Package: fastpolicytree
Type: Package
Title: Constructs Policy Trees from Covariate and Reward Data
Version: 1.0
Authors@R: c(
    person("James", "Cussens", role = c("aut", "cre"),
    email = "james.cussens@bristol.ac.uk",
    comment = c(ORCID = "0000-0002-1363-2336")),
    person("Julia", "Hatamyar", role = "ctb", email = "julia.hatamyar@york.ac.uk"),
    person("Vishalie", "Shah", role = "ctb", email = "vishalieshah@gmail.com"),		
    person("University of Bristol", role = "cph"),
    person("MRC", role = "fnd")
    )
Description: Constructs optimal policy trees which provide a rule-based treatment prescription policy.
 Input is covariate and reward data, where, typically, the rewards will be doubly robust reward estimates.
 This package aims to construct optimal policy trees more quickly than the existing 'policytree' package and
 is intended to be used alongside that package.
 For more details see Cussens, Hatamyar, Shah and Kreif (2025) <doi:10.48550/arXiv.2506.15435>.
URL: https://github.com/jcussens/tailoring
Suggests: policytree
Imports: Rcpp (>= 1.0.7)
LinkingTo: Rcpp
RoxygenNote: 7.3.2
Encoding: UTF-8
License: GPL (>= 3)
NeedsCompilation: yes
Packaged: 2025-06-20 11:36:28 UTC; uw20605
Author: James Cussens [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-1363-2336>),
  Julia Hatamyar [ctb],
  Vishalie Shah [ctb],
  University of Bristol [cph],
  MRC [fnd]
Maintainer: James Cussens <james.cussens@bristol.ac.uk>
Repository: CRAN
Date/Publication: 2025-06-24 08:50:06 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-26 17:54:05 UTC; unix
Archs: fastpolicytree.so.dSYM
