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.

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/>.

Perform user-friendly power analyses for the random
    intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait 
    autoregressive trait state (STARTS) model. The strategy as proposed by 
    Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions 
    include the use of parameter constraints over time, bounded estimation,
    generation of data with skewness and kurtosis, and the option to setup the power 
    analysis for Mplus.

The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.