Package: CRTConjoint
Title: Conditional Randomization Testing (CRT) Approach for Conjoint
        Analysis
Version: 0.1.0
Authors@R: c(
	person("Dae Woong", "Ham", email = "daewoongham@g.harvard.edu", role = c("aut", "cre")),
	person("Kosuke", "Imai", email = "imai@harvard.edu", role = "aut"),
	person("Lucas", "Janson", email = "ljanson@fas.harvard.edu",role = "aut"),
	person("Jacob", "Bien", email = "jbien@usc.edu",role = c("ctb", "cph")))
Maintainer: Dae Woong Ham <daewoongham@g.harvard.edu>
Description: Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" <arXiv:2201.08343>.
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.2.0
Imports: utils, methods, doSNOW, foreach, Rcpp, snow
Depends: R (>= 2.10)
LazyData: true
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/daewoongham97/CRTConjoint
BugReports: https://github.com/daewoongham97/CRTConjoint/issues
Copyright: (c) 2022 Dae Woong Ham. Code in helper_hierNet.R, hierNet.c,
        and hierNet_init.c are taken (with explicit permission) from
        (c) 2020 Jacob Bien.
NeedsCompilation: yes
Packaged: 2022-06-07 16:16:29 UTC; Ham
Author: Dae Woong Ham [aut, cre],
  Kosuke Imai [aut],
  Lucas Janson [aut],
  Jacob Bien [ctb, cph]
Repository: CRAN
Date/Publication: 2022-06-09 08:00:05 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 14:46:09 UTC; windows
Archs: i386, x64
