Copyright: Copyright (C) 2014-2020 Google, Inc.

Copyright: Copyright (C) 2014-2021 Google, Inc.

Copyright: Copyright (C) 2014-2022 Google, Inc.
Imports: assertthat (>= 0.2.0), Boom, ggplot2, zoo

Title: Learning Stable Structures in Kinetic Systems
Description: Implementation of 'CausalKinetiX', a framework for learning stable structures in kinetic systems. Apart from the main functions CausalKinetiX() and CausalKinetiX.modelranking() it includes functions to generate data from three simulations models, which can be used to benchmark structure learning methods for linear ordinary differential equation models. A detailed description of the underlying methods as well as details on the examples are given in Pfister, Bauer and Peters (2018) <arXiv:1810.11776>.
Imports: fda, cvTools, quadprog, randomForest, deSolve, stats,
graphics, pspline, utils, glmnet, nnlasso, sundialr

graphics, pspline, utils, glmnet, sundialr (>= 0.1.3)

graphics, pspline, utils, glmnet, nnlasso, sundialr (>= 0.1.3)

Title: MBSTS Models for Causal Inference and Forecasting
person("Iavor", "Bojinov", email = "ibojinov@hbs.edu", role = c("aut")),
person("Fiammetta", "Menchetti", email = "fiammetta.menchetti@gmail.com", role = c("aut", "cre")),
person("Victoria L.", "Prince", email = "vliublinska@hbs.edu", role = "ctb"),
person("Ista", "Zahn", email = "izahn@hbs.edu", role = "ctb"))
Description: Infers the causal effect of an intervention on a multivariate response through the use of Multivariate
Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>.
The package also includes functions for model building and forecasting.
Imports: CholWishart, forecast, MASS, Matrix, MixMatrix

Title: Causal Inference Modeling for Estimation of Causal Effects
Provides an array of statistical models common in causal inference such as
standardization, IP weighting, propensity matching, outcome regression, and doubly-robust
estimators. Estimates of the average treatment effects from each model are given with the
standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).
Imports: stats, causaldata, boot, multcomp
Author: Joshua Anderson [aut, cre, cph],