'ACE' (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients 
             extremely quickly and efficiently. This package is designed to interface directly with an 
             instance of 'ACE' search engine and facilitates API queries and data dumps. Prerequisite
             is a good knowledge of the temporal language to be able to efficiently construct a query.
             More information available at <https://shahlab.stanford.edu/start>.

A few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.

Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.

Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) <doi:10.1002/9780470316917>. The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.

Implements a hierarchical penalized spline framework for estimating
    achievement gap trajectories in longitudinal educational data.
    The achievement gap between two groups (e.g., low versus high
    socioeconomic status) is modeled directly as a smooth function
    of grade while the baseline trajectory is estimated simultaneously
    within a mixed-effects model. Smoothing parameters are selected
    using restricted maximum likelihood (REML), and simultaneous
    confidence bands with correct joint coverage are constructed
    using posterior simulation. The package also includes functions
    for simulation-based benchmarking, visualization of gap
    trajectories, and hypothesis testing for global and
    grade-specific differences. The modeling framework builds on
    penalized spline methods (Eilers and Marx, 1996,
    <doi:10.1214/ss/1038425655>) and generalized additive modeling
    approaches (Wood, 2017, <doi:10.1201/9781315370279>), with
    uncertainty quantification following Marra and Wood (2012,
    <doi:10.1111/j.1467-9469.2011.00760.x>).