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>.

An R implementation of the Average Marginal Component-specific
    Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
    T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
    Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.

Client for 'CKAN' API (<https://ckan.org/>). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' API.

Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials.

Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).)