Power and sample size calculations for a variety of study designs and 
    outcomes. Methods include t tests, ANOVA (including tests for interactions, 
    simple effects and contrasts), proportions, categorical data (chi-square tests 
    and proportional odds), linear, logistic and Poisson regression, alternative 
    and coprimary endpoints, power for confidence intervals, correlation 
    coefficient tests, cluster randomized trials, individually randomized group 
    treatment trials, multisite trials, treatment-by-covariate interaction effects 
    and nonparametric tests of location. Utilities are provided for 
    computing various effect sizes. Companion package to the book 
    "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). 
    Further resources available at <https://powerandsamplesize.org/>.

Contains functions to calculate power and sample size for
    various study designs used in bioequivalence studies. Use known.designs() to
    see the designs supported. Power and sample size can be obtained based on
    different methods, amongst them prominently the TOST procedure (two one-sided
    t-tests). See README and NEWS for further information.

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
    allow for transforming variables with distributions distant from 0 to
    normality. Transformers are implemented as S4 objects. These allow for
    transforming new instances to normality after optimising fitting parameters
    on other data. A test for central normality allows for rejecting 
    transformations that fail to produce a suitably normal distribution, 
    independent of sample number.

Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes.
 Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>),
 five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect,
 and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). 
 See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.