R Client for 'Customer Journey Analytics' ('CJA') API
c(
person(given = "Ben", family = "Woodard", role = c("aut", "cre"), email = "ben.woodard@searchdiscovery.com"),
person(given = "Charles", family = "Gallagher", role = c("ctb"), email = "charlesjgallagher15@gmail.com"),
person(given = "Search Discovery, LLC", role = c("cph"))
)
Connect to the 'CJA' API, which powers 'CJA Workspace' <https://github.com/AdobeDocs/cja-apis>. The package
was developed with the analyst in mind and will continue to be
developed with the guiding principles of iterative, repeatable,
timely analysis. New features are actively being developed and we
value your feedback and contribution to the process.
assertthat, httr, magrittr, purrr, R6, dplyr, jsonlite, glue,
jose, tibble, lubridate, progress, vctrs, stringr, rlang,
memoise, openssl
Ben Woodard [aut, cre],
Charles Gallagher [ctb],
Search Discovery, LLC [cph]

assertthat, httr, magrittr, purrr, R6, dplyr, tidyr, jsonlite,
glue, jose, tibble, lubridate, progress, vctrs, stringr, rlang,
memoise, openssl

Heterogeneous Effects Analysis of Conjoint Experiments
c(
person(given = "Thomas",
family = "Robinson",
role = c("aut", "cre", "cph"),
email = "ts.robinson1994@gmail.com",
comment = c(ORCID = "0000-0001-7097-1599")),
person(given = "Raymond",
family = "Duch",
role = c("aut","cph"),
email = "raymond.duch@nuffield.ox.ac.uk",
comment = c(ORCID = "0000-0002-1166-7674"))
)
A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level.
Thomas Robinson [aut, cre, cph]
(<https://orcid.org/0000-0001-7097-1599>),
Raymond Duch [aut, cph] (<https://orcid.org/0000-0002-1166-7674>)

A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.
stats, rlang, tidyr, ggplot2, Rdpack, randomForestSRC