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 (2025, under review) for paired proportions. 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.