'R' implementation of the regression-based causal mediation analysis with a treatment-mediator interaction term, as originally implemented in the 'SAS' macro by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.
boot, furrr, future, geepack, knitr, mice, mitools, modelr,
purrr, rlang, rmarkdown, stringr, testthat, tidyverse

Regression-Based Causal Mediation Analysis with Interaction and
Effect Modification Terms
This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models). It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.
boot, furrr, future, geepack, knitr, mice, mitools, modelr,
purrr, rlang, rmarkdown, stringr, testthat, tidyverse, magic,
formattable, kableExtra

Network-Based Regularization for Generalized Linear Models
c(
person("Jie", "Ren", email = "jieren@ksu.edu", role = c("aut", "cre")),
person("Luann C.", "Jung", email = "luannjung01@gmail.com", role = c("aut")),
person("Cen", "Wu", email = "wucen@ksu.edu", role = "aut"),
person("Yinhao", "Du", role = c("ctb")),
person("Jiang", "Yu", role = c("ctb")),
person("Junhao", "Liu", role = c("ctb")))
Network-based regularization has achieved success in variable selections for
high-dimensional biological data, due to its ability to incorporate the correlations
among genomic features.This package provides procedures for fitting network-based regularization,
minimax concave penalty (MCP) and lasso penalty for generalized linear models. This first
version, regent0.1.0, focuses on binary outcomes. Functions for continuous, survival outcomes
and other regularization methods will be included in the forthcoming upgraded version.
Jie Ren [aut, cre],
Luann C. Jung [aut],
Cen Wu [aut],
Yinhao Du [ctb],
Jiang Yu [ctb],
Junhao Liu [ctb]