, see 
    <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.

Implements Bayesian methods, described in
    Hugh-Jones (2019) <doi:10.1007/s40881-019-00069-x>, for estimating the
    proportion of liars in coin flip-style experiments, where subjects
    report a random outcome and are paid for reporting a "good" outcome.

Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer.
    Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>.
    The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess.".