Packaged: 2022-10-17 07:55:49 UTC; giuseppelamberti

Title: Power Calculations Under Genetic Model Misspecification
Authors@R: c(person("Camille", "Moore", email = "moorec@njhealth.org",
role = c("aut", "cre")), person("Sean", "Jacobson", email = "jacobsons@njhealth.org",
Description: Power and sample size calculations for genetic association studies allowing
for misspecification of the model of genetic susceptibility.
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)
Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations
for Genetic Association Studies in the Presence of Genetic Model Misspecification.
Imports: ggplot2, nleqslv, MASS, stats, utils

Packaged: 2019-09-17 19:12:06 UTC; jacobsons

Packaged: 2020-04-07 16:05:17 UTC; jacobsons

Packaged: 2021-03-20 14:23:59 UTC; jacobsons

Packaged: 2021-03-30 14:57:52 UTC; jacobsons

Title: Generalized ridge trace plots for ridge regression
Author@R: c(person(given = "Michael", family = "Friendly",
Description: 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

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