Packaged: 2016-08-04 04:01:40 UTC; junghikim

Title: Mediation Analysis for Generalized Linear Models Using the
Authors@R: person("Daniel", "Nevo", email = "danielnevo@gmail.com", role = c("aut", "cre"))
Description: Causal mediation analysis for a single exposure/treatment and a single mediator,
both allowed to be either continuous or binary. The package implements the difference method
and provide point and interval estimates as well as testing for the natural direct and
indirect effects and the mediation proportion.

Description: Causal mediation analysis for a single exposure/treatment and a
single mediator, both allowed to be either continuous or binary. The package
implements the difference method and provide point and interval estimates as
well as testing for the natural direct and indirect effects and the mediation

proportion. Nevo, Xiao and Spiegelman (2019) <doi:10.1515/ijb-2017-0006>.
Packaged: 2019-07-16 11:30:50 UTC; danielnevo

Title: Gene-Environment Interaction Analysis Incorporating Prior
Author: Xiaoyan Wang, Hongduo Liu, and Shuangge Ma
Description: Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions.
Imports: MASS, glmnet, stats, rvest, dplyr, pheatmap, grDevices,
Packaged: 2022-01-08 04:17:01 UTC; xiaoyanwang

Title: Robust Gene-Environment Interaction Analysis
Authors@R: c(person("Mengyun", "Wu", role=c("aut"), email = "wu.mengyun@mail.shufe.edu.cn"),
person("Xing", "Qin", role=c("aut","cre"), email = "qin.xing@163.sufe.edu.cn"),
person("Shuangge", "Ma", role=c("aut"), email = "shuangge.ma@yale.edu"))
Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of four robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>).
Imports: survAUC, MASS, splines, pcaPP, Hmisc, survival, quantreg,