Computes confidence intervals for binomial or Poisson rates
    and their differences or ratios. Including the rate (or risk)
    difference ('RD') or rate ratio (or relative risk, 'RR') for binomial
    proportions or Poisson rates, and odds ratio ('OR', binomial only).
    Also confidence intervals for RD, RR or OR for paired binomial data,
    and estimation of a proportion from clustered binomial data.  Includes
    skewness-corrected asymptotic score ('SCAS') methods, which have been
    developed in Laud (2017) <doi:10.1002/pst.1813> from Miettinen and
    Nurminen (1985) <doi:10.1002/sim.4780040211> and Gart and Nam (1988)
    <doi:10.2307/2531848>, and in Laud (2026, under review) for paired
    proportions. In each case, the same score produces hypothesis tests that are
    improved versions of the non-inferiority test for binomial RD and RR
    by Farrington and Manning (1990) <doi:10.1002/sim.4780091208>, or a
    generalisation of the McNemar test for paired data. The package also
    includes MOVER methods (Method Of Variance Estimates Recovery) for all
    contrasts, derived from the Newcombe method but with options to use
    equal-tailed intervals in place of the Wilson score method, and
    generalised for Bayesian applications incorporating prior information.
    So-called 'exact' methods for strictly conservative coverage are
    approximated using continuity adjustments, and the amount of
    adjustment can be selected to avoid over-conservative coverage.  Also
    includes methods for stratified calculations (e.g. meta-analysis),
    either with fixed effect assumption (matching the CMH test) or
    incorporating stratum heterogeneity.

A collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) <doi:10.1016/j.jeconom.2020.09.015>. Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter.