based on a modified Kolmogorov-Smirnov statistic. We add Covariate
adjustment to improve the analysis."Adverse event enrichment tests using
VAERS" Shuoran Li, Lili Zhao (2020) <arXiv:2007.02266>.
person(given = "Hongfan", family = "Chen", role = c("aut"), email = "chenhf@umich.edu"),
Imports: dplyr, magrittr, qvalue, doParallel, tidyr, modelr, foreach,

Title: Model-Based Estimation of Confounder-Adjusted Attributable
Author: Elisabeth Dahlqwist and Arvid Sjolander
Description: Estimates the attributable fraction in different sampling designs adjusted for measured confounders using logistic regression (cross-sectional and case-control designs), conditional logistic regression (matched case-control design) and Cox proportional hazard regression (cohort design with time-to-event outcome). The variance of the estimator is obtained by combining the delta method with the the sandwich formula.
Depends: stats, base, graphics, survival, drgee, R (>= 3.0.0)

Author: Elisabeth Dahlqwist and Arvid Sjölander
Depends: stats, base, graphics, survival, drgee, R (>= 3.0.0),

Description: Estimates the attributable fraction in different sampling designs
adjusted for measured confounders using logistic regression (cross-sectional
and case-control designs), conditional logistic regression (matched case-control
design), Cox proportional hazard regression (cohort design with time-to-
event outcome) and gamma-frailty model with a Weibull baseline hazard. The variance of the estimator is obtained by combining the delta
method with the the sandwich formula. Dahlqwist et al.(2016) <doi:10.1007/s10654-016-0137-7>.
Depends: stats, base, graphics, survival, drgee, stdReg, R (>= 3.3.1),

event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis.
An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.
Depends: stats, base, graphics, survival, drgee, stdReg, R (>= 3.5.0),

Title: Hybrid simulation and genome scan for dominant markers
Authors@R: c(person("Francisco", "Balao", role = c("aut", "cre"),email = "fbalao@us.es"),person("Juan Luis", "Garc�a-Casta�o", role = "aut",email = "jlgc@us.es"))
Author: Francisco  Balao and Juan Luis Garc�a-Casta�o
SystemRequirements: Bayescan 2.1. See README.
Description: This package is developed in the Plant Reproductive Biology Lab (RNM-214) - University of Seville. It contains hybrid simulation functions for dominant genetic data. It also provides several genome scan methods.