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.

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.

Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into
		shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative
		approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be
		challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component
		scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between
		corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which
		provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations.
		Please cite the following article when utilizing this package: 
		Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) <doi:10.3389/fnins.2022.969510>.