This is a package for calculating two differing notions of power, or deriving sample
 sizes for specified requisite power in multiple testing experiments under a variety of methods for
 control of the distribution of the False Discovery Proportion (FDP). More specifically, one can
 choose to control the FDP distribution according to control of its (i) mean, e.g. the usual BH-FDR
 procedure, or via the probability that it exceeds a given value, delta, via (ii) the Romano
 procedure, or via (iii) my procedure based upon asymptotic approximation. Likewise, we can think of
 the power in multiple testing experiments in terms of a summary of the distribution of the
 True Positive Proportion (TPP). The package will compute power, sample size or any other missing
 parameter required for power based upon (i) the mean of the TPP which is the average power
 (ii) the probability that the TPP exceeds a given value, lambda, via my asymptotic approximation
 procedure. The theoretical results are described in Izmirlian, G. (2020) Stat. and Prob. letters,
 <doi:10.1016/j.spl.2020.108713>, and an applied  paper describing the methodology with a
 simulation study is in preparation.

Tools for the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.

Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.

Statistical power and minimum required sample size calculations for (1) testing a proportion (one-sample) against a constant,  (2) testing a mean (one-sample) against a constant,  (3) testing difference between two proportions (independent samples),  (4) testing difference between two means or groups (parametric and non-parametric tests for independent and paired samples),  (5) testing a correlation (one-sample) against a constant,  (6) testing difference between two correlations (independent samples), (7) testing a single coefficient in multiple linear regression, logistic regression, and Poisson regression (with standardized or unstandardized coefficients, with no covariates or covariate adjusted), (8) testing an indirect effect (with standardized or unstandardized coefficients, with no covariates or covariate adjusted) in the mediation analysis (Sobel, Joint, and Monte Carlo tests), (9) testing an R-squared against zero in linear regression, (10) testing an R-squared difference against zero in hierarchical regression,  (11) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of variance (could be one-way, two-way, and three-way),  (12) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of covariance (could be one-way, two-way, and three-way),  (13) testing an eta-squared or f-squared (for between, within, and interaction effects) against zero in one-way repeated measures analysis of variance (with non-sphericity correction and repeated measures correlation), and (14) testing goodness-of-fit or independence for contingency tables. Alternative hypothesis can be formulated as "not equal", "less", "greater", "non-inferior", "superior", or "equivalent" in (1), (2), (3), and (4); as "not equal", "less", or "greater" in (5), (6), (7) and (8); but always as "greater" in (9), (10), (11), (12), (13), and (14). Reference: Bulus and Polat (2023) <https://osf.io/ua5fc>.