Power and sample size calculations for genetic association studies allowing
for misspecification of the model of genetic susceptibility.
"Hum Hered. 2019;84(6):256-271.<doi:10.1159/000508558>. Epub 2020 Jul 28."
Power and/or sample size can be calculated for logistic (case/control study design)
and linear (continuous phenotype) regression models, using additive, dominant,
recessive or degree of freedom coding of the genetic covariate while assuming
a true dominant, recessive or additive genetic effect. In addition, power and
sample size calculations can be performed for gene by environment interactions.
These methods are extensions of Gauderman (2002)
<doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973>
and are described in:
Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations
for Genetic Association Studies in the Presence of Genetic Model Misspecification.
American Society of Human Genetics.
October 2018, San Diego.

Generalized ridge trace plots for ridge regression
c(person(given = "Michael", family = "Friendly",
role=c("aut", "cre"), email="friendly@yorku.ca"))
The genridge package introduces generalizations of the
standard univariate ridge trace plot used in ridge regression
and related methods.  These graphical methods show both bias
and precision, by plotting the covariance ellipsoids of the
estimated coefficients, rather than just the estimates
themselves.

The genridge package introduces generalizations of the standard univariate
ridge trace plot used in ridge regression and related methods.  These graphical methods
show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
predictors.

The genridge package introduces generalizations of the
standard univariate ridge trace plot used in ridge regression
and related methods.  These graphical methods show both bias
(actually, shrinkage) and precision, by plotting the covariance
ellipsoids of the estimated coefficients, rather than just the
estimates themselves.  2D and 3D plotting methods are provided,
both in the space of the predictor variables and in the
transformed space of the PCA/SVD of the predictors.