Sample size estimation for bio-equivalence trials is supported through a simulation-based approach 
    that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in 
    hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. 
    Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, 
    and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error 
    rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for 
    equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) 
    <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) 
    <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. 
    Comprehensive documentation and vignettes guide users through implementation and interpretation of results.

Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset).  Emphasis on avoiding common biases due to the use of estimated allele frequencies.  The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects.  Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal).  GWAS method benchmarking functions are also provided.  Described in Yao and Ochoa (2022) <doi:10.1101/2022.03.25.485885>.

Provides some basic routines for simulating a
    clinical trial. The primary intent is to provide some tools to
    generate trial simulations for trials with time to event outcomes.
    Piecewise exponential failure rates and piecewise constant
    enrollment rates are the underlying mechanism used to simulate
    a broad range of scenarios such as those presented in
    Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>.
    However, the basic generation of data is done using pipes to allow
    maximum flexibility for users to meet different needs.