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), and two-stage estimation (TSE) methods for treatment switching in randomized clinical trials.

A convenient R interface to the 'National Health Service (NHS) 
    Technology Reference Update Distribution (TRUD) API'. Retrieve available
    releases for items that you are subscribed to and download these with ease.
    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.
    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." <https://dl.acm.org/doi/10.5555/2981562.2981605>.

Implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the 
   same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.