c(
person("Jason", "Sinnwell", email = "sinnwell.jason@mayo.edu", comment=c(ORCID="0000-0003-1964-5522"), role = c("aut","cre"),),
person("Daniel", "Schaid", email = "schaid.daniel@mayo.edu", comment=c(ORCID="0000-0003-1457-6433"), role = c("aut"),))
Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).
R (>= 3.6.0), methods, graphics, glasso, igraph

Regression-Based Causal Mediation Analysis with an Interaction
Term
c(
# http://r-pkgs.had.co.nz/description.html#author
person('Kazuki', 'Yoshida',
email = 'kazukiyoshida@mail.harvard.edu',
role = c('cre','aut'),
comment = c(ORCID = '0000-0002-2030-3549')),
#
person('Maya', 'Mathur',
role = c('ctb'),
comment = c(ORCID = '0000-0001-6698-2607'))
)
'R' re-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.
Deriv, MASS, Matrix, assertthat, sandwich, survival
boot, furrr, future, geepack, knitr, mice, mitools, modelr,
purrr, rlang, stringr, testthat, tidyverse

c(
# http://r-pkgs.had.co.nz/description.html#author
person('Kazuki', 'Yoshida',
email = 'kazukiyoshida@mail.harvard.edu',
role = c('cre','aut'),
comment = c(ORCID = '0000-0002-2030-3549')),
person('Yi', 'Li',
role = c('ctb','aut'),
comment = c(ORCID = '0000-0002-9359-210X')),
person('Maya', 'Mathur',
role = c('ctb'),
comment = c(ORCID = '0000-0001-6698-2607'))
)