Title: Nonparametric and Cox-Based Estimation of ATE in Competing Risks
Author: Bella Vakulenko-Lagun, Colin Magdamo, Marie-Laure Charpignon, Bang Zheng, Mark W Albers, Sudeshna Das
Description: 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.
Imports: survival, inline, doParallel, parallel, utils, foreach,
Suggests: knitr, rmarkdown, bookdown, tidyverse, ggalt, cobalt, ggsci,
modEvA, naniar, DT, Hmisc, hrbrthemes, summarytools, kableExtra

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

Title: Tools for Causal Discovery on Observational Data
Description: 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.
Imports: pcalg, igraph, RColorBrewer, gtools, clipr, methods, scales
Maintainer: Anne Helby Petersen <ahpe@sund.ku.dk>

Title: Causal Learning of Mixed Graphical Models
Author: Andrew J Sedgewick, Neha Abraham <neha.abraham@pitt.edu>, Vineet Raghu <vineetraghu@gmail.com>, Panagiotis Benos <benos@pitt.edu>
Description: Allows users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables). To learn a directed graph over mixed data, it first calculates the undirected graph (Sedgewick et al, 2016) and then it uses local search strategies to prune-and-orient this graph (Sedgewick et al, 2017). AJ Sedgewick, I Shi, RM Donovan, PV Benos (2016) <doi:10.1186/s12859-016-1039-0>. AJ Sedgewick, JD Ramsey, P Spirtes, C Glymour, PV Benos (2017) <arXiv:1704.02621>.

Title: Optimal Transport Weights for Causal Inference
Description: Uses optimal transport distances to find probabilistic
matching estimators for causal inference.
These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>.
The package will build the weights, estimate treatment effects, and
calculate confidence intervals via the methods described in the paper.
The package also supports several other methods as described in the help
Imports: approxOT, Matrix, matrixStats, methods, lbfgsb3c, loo, osqp,
pbapply, reticulate, R6 (>= 2.4.1), Rcpp (>= 1.0.3), RSpectra,
Suggests: CBPS, data.table (>= 1.12.8), rstan (>= 2.19.3), Rmosek,