Learn optimal policies via doubly robust empirical
welfare maximization over trees. This package implements the multi-action doubly
robust approach of Zhou, Athey and Wager (2018) <arXiv:1810.04778> in the case where
we want to learn policies that belong to the class of depth k decision trees.

c(
person("Erik", "Sverdrup", role = c("aut", "cre"), email = "erikcs@stanford.edu"),
person("Ayush", "Kanodia", role = "aut"),
person("Zhengyuan", "Zhou", role = "aut"),
person("Susan", "Athey", role = "aut"),
person("Stefan", "Wager", role = "aut")
)
Erik Sverdrup [aut, cre],
Ayush Kanodia [aut],
Zhengyuan Zhou [aut],
Susan Athey [aut],
Stefan Wager [aut]

Learn optimal policies via doubly robust empirical
welfare maximization over trees. Given doubly robust reward estimates, this package
finds a rule-based treatment prescription policy, where the policy takes the form of
a shallow decision tree that is globally (or close to) optimal.

This package provides easy access to various political data APIs
directly from R. For example, you can access Google Civic Information API
<https://developers.google.com/civic-information/> or Sunlight Congress API
<https://sunlightlabs.github.io/congress/> for US Congress data, and POPONG
API <http://data.popong.com/> for South Korea National Assembly data.
c(person("Eunjeong", "Park", role = c("aut", "cre"),
email = "ejpark04@snu.ac.kr"),
person("Jong Hee", "Park", role = "aut",
email = "jongheepark@snu.ac.kr"))
Eunjeong Park [aut, cre],
Jong Hee Park [aut]

This package provides easy access to various political data APIs
directly from R. For example, you can access Google Civic Information API
(https://developers.google.com/civic-information/) or Sunlight Congress API
(https://sunlightlabs.github.io/congress/) for US Congress data, and POPONG
API (http://data.popong.com/) for South Korea National Assembly data.

This is a collection of data and functions for common metrics in political science research. Data measuring ideology, and functions calculating geographical diffusion and ideological diffusion - geog.diffuse() and ideo.dist(), respectively. Functions derived from methods developed in: Soule and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>, Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback et al. (2004) <doi:10.1177/1532673X04263801>.
MASS, dplyr, ggplot2, rlang, tidyverse, car, purrr, stats,
graphics, formula.tools, gplots, rstatix, stringr
person("Vann Jr", "Burrel", , "bvannjr@sdsu.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-3066-5815"))
Vann Jr Burrel [aut, cre] (<https://orcid.org/0000-0003-3066-5815>)