A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.

Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) <doi:10.1890/14-1261.1>.

Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.

Provides functions and command-line user interface to generate allocation sequence 
             by response-adaptive randomization for clinical trials. The package currently supports 
             two families of frequentist response-adaptive randomization procedures, Doubly Adaptive 
             Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for 
             binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square 
             (or 'ANOVA') hypothesis testing methods are also available in the package to  facilitate 
             the inference for treatment effect. Additionally, the package provides comprehensive and 
             efficient tools to allow one to evaluate and  compare the performance of randomization 
             procedures and tests based on various criteria. For example, plots for relationship among 
             assumed treatment effects,  sample size, and power are provided. Five allocation functions 
             for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations 
             such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) 
             <doi:10.1111/j.0006-341X.2001.00909.x> and 'Urn' allocations.