An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior
  probability (Thall and Simon (1994) <doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008) <doi:10.1177/1740774508089279>).

Calculate the Bayesian posterior/predictive probability and
    determine the sample size and stopping boundaries for single-arm Phase II design.

Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).

Fits right-truncated meta-analysis (RTMA), a bias correction for
  the joint effects of p-hacking (i.e., manipulation of results within studies
  to obtain significant, positive estimates) and traditional publication bias
  (i.e., the selective publication of studies with significant, positive
  results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for
  p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication
  bias alone, p-hacking that favors significant, positive results (termed
  "affirmative") can distort the distribution of affirmative results. To
  bias-correct results from affirmative studies would require strong assumptions
  on the exact nature of p-hacking. In contrast, joint p-hacking and publication
  bias do not distort the distribution of published nonaffirmative results when
  there is stringent p-hacking (e.g., investigators who hack always eventually
  obtain an affirmative result) or when there is stringent publication bias
  (e.g., nonaffirmative results from hacked studies are never published). This
  means that any published nonaffirmative results are from unhacked studies.
  Under these assumptions, RTMA involves analyzing only the published
  nonaffirmative results to essentially impute the full underlying distribution
  of all results prior to selection due to p-hacking and/or publication bias.
  The package also provides diagnostic plots described in Mathur (2022).