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.

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.

Conduct a priori power analyses via Monte-Carlo style data simulation for linear and generalized linear mixed-effects models (LMMs/GLMMs). Provides a user-friendly workflow with helper functions to easily define fixed and random effects as well as diagnostic functions to evaluate the adequacy of the results of the power analysis.

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.