CRTConjoint: Conditional Randomization Testing (CRT) Approach for Conjoint Analysis

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" <doi:10.48550/arXiv.2201.08343>.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: utils, methods, doSNOW, foreach, Rcpp, snow
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
Published: 2022-06-09
Author: Dae Woong Ham [aut, cre], Kosuke Imai [aut], Lucas Janson [aut], Jacob Bien [ctb, cph]
Maintainer: Dae Woong Ham <daewoongham at g.harvard.edu>
BugReports: https://github.com/daewoongham97/CRTConjoint/issues
License: GPL (≥ 3)
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.
URL: https://github.com/daewoongham97/CRTConjoint
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: CRTConjoint results

Documentation:

Reference manual: CRTConjoint.pdf
Vignettes: Using CRTConjoint

Downloads:

Package source: CRTConjoint_0.1.0.tar.gz
Windows binaries: r-devel: CRTConjoint_0.1.0.zip, r-release: CRTConjoint_0.1.0.zip, r-oldrel: CRTConjoint_0.1.0.zip
macOS binaries: r-release (arm64): CRTConjoint_0.1.0.tgz, r-oldrel (arm64): CRTConjoint_0.1.0.tgz, r-release (x86_64): CRTConjoint_0.1.0.tgz

Linking:

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