knitr, covr, testthat (>= 3.0.0), rmarkdown

c(person("Suyeon", "Kang", role = c("aut", "cre"),
email = "skang062@ucr.edu"),
person("Soojin", "Park", role = "aut",
email = "soojinp@ucr.edu"))
stats, parallel, MASS, nnet, SuppDists, CBPS, PSweight,
spelling, utils
We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) <arXiv:2008.12812> and Park, Kang, and Lee (2021+). This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates.
Suyeon Kang [aut, cre],
Soojin Park [aut]

Nonparametric and Cox-Based Estimation of ATE in Competing Risks
Bella Vakulenko-Lagun, Colin Magdamo, Marie-Laure Charpignon, Bang Zheng, Mark W Albers, Sudeshna Das
Estimation of average treatment effects (ATE) of two static treatment regimes on time-to-event outcomes with K competing events (K can be 1). The method uses propensity scores weighting for emulation of baseline randomization.
survival, inline, doParallel, parallel, utils, foreach,
data.table, purrr
knitr, rmarkdown, bookdown, tidyverse, ggalt, cobalt, ggsci,
modEvA, naniar, DT, Hmisc, hrbrthemes, summarytools, kableExtra

Bella Vakulenko-Lagun, Colin Magdamo, Marie-Laure Charpignon, Bang Zheng, Mark Albers, Sudeshna Das

Tools for Causal Discovery on Observational Data
person(given = "Anne Helby",
family = "Petersen",
role = c("aut", "cre"),
email = "ahpe@sund.ku.dk")
Various tools for inferring causal models from observational data. The package
includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler
and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools
for evaluating differences in adjacency matrices, which can be used for evaluating
performance of causal discovery procedures.
pcalg, igraph, RColorBrewer, gtools, clipr, methods, scales