Transmission Ratio Distortion (TRD) is a genetic phenomenon where two alleles from either parent are not transmitted to the offspring at the Mendelian 1:1 ratio. Occurrence of TRD in general population can lead to false inflation or attenuation of association signals in case populations. Therefore, it is necessary to adjust for TRD in model fitting of case populations. This package uses models such as loglinear model (Weinberg 1998), augmented loglinear model (Huang 2014), and tests such as TDT (Spielman 1993), augmented TDT (Labbe 2013), on simulated or real datasets. This package has a simulation function which generates a population that is under the influence of TRD, and samples a sub-population, which can serve as the dataset to fit a loglinear model or to perform a TDT. Real dataset with the same data structure as described in the help file of the function 'll' can also be used in the loglinear model and the TDT functions. No real dataset is included in this package.

Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.

Tensor Regression with Envelope Structure and Three Generic
Envelope Estimation Approaches
Wenjing Wang [aut, cre] <wenjing.wang@stat.fsu.edu>,
Xin Zhang [aut, cre] <henry@stat.fsu.edu>
Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive.
R (>= 3.5.0), ManifoldOptim, rTensor, MASS