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.

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) <doi:10.1007/978-3-642-79999-0_1>.