FamSKAT-RC is a family-based association kernel test for both 
    rare and common variants. This test is general and several special cases 
    are known as other methods: famSKAT, which only focuses on rare variants 
    in family-based data, SKAT, which focuses on rare variants in 
    population-based data (unrelated individuals), and SKAT-RC, which focuses 
    on both rare and common variants in population-based data. When one 
    applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes 
    famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the 
    kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one 
    applies famSKAT-RC and set the kinship matrix (fullkins) to the identity 
    matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also 
    include a small sample synthetic pedigree to demonstrate the method with.  
    For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.

The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.

Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.